Python is a widely-used programming language essential for data science, AI, and data engineering, serving as a bridge between data analytics and advanced technical fields. Success in learning Python requires treating it as a marathon rather than a sprint, emphasizing consistency over intensity through daily practice of 1-3 hours, building strong foundational concepts, and leveraging community support. The 8-week Python track covers programming basics, functions, control flow, data structures, and exploratory data analysis, with assessments including knowledge questions and code challenges. Learners should find ways to love Python despite frustrations, as the language is learnable but requires dedication and patience to master.
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ALX DP_C11 Python Track Orientation SessionAdded:
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How are we all doing?
Um hope you're doing great today.
Welcome to Pythonation for C1.
Uh [clears throat] just last week I mean just last week Friday we had a Tambali and today we're on boarding you for Python track. I hope you're feeling excited. I hope you're feeling great.
I hope you're feeling very motivated for this next journey or next phase of your learning journey. Um, how are we all feeling? Um, let's put it in the chat.
How are we feeling this morning? It's a Monday morning. It's a beautiful Monday morning. How are we feeling? Hello. Just say hello and let's let us know where you're joining from. Um, I'll go first.
Um, [clears throat] Alma, I'm I'm joining from I'm joining from Lagos, Nigeria.
Andrew, well, this is a break week.
Actually, this orientation week is also your break week. Nobody's going to ask you for any um assessments this week. So yeah, this is your break week. And you also had break week last week for those people who didn't need a consolidation week. You couldn't have an assessment service for consolidation week. You had two weeks, right? Or you have two weeks actually. Oh, okay. Someone said it's 1 p.m. in Kenya. Okay. It's actually 11:05 in Nigeria. Lagos, Nigeria. Okay. Hello from Nairobi, Kenya. Hello. Hello Dean.
Hello Charles. Hello Link. Hello blessing.
I didn't want the break rather. Okay.
Okay. Allah from Egypt. Hello Johnny from Nigeria. Hello Babai. Okay.
Muhammad from Morop. Um Juliet from Abuja Nigeria. Welcome everyone. I can see hands already up. You guys are very very very very eager to have this Python track. You're eager to dive into the learning of Python. [laughter] Grace from Nairobi. Hello. Hello. Prince from Ghana. Okay. Hello everyone. Okay.
Two fellows from Ghana as well. We have a lot of people from Ghana and Nairobi here. Okay. I can see someone from Benin or Os from Bin Nigeria. Okay. Helloas.
Ah. Okay. Welcome everyone. Welcome.
Okay. Andrew from Zimba. From Zambia.
Amed from Sudan. Welcome everyone.
Welcome guys. It's so so really nice seeing you all on this call. um from the data analytics track and now in Python track. What a journey.
What a transition. Okay. Sh says from West Africa.
Ar from Nigeria. Peter from Nigeria.
Mike Noli says from the moon.
Okay. Um Zula from South Africa. Yeah.
Yes. Frank actually so excited to be part of the Python truck. I'm excited for you guys actually. Really excited for you all. Okay. Fatima from Nigeria.
All right, welcome everybody. Welcome all. Um, welcome once again to your Python orientation track for your Python track orientation. Um, my name is Lima and I am your host for today and um let's dive right in.
Okay, so let's go through the agenda for today. The first agenda, first thing on agenda is going to be an icebreaker where we get to know each other and our expectations in this Python track and um the next is going to be Python track deep dive understanding what Python um has to offer. What exactly is Python?
Why are you even learning Python in the first place and how does it connect to data analytics that you have learned before and how it is going to be a bridge for what you're going to learn in the subsequent track that's either data science data engineering so we're going to have a deep deep dive of Python Python track and then next we're going to have the jump start kit the resources to help you begin your Python learning journey and then also we're going to look at succeeding in this track what does success look success success look like in this track the key strategies the things you should do and um you know the things you should avoid and you know how how you should remain consistent basically and then also we're going to have the data program community you can leverage the community during your learning journey this eight weeks of learning journey that you're about to have uh the Q&A and announcements basically so yeah then we're going to have closing remarks afterwards so guys are excited know what is um coming.
Okay, let's start with the icebreaker.
All right, so let's get to know each other. Let's start with this. So complete this sentence in the chat. So now I'm sure you guys must have done like maybe you must have googled what Python is. I'm sure that you have even started to have even research what Python is about. I mean you're about to start learning Python. So we're learning about Python. So you would you'd be curious like okay what exactly does Python mean? And so with the amazing knowledge you have of Python now or with the knowledge you have of Python now because I'm sure some of you are even gurus already in Python but with the knowledge you have of Python when you hear Python when you think of Python when you use Python what do you think of what's that one word that comes to your mind when you think of Python or when you hear Python how do you feel about learning Python so you can put it in the chat let's hear it okay I can see responses someone say snake say snake uh uh Emanuel says gigantic niche says a language. Okay. Um Masia says phone. Okay. And says a programming language. J says programming language. Okay. Okay. AR says programming. James says AI. Okay. Bab says code. Caleb says programming. Okay.
Um said I hear it's simple compared to others. Okay. Okay. That's really interesting. Um, [clears throat] Nigelia [laughter] P said actually I mean that's the symbol of Python, right? I mean Python is actually the name of a snake. First thing to come to your mind when you Python Python programming language.
Okay. Um, okay. Jethro says back end. Philip says coding. As says cracking codes. Okay.
John says poison.
I'm not sure. Is Python poisonous? I think Python is just um I think it's non venomous. Rather, I think that's what they call it. Non venomous actually. Is it venomous? I think he only just um strangles. I think I think is is it constricting or was that what they use?
I don't think that's venom. I'm not sure. I don't know. I don't know about snakes, but now they say coding.
Oh, someone says my voice is echoing.
Okay. Um, let me try to get to that. Just give me a moment.
>> I think your voice is okay.
>> Okay.
>> All right. Um, I don't know. Um, Philips, you said my voice is okay.
Maybe you should try to change your location. I think it's probably from your end.
>> Yeah. Nigel, I'm not sure. I think I'm not sure. Python is generally I don't I'm not sure. Let me see what I do.
Please guys confirmed. Don't say that Alma said Python is nonvenenomous and then when you say python you don't run.
When you see any snake run like that python they really love miniming them from snakes. Right right there to be people who say that when you see some snakes some snakes I think ball python ball python is I think I can speak about ball python a little bit. I say ball python is not very venomous.
It's not venomous. So like so when you when you see it just don't kill them, don't harm them. They are very harmless and stuff. They just call up when they see danger, when they sense danger. But who has time to be checking if it's a ball python when you see a snake? When you see a snake, the first instincts is to run away. Just run in the opposite direction. Nobody has time to be checking what kind of snake it is.
Okay. I mean, we're not here to talk about snakes. We're here to talk about Python programming language. So yeah. Um [clears throat] someone says a widely used programming language in data science automation and web development. Ah a well detailed explanation um view of Python.
I don't know. I don't know. We should ask why they actually named Python the programming language after a snake. I have no idea. Don't know. I don't know why that is. So all right guys um okay um we should drop the chat we should drop the um we'll upload the um we'll put the link to the YouTube um recording after this session we put it on the e um [clears throat] on the program after afterwards or better still we can put it in a chat maybe the record the live the live um recording can put it in the chat so that you can live stream afterwards so you can watch after the session So both the Zoom recording and the YouTube recording will be available. You're welcome.
Okay. Diana says, "I love Python programming language." Okay. Okay. You love Python language. That's great.
That's great. That's great. That's the energy that we need. That's the energy.
That's the spirit that we need. That's the vibe.
It's running on my mind already. Okay.
Okay. Okay. Love the tweet. Love the tweet. Okay. Philip says, "My voice is okay now." Thanks, Philip. Thanks. All right, guys. Um let's get let's go to the next segment now. Um we're going to be having the Python track deep dive and that is going to be taken by no other person than our own very technical mentor um Chev. I'm sure you guys already know she by now I'm sure you must have heard from she maybe from the data analytics track. So she is not exactly a new person to you guys.
But if you don't know she before now, Shun is our technical mentor, our amiable technical mentor and is going to do justice to this particular session, this particular segment of this session. Uh please join me welcoming as he takes this session this segment rather.
Welcome. You guys are not giving him is it your emojis are not like that's your emojis. You guys are not are you not excited to hear from? Yeah. Yeah.
Thanks. All right. Sh please over to you.
>> All right. Thank you. Um Haley, do you mind I share my screen?
>> All right. Thank you. Um just give me a moment to share my screen.
Okay. Oh, good.
Um one moment. Let me get the Q& A.
Okay, good. Um before I start, I have already start seeing question um with regards to I think Thomas is asking question in the chat. Please, how can we register for Python on ALX? I'm done with data analytics. Yeah, a lot of learners are having the same challenge and our technical team is currently working to resolve that. So, we trust that it will be resolved this week. you have nothing to worry about. Um we'll get it done this week so that you'll be able to those of you who initially register for data analytics will be able to have the opportunity to register for or to enroll for either data science or um data engineering. Okay, I see a question by anonymous. Are we going to use DA program hub for Python announcement or we'll be shifted to another one? Um I think yeah you'll be create you a new hub will be created a new what do you guys call it forgotten the name a new space yeah a new space will be created on e-hub and secondly it says when will python be available in the e-hub it is still marked as not available and we have not received our da certificate don't worry you receive your da certificate and um the python track will be made available for those who initially apply for data analytics Okay. All right. Good. So if you have any question regards to I cannot see Python, I cannot see data science is still not available. We are aware of that. Okay.
Um somebody say my voice is echoing. Can anybody confirm if my voice is echoing?
>> No, you're good. You're good. Shun. I think it's on their side. They need to probably leave and come back. So um Lucius uh if my voice is echoing please kindly log out and come in. All right good.
Okay good. So before we start um I just want to ask is anybody on this call scared of Python?
Be honest if is anybody on this call scared of Python?
Honest. Okay.
Okay.
Okay. Okay. No, a bit. Not at all. Okay.
All right. So, we have a mixed multitude.
Some people are scared of Python. Some people are not scared of Python. I want to believe that those that are most likely not scared of Python and those who have interacted with the programming language before [snorts] and probably those who are and I'm not apparent to Python the snake. I'm to Python the programming language. Okay? and those who are maybe scared are encountering Python for the first time. Right? So, let me start like this.
How many of you um how would I put it now?
Um okay, fine. So, I want to find something that is hard that you are forced to love. Okay, let me use Okay, first of all, I'll start by saying Python is not easy. I'm not going to sugar coat anything. All right? It's not easy, but it's very learnable. All right? So, how many of you have been in love?
You are in love with this very frustrating partner, but you can't help but keep loving the person.
Or let's put it like this. You have a sibling that is very frustrating, very hard to work with, but you just have to love that sibling because you are supposed to love that sibling. or you have a parent that is very frustrating but you have to find a way to love that person. How many of you can relate with what I'm saying? Anybody in the chat?
Okay, you can relate. Okay, let me bring it a little bit closer. How many of you are supporting a very frustrating football club and you tell yourself, "I would no longer support this club." But the following week and they have a match game, you are you are there to watch.
You just can't help it. Like maybe you say, "You know what? I'm not going to watch again." And even if you're not watching, you are you are you you are taking up life scores to see what's happening.
So you know that this club is frustrating you, but you still find a way to love them. Okay, good. So I've given all these scenarios. You didn't just walk away from your lover. You didn't walk away from your family member. You didn't walk away from that your friend or your parents or the football club. You just stuck there right now. That's how you have to love Python to be able to succeed in the program. It will come as difficult, misunderstanding, frustrating, like it's taken from you and not giving you anything. But you just have to find a way to love Python. The only way or one of the ways you can easily learn Python is to love the language. Right? So let me take us back to school. How many of us remember that subjects in university or secondary school or higher institutions subject that you really hated the lecturers or the teachers you find it difficult to pass and subject that you really love the teachers or the lecturers you found it easy to pass. Now one of the reason why most people hate mathematics is that mathematics teachers is like there's something wrong with them. They are very frustrating the way they behave. You You get the story, right? Good. So, what I want you to do today is find a way to love Python.
Okay? So, each time I'm taking anything that you guys to Python, I just have I have what I try to tell my learners to do, right? So, I'm going to type something in the chat and I want you all to type P2. All right? I want you to say to yourself, I love Python. Type it in the chat. I know it's going to be very hard to say it.
It's funny, but every day you want to come in to learn Python. Just tell yourself, I love Python. I'm gonna learn Python. I'm going to understand Python.
No matter how frustrating it is, just tell yourself, I love Python. It's going to be very difficult. You're going to keep you're going to be in a relationship with Python for the next 8 weeks or so. The only way you can understand it is find a way to love it.
The more you love Python, the easier it is to learn. So, when you get frustrated, remember these examples I gave. your lover, your partner, your relative, your parents, um the football club you love, as frustrating as they can be, you still find a way to come back to them. That's how I want you to treat Python. Okay. All right. Let's dive into it. Before I continue, let me see a question from Shelia. Wait, I thought we are progressing to Python automatically after the DA is marked complete. Are we supposed to register again? So, Shelia, is it so I can I can't pronounce the name an okay. Yes.
So and there are learners who applied for data science there learners who applied for data engineering there learners who applied for data analytics.
If you applied from the beginning for data science or data engineering you don't have to register you automatically moved to Python. But for those who applied for data analytics only maybe they got into the course and they got interested in what we are doing and the data career and they want to now move to data science or data engineering. They are the ones that want to register. I hope I answered your question well.
Good. All right.
So, can I dive deep take a deep dive into Python? Good. I see that we're writing a lot of Python. That's nice.
So, we started where's my pointer. Good.
We started from data analytics. That's four weeks.
Um 14 learning weeks then we are now here. After we finish our eight weeks of Python, some of us will be moving to machine learning while some of us will be moving to data engineering. Right?
Good. So this is our learning journey for the data. So this track like this is the data science track and this track is the data engineering track that's from Python to data engineering. Okay good. So the Python track what's it all about?
Why why are we learning Python? Are we just learning Python for the sake of learning Python? So in the Python track we have the core skills for your Python journey. So what the Python track will give you or the Python program. You learn programming from ground up. So this programming is tailored for beginner and is tailored to carry you from a beginner intermediate to advanced level. Okay. So it's 18 weeks but it's a lot that sparked into it. So we're trying to teach Python or this track will be teaching Python from ground up.
Right? Then we teach how to apply Python to automate task and analyze data. So the main aim we are teaching Python is not to learn Python for the sake of Python. Python is a very robust programming language that can be used for many things. Examples is web development like the back end where we have the Django and then it's used for gaming automation. But what I want to use Python for is Python for data analysis and data science and data engineering. So you'll be able to use Python for to automate task and analyze data. then you'll be able to build strong coding foundations for advanced topics. So one thing I want you to understand is there is no important aspect or less important aspect in the Python track. In fact from the very moment you start learning everything is important especially the foundational part right if you don't grasp the foundation part when you move to machine learning or data engineering you're going to struggle. So a lot is lying on learning Python as you make your advancement in data science or data engineering. So please take your time.
This next 8 weeks are very crucial. If you played during the data analytics time fine you can't afford to play right? So you have to give in more time okay to data science to Python. So it bridges between data analysis, machine learning and data engineering. So, Python is the bridge between data analytics, machine learning and data engineering. Remember we have done um data analytics and in data analytics we did um preparing data with Google sheets, quering data with um SQL, visualizing data with PowerBI. All those things you did with in data analytics, you can do all of them with Python and more. That's why Python is the bridge between data analytics and data engineering and machine learning. Okay, good.
So why it matters? Why why does it matter? Python is the most used language in data science and AI. So when you have to do anything with regards to data, AI, modeling, all those things that other languages can do it, right? But Python is leading the race in that regard. So Python is the core skill for machine learning and data engineering currently.
So it's the um one of the most important programming language for it. Okay. So is the most popular programming language.
Let me use that word. So it is widely used in real world data job. So every data job requires you to have the knowledge of Python. Okay. Every data related job because of how popular it is and how robust it is. Good. Yeah. Then [snorts] it expands your problem solving capability beyond data analysis tools.
Okay. So in all I want you to remember as you are doing this whole program the one important skills we want to give you is how to solve problems. Everything we are learning are the tools we are giving to you to solve problems. But the core skills that we are passing out in this data program is your ability to solve problems. If you take your mind back to um the first course where we talk about problem solving and different style to solve problems, right? This is why we want to use Python and Python is a good skill. Um it's a good tool for solving problems. Okay, good.
Um so the tools so the tools we'll be covering includes Jupyter notebook. You must have interacted with Jupit Jupyter notebook in my um doing my SQL, right?
Then we have Python libraries. You will talk more about them when you get into the course next week. We have Google Collab. So if you don't want to write code locally on Jupyter notebook, you can decide to write it on Google Collab.
Then we have um GitHub. We have G for version control. And we have um Microsoft Visual Studio Code. Some of us prefer to we may not be so com we don't we may not want to use Jupyter Notebook.
We want to open our Jupyter notebook in VS Code. So these are the basic tools we'll be using in the Python track. So what to expect? So every week we contain a mixture of knowledge pieces that is after every section there's this knowledge check we have. Then we have exercises at the end of a a module you have exercises. Then we have what knowledge questions. So there will be one graded assessment per week at least one either in the form of a code challenge or a multiplechoice question. So every week starting from next week you will have a code challenge or an assessment to submit. So it's either a an MCQ or a code challenge.
Right? So it is important to note that we cannot realistically cover everything there is to learn in Python in 8 weeks.
So it is not possible to cover every aspect of Python. What we are giving to you is the foundation and the basics and the most important part from which you can build up from. Okay. So you can learn more outside of what is covered in this course. But this course have everything you need to move to to learn Python enough to use for analysis to learn machine learning or to learn data engineering. Right? But if you want to explore more other um concepts in Python or you want to understand deeper concept much better feel free to explore the Python documentation right don't worry as we go through the course next week you will come across it okay good so there are three types of assessment and or activities right we have the knowledge question so the know question is a multiple choice question okay um it's it's not graded all right it doesn't add to your grade but it help you to revise to see if the concept you have learned either in the video in the notebook or in the slides whether you understand those concepts you just want to test your ability understanding um Diana you say I am too fast can anybody confirm if I'm too fast I'm not teaching Python I'm just talking about Python anybody in the chat Diana says I'm too fast Lima am I too fast.
>> No, you're not. Yeah, I think you're good.
>> Um, Diana, I am I'm surprised you say I'm too fast. So, should I slow down?
Okay, I I'll try to to match the breaks, right? Maybe I'm too excited about Python. That's why.
Okay, I'll continue. [laughter] No, no, no, no. Joseph, don't say Diana is too slow. I I'll try to adjust. All right, cool. So we have multiple choice question.
Yeah, this session iframe this session is being recorded, right? So you have the MCQs, multiple choice question. You should be conversant with MCQ, right?
That's basically what you did the whole of data analytics. So it's a summative assessment. Um they tag MCQ their tests, they're not timed, right? You just write the test but they're not timed. So you will see the release date, the due date, whenever you attempt the test and your scores. Right? Remember for each MCQ you have um one attempt to score above 50%, if you score above 50% on your first attempt, you have passed and you are done. If you score less than 50%, you have three supplementary attempts to pass. And remember supplementary attempts are capped at 50%.
Then we have the code challenge.
um the code challenge, the assessment for um to integrate theory and programming language, right?
You know, MCQ just test um mostly the theory part or if you have a project, you do a project and ask question about the project, but code challenge is here to test your technical abilities, right?
So the challenge is not timed. Okay? So you can see the release date and the start date. But one difference with code challenge from multiple MCQ is that before the deadline for code challenge you have unlimited attempt. So you can keep attempting unlike MCQ that you have your first attempt score 10 and three supplementary attempt but for code challenge before the deadline you have unlimited um attempts right that's the good thing about code challenge. Good. So for this module it's 8 weeks and the recommended time is 280 hours right that's recommended time. So if you want to know the time for each week you divide 280 by 8 you get an approximate time you need to spend a week but you may spend more or less time depending on how fast or slow you can go through the content. Okay. So we'll be looking at programming basics.
Then we look at functions and control flow.
Then we'll look at data structures.
Then we look at exploratory data analysis. So everything we are learning in Python is to be able to have the core skill and technique in Python to be able to do data analysis with Python.
Remember I said everything we did with Google Sheets, SQL and PowerBI, you can do everything with Python. Okay? So, Python is like integrating everything you've done with Google Sheets and more. And the more comes when you move to machine learning or data engineering. Good.
Now, we have eight weeks in Python. The first four weeks [clears throat] we call it the basics, right? Python basics, right? Don't be deceived by the name. It's just the core concepts to give you very foundational knowledge of Python. And in the first four weeks, we have one assessment per week. So the first week we have a code challenge. This week two we have code challenge. Week three we have an MCQ.
Week four we have a code challenge. Then for the next five weeks is um we call it advanced Python where you're applying the Python to data problems. So you're now using the so the first four weeks you learn the core skills of Python.
Then the final four weeks that's from week five to week eight you are now using the skills you have learned and applying it to solve data problems.
Okay.
So for week five we have an MCQ. Week six we have an MCQ. Week seven you have a code challenge and week eight we have two assessments. Python exam part one which is theory and Python exam part two um practical.
Will we get the slide? That's this slide. Um Halema can you confirm if this slide will be shared with the learners?
Sure actually.
>> Okay. All right. Good. Good. They have confirmed it will be shared shared.
>> All right. Thank you.
All right. Thank you. Thank you team. So remember okay I know sometimes when it comes to end of the program you guys will be asking have I passed? Did I make it? Am I graduating? So I'm going to say this very clearly. I want you to listen very clearly. There are a total of nine assessments. If you count them 1 2 3 4 5 6 7 8 9 to graduate you need to pass 75% of the total assessment that and 75% of nine is seven. So you need to pass at least seven assessment to graduate. I repeat seven assessment to graduate. So please, if you're writing an assessment and you are now getting to the point where you're using your supplementary attempt, be very careful how you use your supplementary attempt. We had people coming to us in data analytics and saying, "Oh, my supplementary attempt is finished. Help me. There's nothing we can do. You have three supplementary attempts." When you write the first supplementary attempt and you see you didn't pass, you have two more.
Please be very careful so that you don't exhaust your supplementary attempt. If you exhaust your three supplementary attempts, it means you have failed the assessment. And if you pass six assessment, you're not going to graduate because you need seven assessments to meet the graduation requirement. I hope that is clear. Okay, good. Um, before I move into the code challenge submission work, I just want to get you what what do you think about Python now?
Especially for those of us who are just interacting with Python.
Yeah. Yeah. Yeah. All nine assessments are weighted evenly. Okay. So, what's your thoughts on Python before I move to the code challenge submission? Anybody in the chat? Are you still scared?
Anybody scared but excited? Okay.
All right. All right. All right. Okay.
Okay. That's nice. So, let me move to showing you how we interact with um the code challenge. Good. So um what will I do? Where is it? Okay. So um when you go to attainer for those of you who who have been able to enroll for Python that's those that initially were digitized for data science and data engineering those are in digital for data analytics. Uh we are working to make sure that you be enrolled this week. Uh so don't there's nothing to worry about. So I'm going to move to those that Python C1. Good. So when you come to Python C1 this week, so for those have not enrolled yet, this week is your is your orientation week. Nobody have access to the courses. So this is the only thing you will see an outline of all that you'll be covering, right?
So feel free to read through this. So you can't access any of the course right now because the course is going to be open next week. Okay? So let's assume we have gotten to next week Monday. Okay?
By some time travel magic, we get to next week Monday. So all these contents are the content for the first four weeks. So only these three content I released from this week. So from 1 to four are released for this week. Then five, six or seven there about to be released for next week and with each coming week this content will be open.
Right? At the end of week four we will complete this and in week five the remaining content will be made available for you. Okay. But for this week I want to walk you through how you can submit your first assessment. Right? So you have to pay close attention. The autograder is very strict. Any deviation from the instructions would give you a score of zero in your assessment. So please pay um close attention. Okay. The pass mark for each assessment is 50%. The 75% I talked about is 75% submission rate.
That's out of the um nine assessment you're supposed to pass 75% and 75% is seven. But to pass an assessment, you need to score at least 50%. Okay, I hope I answered that. So when you come to when you come to Python data structures, you'll see your assessment right now. I want you to please make sure you're reading everything. We have observed over time that some of the learners don't really take their time to read the instructions properly. So please pay attention to details. Right? As a programmer, yeah, we know we are data professionals, but because we need programming, we have to double up as software engineers or programmers, people that use programming language.
And as a programmer, you have to pay close attention to details, right? Good.
So, when you come to section 4.11, it said this notebook is a quick guide to how code challenge works. So, you download the train. Good. I've downloaded it. Then I would go to my download section and you can see the code challenge guide. Good. So um I read the instructions. I follow what is saying but I'm going to come down here.
Look at this tip. If you don't read this tip, you'll not be able to pass that the code challenge assessment. Right? So the tip says download a copy of the notebook and rename it data structures code challenge. So what it says you do download a copy of the notebook. So we'll go to the notebook. Which notebook is it talking about? This is just the um code challenge guide. Right now this is the code. You're not you're not submitting this notebook. The notebook you are submitting is this notebook. Let me go to it. The one 4.12 data structures code challenge. Good.
So I'm going to say start.
Start. Good. Now it says download questions. So I'll click here download questions. Okay. So I'll go to my download section. I want to show you to see how the notebook comes. So I do believe you can see you can see the notebook the one I downloaded for the this is the code challenge guide. This is the one I'm submitting. GCC data structures code challenge. Let me go back to the browser and you see the name. Okay. Let me refresh it. Good. So this is the notebook I downloaded. GCC data structures type student version.
Okay, what did the instructions says? It says download a copy of the notebook uh and rename it data structures code data [clears throat] structures code challenge to data um structures code challenge work. So I can decide to rename it to statru code challenge work then use it to complete the code challenge and do it for a rough work.
All right. So when I'm done I now download another notebook the same one that's this one this data structures code challenge and what type your code in another notebook of what data structures code challenge and submit the file to the answer. So make sure your notebook runs in the notebook you're submitting. Good. So I will copy this name data structures code challenge. I would go to this and I would do what? Rename it. So, let me just rename it here.
Rename data structures code challenge. If you don't do this, you won't be able to submit. So, this is the notebook I'm going to submit. Data structures code challenge. Look at the name. If the name is not exactly this, you won't be able to get your 100% score. All right? So when you download the notebook, you just rename it again.
So I would repeat that step again. Okay.
So let me go go to my download section and delete this notebook. Um data structures code challenge. I move it to trash. Please if you are confused, you can watch the recording again.
Okay. So this is what I'm going to do.
Good. So I come to data structures code challenge. I say start start and download question. Good. I've downloaded the question, right?
Good. Then you see I have my code challenge here. I will rename this notebook. I would go here, get the name of the notebook. I want not code challenge py. And I can rename it. I can rename it anywhere. So let me say I rename it here. Rename it here. Good.
I've renamed the notebook. Good. So before I start working on the notebook, I want to show you something because sometimes you want to work on one notebook multiple times, right? So this is what you should do.
Go to your desktop and you create like a folder. All right? You can put it um data structure code challenge. Just put um I can put anything you want to say data structures code challenge version one. Okay. Then I copy this notebook. Copy to make it work easy. I copy this notebook and I put it here. Right? So when I work on this version of the notebook, this one I work on it. I want to maybe I submitted. I'm not comfortable with the score. I want to improve it. Before I change it again, I create another folder.
Okay. I say um can you guys see my screen?
Okay, good. I say maybe another folder.
New folder. data structures code challenge v2 good okay okay then probably I'll copy this notebook again remember I'm creating new versions of the notebook but the notebook name is not changing so why I'm doing this is that I'm having different version but the name of the notebook is very the same is the same I'm going to explain why good so let's assume I've worked on my code challenge and I'm ready to submit right now I say upload my answers. Okay, I would go to my document where I've worked on the notebook. And here's my document. Um, desktop elx. Please don't mind me. I'm very very structured. I'm very structured. I like putting my things very well. It's easier to find. Okay. So, Python data structure. So, you see how I did my notebook V_sub_1, V2, V3. So that the name is consistent. So, let me come to V1. All right. Good. Yeah. One more thing I forgot to tell you. Yeah, let me go back. Let me go back to my desktop.
So, um, where's my desktop?
When you are done working on your notebook, for example, this notebook, right? I'm done working on it. I want to submit. You have to zip it. And zipping the notebook, you right click on it and you put what? For those using Mac, you say compress. For those using Windows notebook, you just put zip or something like that. For those using Windows notebook, when you just click on zip, that's it. For those using Mac, you click on what? Compress. When you click on compress, you will click on rename.
All right. You can see is ipy.zip.
So just remove keep remove every other thing and leave the zip. Move the ipy ng and leave the dotzip. So if you're using Mac, that's what you need to do. So this is what you'll be submitting, not the raw file, your zipped file. Okay, I hope that is clear. So I'm going to go back quickly to submit my code challenge. So as I've said, I'm going to say go to my desktop where my file is and I'm going to look for good Python good Python data structure. So this is I I this is a a version, right?
So I open this version. I've zipped the file data structures code challenge. I click on it. I say open.
Yep. And I click on save. Is 98% loading. This is not your score. It's loading. Good. Now I've uploaded my notebook. I will check my report card.
You can see I got 70%. You can see 70.
It showed me the areas where I pass and fail. All right. I pass and fail. Good.
Okay. All right. So, I've passed the assessment. But there are some of us who really want to get more. We really want to score more.
Right. So, when you get a score like this, right? Remember this is the notebook I worked on. Let me go back to my desktop. Okay. The one I submitted.
So, this is a notebook I submitted. Um, look at it here. Desktop. Desktop document.
Okay.
Um this is the notebook Python Python data structure. Yeah. So this is the notebook I submitted that gave me 70%.
Now when you want to improve the score of your notebook, don't work on that notebook directly. Go back, create another folder, copy so that you have that original version. I'm going to show you why.
No, if you have 50%, yes, you can still retake it. even above 50% is different from code um MCQ. So if I don't create another copy and keep the original copy, this what the problem you can have. If I leave my original copy and work on it and I say let me submit another copy, I think I can do better and I say start code challenge um upload answers upload and let me say I come to Python data structures. I come to another notebook I've worked on and I'll submit and I'll say save.
Good. And now check my report card.
You can see it's 40%. Yeah, Lawrence, watch the recording again. Don't worry.
Watch the recording. You will understand. You can see now I now have 40%.
Now it is at this point some learners will come to us and say, "Oh, I wrote this test. I scored 70%, now it's 40. I want back my 70%." At this point, there's nothing we can do for you.
That's why if you get get a good score and you want to improve in it, keep that original notebook, create another copy and work on it. So if I have 40%, it means I have to go back and work on a notebook or revert back to my old version. So because I have the old version, I can just say, okay, fine, I don't want the 40 again. I'm going to go back to my old notebook and get it. There are multiple attempts.
There are uncountable attempts. It's not just three attempts before the deadline there multiple attempts. So I say okay fine let me go back to my old notebook since I have a copy of it I don't want to get 100% I can work with 70%. And because I have a version of the old notebook you can see I got back my 70%.
But let's assume the deadline is not due and I keep working and improving my score create more notebooks and I just feel like okay fine at this point I think I can do better. I've worked on it and I have a good notebook. So, I'm going to go back to submit again. And as I said before the deadline, you can submit multiple times. So, I go back, create another notebook, work on it, and I say I think this will give me 100%.
And I click save and if I check my report card, you see I have 100%. Okay, good. So, I've gotten my 100%. So you have the opportunity to keep working on the code challenge over and over again. But if you have a notebook that gave you a good score before you work on that notebook, create a version and keep it. So after the deadline, if you have not submitted and you wait after the deadline to submit, yes, you can submit after the deadline if you have not submitted. But submission after the deadline will give you a score of 50%, your score will be capped at 50%.
Irrespective of what you get even if you get 100%.
Okay. Can the challenge work on Ubuntu?
Ubuntu.
Um, did you use Ubuntu? Um, Dian, you say Ubuntu? Did you use Ubuntu for data analytics for your As long as you can open your Jupyter notebook?
You'll be fine. As long as you find a way of opening your Jupyter notebook, yes, then you'll be fine. Okay, good.
So, that's all for the code challenge.
Please if you have any question feel free to reach out to us and ask for clarity or ask the community. Just if you have any questions feel free to ask and we would cl uh work on um clearing your clarifying any question you have. Can I use MacBook for Python? I'm actually using MacBook.
So I'm using MacBook for Python. And if you remember when you're installing Anaconda there's the Windows version and there is the Mac version. So yeah, you can use MacBook. I'm currently using MacBook myself. Good. So that's it about the code challenge. So let's look at the Python jump start kit. This especially for absolutely beginners, those who have never coded in Python before. We decided to create something simple for you to use. Okay. Um the link will be shared with you. Uh let me just quickly share it here.
um you if you have passed at least 10 assessment then you have graduated from data analys um data analytics right so if you have if you didn't pass up to 10 assessment it doesn't mean you have graduated so you need to meet the graduation requirement so this is your python job start kit I'm going to quickly share it in the chat I know it will still be shared with you on e-hub but for being here so what we why we created this is for you to familiarize yourself with Python. So build this. So there are different short videos and resources for Python. So this is the video. This is the resource right. So for every video so you can see getting started with Python overview. You can see overview introductions Google Collab. You can see it here. So this is the video and this is the Google Collab notebook. So I can click on this. I would see the Google Collab notebook.
Okay. This is just a slide. Okay. Let's go to the intro in introduction to Google Collab. So this is a video. Okay.
So I can just quickly play the video. So you watch by playing the video.
>> All right. Welcome to introduction to Google Collab. So the learn objective for this course for this video is to understand what Okay. So you can play the video, watch the video and all that. Good. But if you want to access the notebook I use in the video, you click on the notebook. You can see this is the notebook. The Google collab notebook will be made available to you. And if you want to code along, you can all you can easily do is just come here, click file, then go down to uh where is make a copy. Oh, I'm the owner of this notebook. I won't be able to make a copy. Um, okay. Let me see. Python variables. Is this me?
Okay. Yeah. Okay. Now, when you have a Okay. No, this is not me. I'm trying to find the notebook.
Okay, this is crazy. I'm trying to find the notebook. Okay, fine. If you have the notebook here, just you click on file. You should see save a copy to drive. Exactly. So, you just click on save a copy. You have a copy created for you and you do it. How about those who have no longer their previous laptop and want to install new Jupyter notebook again. So if you have lost your notebook and you want to install new jupy notebook, you just come to uh which section is that uh code challenging. There's a section that they said how to install um markdown working with notebook on Atheina. Okay, there's a section that talked about how to um install and cond.
If you the laptop you're using before, kindly go back to the data analytics section and watch how to install Anaconda and your notebook. Right? Good.
So, just walk through watch through these videos and these links and go through them to be able to have an idea of what Python looks like.
Yes. Thank you. Blessing. That's the respond. You go back to the DA course and find out how to install it.
Um what model of Mac can the model of Mac you're using should be enough Juliet or you want to buy a new system if you want to buy a new system is different but if you have a Mac system the Mac system should work all right good so this is a jump task kit feel free to explore it especially if you are beginner to python so that you have an idea of what the program is p um code challenge is all about you want to get a new one um Julia send me a DM on e-hob If that's okay, you can find me on EHB. My name is Shun Law. Um, I will need to go into more details to look at to be able to provide answer because we have to look at specification, price, what you want.
So, I don't want to have that conversation here on the call. So, Juliet, if you can hear me, send me a DM on e-hub. My name is Shagun Law. Let me just type my name.
Uh, no, that is it's not that you Yeah, there's no special.
Yeah. Yeah, Shelia. And you will be you will be. Okay. Um Yeah. So that's it about the jump t kids. So feel free to explore it. It's I I'm going to tell you the truth. Python is very fun. It's and especially when you bring out your mind to learn it, okay? Python will eventually change your life for good.
I'm telling you, it will change your life. And one more thing before I continue to the next stage. You have to make amendments to your life as you are going into Python. What I mean is this.
You have to cut down the time you spend on social media. I hear people say, "Oh, we are busy. We don't have time." But some of you watch football. A football match is 2 hours. Some of you watch more than one football match in a day.
Tomorrow is Champions League. You're going to watch tomorrow match. Some of you manu fans will watch match today.
And a match is 2 hours. Some of you watch series. You watch movies. These are long hours. Some of you have gone to watch Michael Jackson, right? The new release. These are two long long movies, right? Some of you spend so much time on Tik Tok, Instagram, Facebook. Come on.
You have to cut down your time on this thing. It's just 8 weeks, right? You have to make adjustment. In fact, your friends or people around you should look at you and tell you, "Oh, you are changing. You don't have time for me."
In fact, if if it means your better half or your partner, you tell them to understand. I want to do something that will change my life. Can you please give me some time?
[laughter] All right. So this is why we created the task kit to you know bridge the gap for beginners and we help you introduce you to the core concept of Python. Right. So when you have this um slides you click on this link.
Okay. So all I'm just trying to say is bring out time. You have to dedicate so much time. If you are playing smart with data analytics maybe you didn't dedicate more time. You can't do that in Python.
You have to be very deliberate um with it. Good. Um Uber, I see you're here. You want to take this part success in Python track or should I just go along?
>> Sh please carry on.
>> Okay. All right. Thank you. Good. So success in Python track. Now let's face reality, right? How many of you have come online and you see they say invest and you make 150% gain and you know you hear those kind of ponzi skin like kind of things and you just feel like this looks too good to be true right you understand what I'm trying to say good so we're going to be very honest with you about Python right what does success look like in Python so how do we define success you know what will happen that you say oh me learning Python in the next eight weeks or at the end of the eight weeks is successful.
This is what we talk about success learning and grasping basic Python concepts building a strong foundation um easier problem solving later. So it's a process. Python is not learning Python is not just an event. It's a long process, right? Remember it's a programming language and you cannot um totally learn a language in just 8 weeks. So the essence of this 8 week is to give you a solid foundation to build on going forward. I am still learning Python till today because you keep improving your in fact some of you you have been speaking a particular language even the English we speak we are still learning similar to Python the essence what we define as success is you understand the foundational concepts whereby after the program you can build on continuously from there. Okay. So what success is not okay not success becoming a Python guru in 8 weeks comparing your skills to global expert. So we are not looking for you to become expert in 8 weeks. If you know anybody that can code in Python and you will have colleagues in this in this call that are proficient in Python in quotes and if you ask them how long it takes they will tell you or give you an idea. Okay. So the goal is not to become a an expert in 8 weeks but the goal is to build foundational understanding and knowledge and concepts in Python so that as you make progress in your data career you're able to build from there. Okay, I hope that makes sense. So Python guru how long I'm going to ask you in the question in the chat please. How long does it take for you to become a Python expert? Anybody just put your random answer in the chat. How long do you think it takes an individual to become a Python guru? Okay, Joseph said 2 years, 6 months. Oh, Abdul Aim say never. 5 years plus. Somebody say yes. Okay. Forever. 3 years plus. Okay.
Good. Nice. Nice responses.
So there oh there's this there are okay I was trying to read something infinity all right becoming a Python expert in data science is a journey based on your experience or expectation okay less than 6 months usually enough to grasp basic and start doing simple data analytics or simple machine learning project for junior roles okay 6 to 12 months now this les than 12 months is with consistent study.
Not like you study for a week or two, then you take a break. We're talking about consistent study, right? No interruption for Tik Tok and um Instagram and Facebook.
6 to 12 months, you become comfortable with common libraries and workflow ready for more independent work. one to three is this is where you build deeper expertise, solve complex problems, write clean production codes and confidently handle a wide range of data science task more than three years often for senior specialists, leaders or those mastering um advanced areas like deep learning or large scale system. Okay. So you can see that it is a journey you keep building. Okay. So what we are doing in this eight weeks is to give you the foundation and basic concept. Once you understand the foundation and basics of Python or the language or the concept of the language, you can keep building on it as you build your career in the data space. Okay. So winning the Python data race. What kind of race is this? Anybody? Anybody? You have seen a race like this. What do you call this kind of race?
Come on. You guys don't want sports.
Marathon. Good. So, Python is not an event for us Bolts and Justin Gatlin.
No, no, no. Python is for the uh is it Moara they call him? [laughter] Yeah. Heavenly race. So, it's a long distance race, right? So, as I usually say, it's not about intensity, it's about consistency, right? So, when you want to run a marathon, you don't start.
So it's not about oh I spent 8 weeks sorry 8 hours of 10 hours on Python today then you not touch it again till the following week and you do 10. No it's better to do 1 hour 2 hours 3 hours every day consistently over the 8 weeks rather than go on intensity right. So consistency over intensity.
So Python is like running a marathon right? You don't have to start at full speed and expect to make it to the finish line. So start with a steady pace. Build your rhythm. Focus on putting one foot front of another. So over the next 8 weeks, your goal isn't to be fast, is to keep moving forward, stay consistent, and cross the finish line with energy to spare. Another important thing I would say, do not learn alone. Okay? Do not learn alone.
It's going to be very difficult. You can have an accountability partner, a study body. Find people within your squad, within your country or outside your country, in the community. Have that one serious person that can carry you along.
Right? Good.
And why systems beats goals. So goals is equals to destination. Right? When you talk about looking at the goal, you're looking at somebody say if we don't go fast, deadline will go catch us. Okay, that's a Nigerian speaking I assume. Um yes, we you have to have a steady speed. Not just going fast without understanding, right? You have to have a steady speed.
So goals equals to destination, right?
So if you're focused on the goal, you are only focused on this destination in quote, right? But when we talk about system, we're talking about the repeatable steps that get you there. You break down the task, the daytoday, minuteby minute task you um execute to help you achieve your goal. Okay? So a strong system makes success inevitable even on low motivation. Right? So if you have a system or a routine that you put in place, so whether you are motivated or not, you'll be able to keep moving, right? If you rely on motivation, you only read or study when you are excited or you feel like. So having a system makes you study or make progress whether you feel like it or you don't feel like it. Okay. So example of Python success, the goal finish all Python modules in eight weeks. Right? This is the goal.
It's it's very broad. It's very vague.
It looks a bit cumbersome, right? But when you break it down into systems, the way we did in problem solving, you know, you break it down using the whole flowchart and different system, you fix daily coding blocks, right? So you say maybe 30 to 60 minutes, you take a break, 30 to different blocks. Um, review and practice notes immediately after webinars or live sessions. Submit assessments 48 hours before deadline. So have two days buffer. What I mean by two days, don't wait until the deadline day before you submit your assessment. What you need to do is to give yourself um 48 hours earlier so that if in case you miss your deadline, you have two extra days to be able to catch up. All right?
Then have weekly check-ins with a study partner. As I said, get a study partner, a study body, an accountability partner.
get involved with different people so that you have people too that will be accountable that can quickly check in on you and ask you oh have you submitted your assessment are you on track and all that what I mean is do not do this course alone all right good so thank you very much for listening I hope this was very helpful all I would tell you as I said earlier you have to find a way to love Python okay so before I hand it back to the team I want you to type in the chat I love Python okay let me start that I love Python. Okay, can we all say we love Python? Thank you very much for listening. I'll be at the Q&A to respond to any of your questions. So, Hale Lima, uh, I'll hand it over back to you.
>> Thank you very much for that deep dive and you guys have gotten clarity so far.
Um, I've been seeing some hands up and also questions in the Q&A. We're going to attend to them very soon. Um, let me just share my screen.
Okay. And so Chev actually said at the start of the segment that for those people who actually enrolled for data science data engineering at the start of the program they don't have to enroll again for Python but for those people actually start data analytics they need just for data analytics and maybe along the line they decided that they wanted go for data science data engineering you have to enroll for Python chart to be able to do data science with data engineering so this particular workflow is for how you can enroll for the Python chart if you haven't already so I'm just going to do an intro of the demo and then I'll let you know when you can share your screen and uh you will. All right, just a minute please.
All right, screen is coming up in 3 2 1 zero.
Awesome. So currently um if you are a data analytics learner current um in ALX at the moment this is what you're going to be seeing at the moment um on your ehop right so this is what it looks like at the moment you can confirm that and if you are a data science learner if that is if you enrolled from the beginning um of the program as a data science learner this is also what um you would be um seeing um on your e-hub. So for data science learners, it's very easy. Um all you need to do um is come to you'll see Python track and you can just click on continue um to continue to um learning Python track just ensure that um you um you are enrolled before you know the start date um for course 7 Python and then for data analytics learner given that this is what you see on your screen um the question therefore is where is Python here. How can I proceed? As we can see, a majority of us are already saying yes, I'm going to go for Python.
This Python, we're going to we're going to do this. We're going to do this, right? So, but where is Python now? How do I, you know, enroll into Python? So, I'm just going to let Wellin share his screen to walk you through that um journey.
>> Okay. Thank you Bumi for for that.
Um so here is um what we'll be showing on your screen when you enroll for data analytics. I guess everybody can see my screen now.
Can you confirm please? Okay, thank you.
All right. So when you enroll into data analytics, this is what you see. There is no Python, there is no machine learning. and you're wondering how am I going to do this? So, we've got you covered this time around because we've got this video for you just to show you how you do it. So, if you have um um enrolled only for data analytics, here is what you do. You need to uh scroll up a little and then you click on view all programs. Here you get you see this. You click here and then you will be able to view all programs. And then you see this is data science. You need to enroll again into data science so that you get to do uh Python and machine learning. So you click on enroll and then you click I confirm um and then it starts loading.
Once it confirms you will see this now but you're wondering what happens if I enroll into data science after data analytics. Am I going to uh repeat doing professional foundations? Am I going to repeat doing data analytics because it's there on the p on the data science uh page? So what the system does is when you have already done professional foundations, it will be writing here instead of continue to be writing completed completed and then you just have to continue with Python. Then you can continue with your Python. Very easy guys, very simple. Uh this is what we have for you. So this is what you do.
You just have to um enroll into data science and then the system automatically picks that you have already completed PF, you have already completed DA. Now you are moving to Python. So this is very easy easy peasy right I think uh I'll stop here for now.
>> Okay. And that was a walk through for the Pon for those who welcome a little bit.
Okay, let's go to the next segment.
The next segment is the community.
Hi, let's welcome let's let's keep our images coming in.
Let's check.
>> Thank you, Halima.
Thank you guys and I'm glad you guys made it to Python, right? I know we had our tambali last week and it was really exciting seeing many of you joining and now we are moving into um Python which is great, right? So I want to talk basically about the ALX community, right? The DP community because this has basically been your home for the past 15 weeks and it will continue to be your home for the coming weeks as you move through the Python track and as you also move into your respective whether data science or data engineering tracks. So if you're ever confused, stuck, unsure about what's happening in the program, you need to hop on the community because all the information is there. It should be the first place that you check aside your learning platform that's Atheina.
So everything lives here, announcements, resources, support, even meaningful conversations with your peers. So once you get comfortable navigating this, your learning experience will become a lot lot more smoother. So first off, I want you all to be very clear about something. This program is not designed for you to do it alone. Yes, you study independently, do your courses, um do go through your modules independently, right? But your success will significantly increase when you engage with the community. So the data programs community space is filled with learners who are going through the same challenges the same um learning asking similar questions and figuring things out just like you. So we want you to use that leverage that lean into it and take that community as your strength and not a distraction.
So next slide please Halema. So how can you take advantage of um this community?
Right. One thing I love about ALX is that the community is never passive, right? Your questions, your feedback, your ideas, everything, even your frustrations, right? Will shape the experience for every other person. So don't just hide it, share it. If you see a gap, speak up about it. If you have an idea, share it. If you if something is unclear about the course, ask. There will always be someone there to respond.
And it's your learning journey. You are encouraged to take ownership of it, right? We want you to lead, take the lead. Don't be afraid to share on the community.
So, um I'll speak briefly about how we show up in the community, the guidelines because the experience you get here is heavily influenced by yourselves. It's influenced by how you treat each other, how we all treat each other. First up, very non-negotiable is respect. Right?
Everyone here is learning. People ask basic questions, make mistakes, and even sometimes misunderstand things. It's normal, right? So, I would like for everyone to be kind when asking. Be patient. Respond with empathy, right?
Don't shame anybody cuz you're all learning, right? Don't be sarcastic.
Don't do you should already know this, like perhaps a person wasn't able to join a particular session and they're asking a question on the e-hop. Be be kind, be patient, respond to that question. Don't assume anything about every anybody. So we want to make sure that the environment is safe for you right. So please like I said respect is a very very key thing and I said patience as well when requesting for support when also supporting others and please please follow our rules both online even within these zoom chats. Um let's be respectful let's be kind. Let's let's be human, right? Be kind humans. Let me put it that way. So, um, something else I want to touch on is using the right spaces for the right things, right? So, we'll move into the type of spaces you see.
I'm sure you've seen some spaces while you were in your DA track, but we would like for you to use the right spaces for the right thing so that things don't get missed in the process, right? Um you know if you're asking for help but you're putting it in um say your squad let me let me put if you're asking for help from a technical mentor but you're putting it in your squad channel it will get missed the mentor might not see that question right and it will be something that might slow you down. So um ask questions early and clearly and don't just log in and disappear and show up for exams. Right? That's not the type of program we're in. Right? So, we would love for you to engage in discussions, share insight, celebrate your wins, and participate consistently, honestly, because we've observed that learners who participate consistently tend to understand better and finish stronger.
So, it's a learning space, but as much as it's a learning space, it's a professional environment. So, no harassment, no offensive language, no spamming. We want it to be safe for everyone, comfortable for asking questions and learning out loud.
So now these are Sorry Hale Lima please go back a step back. Right. So now let's talk about the community hub right a step forward. Sorry Hale Lima. So the community is like your central hub, right? Your learning headquarters, right? And your cohort space will be labeled as Python 05 2026. I'm sure some of you have already started seeing the spaces. If you are yet to see the spaces, don't worry. Um by before the beginning of next week, you should be able to be added to that space. And if by next week Monday you're still not add added, please feel free to um submit a ticket to our help desk and we'll get it sorted for you. So your cohort space is labeled Python05 2026 and that's how you know where you should be right inside that space. Everything is organized intentionally so that you understand the structure and um you're not lost on where to ask certain things. So a quick breakdown of it. You have the Python05 2026 announcements, right? That should be the first place you check every regularly, right? Every week, even daily if possible because important updates will always be shared there first. So try to check it daily so you don't miss out on any important updates whether about assignment deadlines or events happening. Always go to the announcement space to see what's going on. Then you have the program hub. This is where your learning resources live. The recording for this session, if you're looking for it, go to your program hub. The recording of your PPL sessions, you're looking for it, go to that program hub.
The Python jump start kit that Shu shared a while ago. You're looking for it, go to that program hub and you'll find them there. Your materials, recordings, links, everything. If you're looking for that, start from the program hub. And then we have the ask ALX data programs team, right? So if you have any program related question, you should go there. So if you're unsure about deadlines, you're having blockers with respect to a particular assessment or you're having access issues or just technical questions related to the content that you are learning, don't hesitate to ask on that um particular space. And then aside these four um three major spaces that I just um highlighted, there's now your country space, right? If you are from Kenya, you have a particular space. If you're from Nigeria, you have a particular space.
And these places are where you can connect with your peers, right? Peers in the same country as you, peers in the same region as you. So you can um create study groups from there, have accountability partners, encourage even encourage yourselves like push your squad together to to complete this journey together. For the diaspora, there's a space for you. We we won't forget you. And that is the ALX Africa space. I'm sure you've seen it around.
So those in diaspora diaspora those are who are not in the eight major um countries that ALX has all hubs in. I know we are creating new hubs in specific um countries but those haven't been implemented in our space um um community space plan yet but for the eight major um ALX countries where ALX is operating that's Kenya, Ghana, Ethiopia, South Africa you have your specific country spaces and for those outside those countries we have a special space ALX Africa for the diaspora and um spaces that for the diaspora and um learners that are not um from the eight major spaces where where ALX um operates right hubs. So, um I'll pause here, right? Um we had to alllight those spaces because we want you to have the best experience, right? Um using the right space will keep your communication clear and help everyone get faster responses. So, um I would stop here. Um, I think Kale Lima would show you a brief demo of what your spaces look like and um, yeah, so the demo will be there to help you feel more confident about navigating the platform and once you know where things are, you'll spend less time searching and more time actually learning. So we want to remove the friction of searching for things when of you searching for things, right?
Learning Python is already challenging and we don't want the platform to be another obstacle that will stop you from reaching your goal. So, Haleima, over to you.
Or should I if you're unable to?
>> Yes, please. Just go ahead.
>> Okay. So, let me just take you through a run through of what your space will look like. So, like I said before, I hope you guys can see my screen. If you can, give me a quick thumbs up.
If you can see my screen, give me a quick thumbs up. Okay. Okay. Thank you.
Thank you. So, this is what it looks like, right? We already have an announcement up for you, but like I said, you have the announcement space, right? where you can get all updates, official deadlines, program notices and everything. Right? You see there's already an announcement here for you.
I'm sure some of you have already seen it. Please be sure to go through it and ensure you take the information that you need from it. And then the program hub that I was talking about um it's currently not populated here but immediately after this session you'll start seeing event recordings um Python jump start kit community guidelines everything you need to know to make your journey smooth you see it in this particular space the program hub and then you have the alx data programs team here you can post ask your questions and um our team will be there to help you unblock and respond to those questions.
And then you have your specific squad spaces you can start posting there. Now let your peers know that you're excited to start Python. So we have South Africa, Ethiopia, Kenya. Just go in there, type a post, say hi. Um, welcome your fellow learners that made it from DA because it wasn't easy, right? Just try to identify those that you learned with in data analytics and bring it back, bring it into Python and collaborate. So that's what should um your spaces will look like guys. Um I wish you the best. I wish you success.
As much as I talked about the ask alex data program, don't also forget about a base. Right. And um on this note, I will hand it back to Haleima to facilitate the Q&A.
>> Thanks. Thank you very much for that. Um okay guys, this is time for you to and ask your question. If you have any questions, you can raise your hands. But in the meantime, let's go over the Q&A part of this. Most of them can answer your questions. Um, okay. Someone is asking, I'm enrolled, but when I click continue, it doesn't take me to Python. It's not there. What should I do? Um, okay. Because this is not the training starts next to it. So, okay. Okay. Uber has an answer.
and I will answer that question.
>> All right. Thank you. Um, thank you Hima. So, I would use this question to respond to every enrollment related questions. Um, we understand that at the moment you do not you still have Python grade out for you whether you're data science or data engineering. For data analytics learner looking to take Python, you probably do not cannot see data science as a course to enroll into or data engineering.
Um so let me take away the from the first one.
Python is still grayed out to you. We understand. But by between today and [clears throat] before the end of this week, by the end of today probably or tomorrow or midweek, you should begin to see that it's no longer grayed out. You would have been marked as graduated for data analytics and or have it marked as completed. And then you'll be able to opt into Python just like the walk through video showed you. So the patience right um the systems are automated in such a way that it first graduates you before you can then um before it unlocks that next track which in this case is Python right so um just exercise some patience you will see that um available for you in the coming days then for those data analytics learners specifically who may want to op into Python while we have shown you what the process is at the moment the data science um may not be data science or data engineering may not be available on EHB um but we're working to make that [snorts] um available so be on the lookout for that as well alternatively if that does not happen you will be able to join um the self-paced data science program when it's when it kicks off in the coming months, but we'll do our best to make sure that uh you can try to enroll for it with this current cohort. Um then on AWS as well because I see it's also um part of the concerns that were raised during the walk through on enrollment. Um, so that video is not the most recent video. So at the time the video was recorded, it was we had AWS as an offering. However, as of today, ALX does not offer AWS the AWS program on any of its on any of its um I mean with as one of its portfolio um programs. So none of you will be saying AWS and that's um that's no problem at all. That's no problem at all. It has been discontinued as a follow on track for the data science program and it's also not a track for the data engineering program.
I hope that answers all those questions on enrollment in AWS.
Thanks. Thanks. I believe that answered your question to Michelle and the said just to retreat what you were going to say earlier. Keep in mind this week is just your orientation week. You won't be starting learning this week. Your learning week starts next week. As such, just use this week to settle into the track. begin to go through the Python jump start kit which has been shared with you or will be shared with you and then um in the coming days like I already said all of this um you'll be able to opt into the Python track and then next week begin your your learning >> thank you um okay um the certificate question has come up again for I think data analytics uh I'm going to say that your your certificate is is entering it. It's going to get to you. You'll get it soon.
I believe massive book your certificate will get to you soon. Um someone is asking what realistic thing can Python help me achieve. Okay, I will then allow Sh.
Any technical mentors here?
>> Yes, I'm here. Please can you get give me a question again?
>> Person is asking what what good can Python achieve for me?
>> Uh is in the Q&A or in the um chat?
um in the Q&A actually.
>> Okay.
So I can read the question one more time.
>> Um okay. Uh okay has actually responded to that question. It was before it was responded to.
>> Okay.
>> The question is what realistic thing can Python make me achieve?
>> Um did the person attend this session that will explain what Python can do?
[laughter] >> Okay good. So, Python can do a whole lot of things.
Every every automation kind of things.
Python can automate things. Python can build applications for you. Python can build games. You can use Python for so many things. But with regards to data, you can use Python just as Bar have said, you can use Python for data analytics. You can use Python to analyze data. You can use Python to query data.
You can use Python to visualize data and going forward you can use Python to build machine learning models. You can use Python to build apps to deploy these models. You can also use Python in data engineering for your ETL and um to create your pipelines and all that.
Python is a very very robust language like um whoever has this question is just like saying what can what what can um Dangot realistically do like it's it's it I can put that question in that way.
So Python is the boss. It can do a whole lot of things if you ask it correctly.
Yeah, I think I'll leave it at that.
>> Right. Thanks. Um okay. So, and some people are raising concerns about I can see a couple of questions around not seeing the Python community yet. The E-Hub spaces or the Yeah, the EUB spaces. Um, just um calm down. You will see those spaces very soon. If by um maybe the end of this week you still haven't been added, then you can send the tickets to Fresh so that they can look into it. Uh I believe that answered the question about not seeing the Python community and announcement spaces yet. Um someone is asking is there a view where I can see my overall mark for data science similar to professional foundation.
Um Uber would you like to get?
>> Can I get a question again?
Is there a view where I can see my overall mark for data science similar to professional foundation? Overall mark uh on Athena it's just um going to be some I don't want to say tedious process but basically you have to look through all your assessments to see your marks like for every assessment you see the marks you scored in them you see for those even the supplementaries if you hover over it you would see your actual mark but keep in mind if you get a supplementary score that's what actually goes into your records so um I is your best bet to get that. Go through all the assessments, check out all the scores you got got there. If you want to get the overall average, just add them up and take the average and that that points you at what your overall average score is. But um as per database is something else that can show you that we we don't have that.
>> Yeah, thanks for that. Um someone is Anthony is asking um in Python phase do we have Kimba?
Uh she going to take this question first Kimba.
>> Oh no we don't have Kima on Python.
>> Okay that has answered the question. Um, someone is asking, I'm enrolled in the data engineering track but did not receive gradation even though I completed tests and MCQs.
>> Can I get that again? Enrolled in data engineering but did not work.
>> Data enrolled in data engineering track but did not receive gradation email even though I completed tests MCQs.
>> Even though I completed assessment is that what they're saying?
>> Yes. Completed all tests and MCQs.
Okay. So, um it depends on when you um completed and please let's let's try and clarify on this. The graduation requirement is not simply to complete. The graduation requirement is to pass a minimum of 75% of all of those assessments.
Completing it is one thing. Passing it is another. So you got to be sure that you passed at least 75%.
And 75% of I think Python has a total of data analytics which is where you're coming from has a total of 13 assessments.
So you need to have passed at least 10.
So check and confirm that you passed at least 10 assessments. And if you did and you didn't receive an email, it could be that probably you passed it or you met that requirement after the main it could be that you know you you probably met it towards the final deadline of submission which was deadline for submission which was on Friday and so you probably weren't captured at the time those meals were um set automated to go out. Um but if you have going forward, if you have met the graduation requirements at the time the program ended, keep in mind it's another important thing to emphasize the program ended and all submissions ended 11:59 p.m. CAT on Friday. So if you met if you did that over the weekend, it doesn't count during I mean it doesn't count towards graduation.
So just to allay your concerns, if you met that graduation requirement before the final deadline when the graduation was run, then um you will be in subsequent mailing um comes.
>> Thanks. Um question in the chat. Okay.
So so the new content will be unlocked next week. Daniel learning starts next week.
Uh next week Monday actually is that May 4th 4th of May. Next Monday. Um so I think some of our still have concern about data analytics showing it completed.
>> Um okay data analytics showing a computer.
Yes, that's normal. Um I believe every one of you will probably be seeing that as well. Uh and that's why I said in the coming days it will that will be that um that will be updated to completed or graduated as the case may be and then you'll see that your Python is no longer grayed out. So um there's a systematic logic to our systems and so just be patient. All of those logic will play out this within this first couple of days and you should begin to see your current status updated.
>> Okay. Um you can't hear me. Okay. I'll try to speak. Just speak to I'll try to speak up so that you can Can you hear me now? You can hear me now. Um okay. If you have sent a message for congratulating you on your graduation, does that mean you graduated? Yes. Yes, you it means you graduated. If you got a congratulatory email.
Okay. Um, I think we are we're doing the questions in the chat and as well as in Oh, yeah. I think there are more questions.
Uh, how do I know I passed the email? I know I failed one assessment. I did receive a congratulations email. Just not sure if it was automated for you. No. Um, if you received it, it's because you passed.
because we passed the the track to people who actually is learners who actually passed the who met the requirements and the congratulatory will be sent to um will self-paced programs have such support for?
Yes. um self-paced program have same support program support forums as well or have the community um forums.
Um someone is asking does Python course completely cater for Mac users in terms of softwares being used? Mac users were thrown under the bus in the no virtual machine IP addresses were provided to use.
No alternative was presented.
Okay. I don't know if she wants to take >> All right, I can take that. I see boy is responding. So for for Python Mac users, all applications can be effectively used on on Mac. You just need an aonda and your Python environment where you run your Jupyter notebooks. That's all you need for the Python track. So you are all covered whether you're using Windows or Mac for the Python track.
Hey, thanks Sh. Um, all right everyone, these are the questions you can take. If you have more questions, you can send them send a ticket to finish this before. Um, there's going to be an office hour Wednesday for you guys. So, when you have what the questions that you have, you just them.
Uh, okay.
Okay. Um, need to be launched now. So kindly fill in kindly responses.
Okay. Tell me what you think about this orientation session, your feedbacks, what you think and how what you think actually how experience was and yeah.
Okay. Okay. I see someone raising their hand. Is raising their hand.
Hillary, you dropped your hand. I was about to mute you so you can speak.
Okay.
Um right.
So our next steps now is u um if you have not enrolled to the Python app, you know, kindly do that once the button is activated and then navigate the community spaces.
So and where you can access all the spaces we already mentioned the announcement space the program space where you can see where you get to see all recordings and resources for sessions and events.
So all the slides the recordings zoom recording YouTube recordings and everything will be in that particular space also your your squad space particular country you can also reach out to your squad members if you have issues usually when people have issues they get the responses from your spaces you can always squad you have issues ask questions also you can also reach out to your technical mentors if you have technical issues just search for their name and send them a DM and it's strictly for technical issues for learning for Python track related learning issues please also um like I said join us for office hours and Wednesday um um 12 p.m.
I'm going to be answering any questions that you may have that you're about to start this week. Like I've said, this week is where you get to chill. Just try to listen to the track, familiarize yourself with the Python starter kit and all the things that you need to get with Python when eventually start next week.
All right guys, as always your community spaces your fresh days they're there for you if you have issues on your squad on the community spaces if you have a ticket and questions will be attended to okay thank you all very much for um attending this session thank you to um sh techical mentor to Na to Uber and B and every other person.
Thank you all very much.
Have a wonderful day ahead.
[music] [music] I want yellow. [music] You don't like toize all the time. It's horrible for your disguise. It's your demise. You drop [music] one time rush now in a minute. [music] [music] I love myself. Make no [music] jack.
[music] [music] $200 million on my cash from [music] show. [music] Walking in my many many [music] for the boys and one for daddy.
How many pop [music] music? [music] [music] [music] >> [music] >> face [music] when passie [music] [music] of I know [music and singing] the one by that time I don't come on Wicked Daniel.
[music] [music]
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