Bros na wetin I do o. Started teaching myself in February. I applied to every shit. Just started job hunting two weeks ago and got my first offer today. My own na web development sha.
Congrats brother, this is big news. Meanwhile, "I applied to every shit."
We get it! You are a bookworm, you can't help it, we understand, but be careful with your advice. Ejiod already explained the process of becoming a data scientist.
How? By FIRST becoming a data analyst. You do not need to understand the intricacies of advanced statistics (or ML) to become a data analyst. What you need is
1. basic mathematics (statistics),
2. basic intuition of how ML algos work(they've been written in packages already for your usage). Kiril eremenko did a great job on that on udemy. Just get the course for free and learn the intuition with how and when to use it.
3. Excel,PowerBI(because it's cheaper for companies to integrate as most companies rely on Microsoft products and it's simply advanced excel) and or Tableau (or any other visualization tools). Tableau is a bit expensive though
4. SQL (T-Sql/MySQL/P-SQL) they are all 90% the same. Learn MySQL and the rest will become a breeze!
5. A programming language (Python/R/Julia, etc). You don't need to know all the functions of the language, just learn the basics and the necessary libraries for now (trust me, there'll be plenty of time to dive deeper into these languages later!)
After you're pretty solid on the above, start applying for jobs (junior data analyst or Business Intelligence Analyst).
Or if you want a higher pay, Immerse yourself in MSSQL (Data warehouse tools) I.e Learn
T-SQL (Microsoft customized SQL)
SSRS (pretty deprecated, but many companies still use it for reporting. To be frank, modern visuals like PBI, Tableau are taking over.dont spend too much of your time here)
SSIS -makes your life easier, as it reduces the amount of dml you need write. It's very easy.
SSAS - same.
Spend your time learning the above and you'll begin to earn a lot.
Because if you spend your time going though the Forever learning path, you're gonna be a jack of all trades yet a master of none.
If you need to feed, get a job with excel,powerBI/tableau,SQL,Python/R then learn the remain (Statistics, ML-DL) on the job as required.
Else, you go HUNGRY
This is barely my 3rd month into data analysis and I've gotten some interviews already... No time to waste, as you need to survive...
Yes..but they speak english tho So it"s still ok... Try tutorialspoint....indian too...but English is ok
It's not about the language, I don't just rate their tutorials save a few and the ones I saw on YouTube don't impress me. Will try your suggestion, Thanks.
I'm kinda having a difficulty with Bayes Theorem. After spending hours to understand I decided to practice what I had learnt.
Abeg our mathematicians and staticians in the house coman help me ooo
This is an interesting question, and this is how I'd do it.
Prior probability that the woman has cancer is P(A) = 0.01 Let event A be that the woman has cancer Let event B be that the woman gets a positive mammogram test So, we are looking for the probability that the woman has cancer given that she gets a positive mammogram test, i.e P(A|B)
Using Bayes theorem, P(A|B) = (P(B|A) * P(A))/(P(B)) We know the prior, which is P(A). P(B) can be expressed as P(B|A) * P(A) + P(B|not A) * P(not A) P(not A) = 1 - P(A) = 0.99 We take P(A|B) to be 0.75, which is what the physicians claimed, i.e. the probability that the woman has cancer given that she has a positive mammogram test.
P(B|A) is the probability that she gets a positive mammogram test, given that she has cancer. That probability is 0.8, since the mammogram accurately classifies 80% of cases.
If you substitute everything into the equation, you get P(B|not A) = 0.27%. This means the probability that the woman gets a positive mammogram result given that she does not have cancer is 0.27%. This is the size of the false positives and honestly is too low. This would suggest that the test is incredibly accurate, which as we can see from the figures of 80% and 90%, is not the case. So, it is very unlikely that the 75% from the physicians is correct. I suspect the "benign" case is the complement of the "cancerous" case, though it's not clear. If that's the case, then the probability of getting a negative test given that she does not have cancer (it's benign) is P(not B|not A) = 0.90, so that P(B| not A) = 1-0.90=0.1 or 10%. This is more likely the value of your false positives. If we compare with our earlier 2.7%, we see it is much higher. With this we can confirm that the physicians obviously are not right in their diagnosis.
This is an interesting question, and this is how I'd do it.
Prior probability that the woman has cancer is P(A) = 0.01 Let event A be that the woman has cancer Let event B be that the woman gets a positive mammogram test So, we are looking for the probability that the woman has cancer given that she gets a positive mammogram test, i.e P(A|B)
Using Bayes theorem, P(A|B) = (P(B|A) * P(A))/(P(B)) We know the prior, which is P(A). P(B) can be expressed as P(B|A) * P(A) + P(B|not A) * P(not A) P(not A) = 1 - P(A) = 0.99 We take P(A|B) to be 0.75, which is what the physicians claimed, i.e. the probability that the woman has cancer given that she has a positive mammogram test.
P(B|A) is the probability that she gets a positive mammogram test, given that she has cancer. That probability is 0.8, since the mammogram accurately classifies 80% of cases.
If you substitute everything into the equation, you get P(B|not A) = 0.27%. This means the probability that the woman gets a positive mammogram result given that she does not have cancer is 0.27%. This is the size of the false positives and honestly is too low. This would suggest that the test is incredibly accurate, which as we can see from the figures of 80% and 90%, is not the case. So, it is very unlikely that the 75% from the physicians is correct. I suspect the "benign" case is the complement of the "cancerous" case, though it's not clear. If that's the case, then the probability of getting a negative test given that she does not have cancer (it's benign) is P(not B|not A) = 0.90, so that P(B| not A) = 1-0.90=0.1 or 10%. This is more likely the value of your false positives. If we compare with our earlier 2.7%, we see it is much higher. With this we can confirm that the physicians obviously are not right in their diagnosis.
Or if you want a higher pay, Immerse yourself in MSSQL (Data warehouse tools) I.e Learn
T-SQL (Microsoft customized SQL)
SSRS (pretty deprecated, but many companies still use it for reporting. To be frank, modern visuals like PBI, Tableau are taking over.dont spend too much of your time here)
SSIS -makes your life easier, as it reduces the amount of dml you need write. It's very easy.
SSAS - same.
Spend your time learning the above and you'll begin to earn a lot.
Because if you spend your time going though the Forever learning path, you're gonna be a jack of all trades yet a master of none.
If you need to feed, get a job with excel,powerBI/tableau,SQL,Python/R then learn the remain (Statistics, ML-DL) on the job as required.
Else, you go HUNGRY
This is barely my [b]3rd month into data analysis and I've gotten some interviews already... No time to waste, as you need to survive... [/b] My 2cents
This is awesome.
That means you must have a solid CV. Can you share a template for a data science CV?
I believe it is the first step to getting a Data Science job after all the learning a good CV will help too.
We get it! You are a bookworm, you can't help it, we understand, but be careful with your advice. Ejiod already explained the process of becoming a data scientist.
How? By FIRST becoming a data analyst. You do not need to understand the intricacies of advanced statistics (or ML) to become a data analyst. What you need is
1. basic mathematics (statistics),
2. basic intuition of how ML algos work(they've been written in packages already for your usage). Kiril eremenko did a great job on that on udemy. Just get the course for free and learn the intuition with how and when to use it.
3. Excel,PowerBI(because it's cheaper for companies to integrate as most companies rely on Microsoft products and it's simply advanced excel) and or Tableau (or any other visualization tools). Tableau is a bit expensive though
4. SQL (T-Sql/MySQL/P-SQL) they are all 90% the same. Learn MySQL and the rest will become a breeze!
5. A programming language (Python/R/Julia, etc). You don't need to know all the functions of the language, just learn the basics and the necessary libraries for now (trust me, there'll be plenty of time to dive deeper into these languages later!)
After you're pretty solid on the above, start applying for jobs (junior data analyst or Business Intelligence Analyst).
Or if you want a higher pay, Immerse yourself in MSSQL (Data warehouse tools) I.e Learn
T-SQL (Microsoft customized SQL)
SSRS (pretty deprecated, but many companies still use it for reporting. To be frank, modern visuals like PBI, Tableau are taking over.dont spend too much of your time here)
SSIS -makes your life easier, as it reduces the amount of dml you need write. It's very easy.
SSAS - same.
Spend your time learning the above and you'll begin to earn a lot.
Because if you spend your time going though the Forever learning path, you're gonna be a jack of all trades yet a master of none.
If you need to feed, get a job with excel,powerBI/tableau,SQL,Python/R then learn the remain (Statistics, ML-DL) on the job as required.
Else, you go HUNGRY
This is barely my 3rd month into data analysis and I've gotten some interviews already... No time to waste, as you need to survive...
Bros na wetin I do o. Started teaching myself in February. I applied to every shit. Just started job hunting two weeks ago and got my first offer today. My own na web development sha.
Please for web development what resources/materials did you use?
mcemmy0z: If you reside around songo Ota I have these available *Udemy - Beginner to Pro in Excel Financial Modeling and Valuation *Udemy - SQL - MySQL for Data Analytics and Business Intelligence *Tableau 10 A-Z Hands-On Tableau Training For Data Science! *Tableau Hands-on Learn Data Visualization with Tableau *Udemy - Power BI A-Z Hands-On Power BI Training For Data Science *Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science *Udemy - Python for Financial Analysis and Algorithmic Trading
Please bro I already sent you a Dm please check thanks
It's funny how you listed all of these for people to learn while you have problem with the little I listed. And you do not get it that I am a bookworm cos you don't know whether I am or not.
Someone asked about ML and I explained to him how he can start up with that. You don't even know whether he/she can do analysis already, or interested in the "sharp sharp" you are advising.
Actually, the intro to statistics can help him greatly in analysis. You should never advise someone to dim his/her curiosity no matter what. If he/she is curious about anything, direct him to the right path. He/she has already seen what is needed for data analysis and not here saying ML doesn't make sense in Nigeria.
The interview you attended, if someone else interviewed for that position and has the same skills as yours and maybe has a knowledge of regression analysis, who do you think will be picked?
The ML you are talking about that doesn't make sense, is it not with the same knowledge that some Nigerians are winning some money on Zindi Africa? Zindi Africa is supposed to be for Africans but guess the country that is winning mostly there?....
Not everybody here is sitting at home hustling for data science and analytics jobs. Some have good jobs and want to transition, and it will be really good if they have a very good understanding and be really good at it.
I became very good with Python because I always download and go through notebooks on Kaggle. Why did I go to Kaggle on the first place? Because I was curious about ML. Nobody can do ML without processing the data first and you get to see notebooks that are just for analysis.
I couldn't learn Tableau even after watching videos till I went back to statistics to really understand about data. I remembered asking here which videos I can watch to really understand it. Do you also think that analysis is just about dragging and dropping and drawing charts? Won't you recommend or do some predictions for the company based on what you understood from the data given to you.
Oh, before I submit, let me also said that curiosity about ML also led me to learn about softwares too. I can build websites also. While I have come up with a portfolio on data science and applying for the jobs, I am also building a web project to apply for front-end web developer. Yes, I was curious about ML, and it has led me to acquire more than what I expected.
ML Engineers are also available remotely. You should be curious enough to learn what is relevant beyond your location. Upwork and Fiverr are all global. Someone has brought two questions here from upwork and we were just confounded. A work on excel actually, and we couldn't get it. All of them are available online. The day I watched Edureka 4hrs video on excel, I was dumbfounded to know so many things I can do with excel. You can build bots using excel, finance goal seeks, VBAs, Macros...
We are still here talking charts and functions and still asking what excel can do that pandas can't do. If someone says anything that is foreign here, we try to shut down the person with "it's not applicable in Nigeria bla bla bla". Do you even know the worst thing about us youth in Naija? We are even ready to explore or think beyond our locality, and the worst of the worst? We try to drag others along with us to stay myopic as well, that is why we are where we are today.
We get it! You are a bookworm, you can't help it, we understand, but be careful with your advice. Ejiod already explained the process of becoming a data scientist.
How? By FIRST becoming a data analyst. You do not need to understand the intricacies of advanced statistics (or ML) to become a data analyst. What you need is
1. basic mathematics (statistics),
2. basic intuition of how ML algos work(they've been written in packages already for your usage). Kiril eremenko did a great job on that on udemy. Just get the course for free and learn the intuition with how and when to use it.
3. Excel,PowerBI(because it's cheaper for companies to integrate as most companies rely on Microsoft products and it's simply advanced excel) and or Tableau (or any other visualization tools). Tableau is a bit expensive though
4. SQL (T-Sql/MySQL/P-SQL) they are all 90% the same. Learn MySQL and the rest will become a breeze!
5. A programming language (Python/R/Julia, etc). You don't need to know all the functions of the language, just learn the basics and the necessary libraries for now (trust me, there'll be plenty of time to dive deeper into these languages later!)
After you're pretty solid on the above, start applying for jobs (junior data analyst or Business Intelligence Analyst).
Or if you want a higher pay, Immerse yourself in MSSQL (Data warehouse tools) I.e Learn
T-SQL (Microsoft customized SQL)
SSRS (pretty deprecated, but many companies still use it for reporting. To be frank, modern visuals like PBI, Tableau are taking over.dont spend too much of your time here)
SSIS -makes your life easier, as it reduces the amount of dml you need write. It's very easy.
SSAS - same.
Spend your time learning the above and you'll begin to earn a lot.
Because if you spend your time going though the Forever learning path, you're gonna be a jack of all trades yet a master of none.
If you need to feed, get a job with excel,powerBI/tableau,SQL,Python/R then learn the remain (Statistics, ML-DL) on the job as required.
Else, you go HUNGRY
This is barely my 3rd month into data analysis and I've gotten some interviews already... No time to waste, as you need to survive...
Oddy16: It's funny how you listed all of these for people to learn while you have problem with the little I listed. And you do not get it that I am a bookworm cos you don't know whether I am or not.
Someone asked about ML and I explained to him how he can start up with that. You don't even know whether he/she can do analysis already, or interested in the "sharp sharp" you are advising.
Actually, the intro to statistics can help him greatly in analysis. You should never advise someone to dim his/her curiosity no matter what. If he/she is curious about anything, direct him to the right path. He/she has already seen what is needed for data analysis and not here saying ML doesn't make sense in Nigeria.
The interview you attended, if someone else interviewed for that position and has the same skills as yours and maybe has a knowledge of regression analysis, who do you think will be picked?
The ML you are talking about that doesn't make sense, is it not with the same knowledge that some Nigerians are winning some money on Zindi Africa? Zindi Africa is supposed to be for Africans but guess the country that is winning mostly there?....
Not everybody here is sitting at home hustling for data science and analytics jobs. Some have good jobs and want to transition, and it will be really good if they have a very good understanding and be really good at it.
I became very good with Python because I always download and go through notebooks on Kaggle. Why did I go to Kaggle on the first place? Because I was curious about ML. Nobody can do ML without processing the data first and you get to see notebooks that are just for analysis.
I couldn't learn Tableau even after watching videos till I went back to statistics to really understand about data. I remembered asking here which videos I can watch to really understand it. Do you also think that analysis is just about dragging and dropping and drawing charts? Won't you recommend or do some predictions for the company based on what you understood from the data given to you.
Oh, before I submit, let me also said that curiosity about ML also led me to learn about softwares too. I can build websites also. While I have come up with a portfolio on data science and applying for the jobs, I am also building a web project to apply for front-end web developer. Yes, I was curious about ML, and it has led me to acquire more than what I expected.
ML Engineers are also available remotely. You should be curious enough to learn what is relevant beyond your location. Upwork and Fiverr are all global. Someone has brought two questions here from upwork and we were just confounded. A work on excel actually, and we couldn't get it. All of them are available online. The day I watched Edureka 4hrs video on excel, I was dumbfounded to know so many things I can do with excel. You can build bots using excel, finance goal seeks, VBAs, Macros...
We are still here talking charts and functions and still asking what excel can do that pandas can't do. If someone says anything that is foreign here, we try to shut down the person with "it's not applicable in Nigeria bla bla bla". Do you even know the worst thing about us youth in Naija? We are even ready to explore or think beyond our locality, and the worst of the worst? We try to drag others along with us to stay myopic as well, that is why we are where we are today.
Shalom!
Nice submission but you have to take it easy boss. I have a feeling that we have some things in common. When I started with Python last year September, my goal was just to do data analysis. When I 'finished' mastering the fundamentals of descriptive and inferential statistics, I said to myself; this can't be the end of it so I started learning about ML, then DL and I became obsessed with CV and NLP. After getting a solid understanding of DL, I said to myself; this can't be the end so I made some research and found out how ML/DL models can be turned to web/mobile apps so I started learning web dev (backend and fronend) till I gained proficiency in it and started building my portfolio. My next target is to start contributing to open source projects (most especially, NLP).
I think for anyone to be come a master (I mean not working for someone your entire life) in this tech space, one has to be curious and be innovative at the same time.
For anyone that wants to go deep into DS, don't listen to anyone telling you to focus on some aspects and ignore some. Get a standard learning path (check Analytics Vidhiya) and stay focused while you climb the ladder.
Between, could you please quote me with the link to that Edureka video on excel?. I don't really like their courses but let me try this one since you found it interesting. Thanks!
You may still not find it interesting because they were a bit fast in the video. I think they were trying to cover up as much topics as possible. What I normally do is to write down the topics and summary of what it is all about, then search YouTube for where they digested any topic I want to learn well enough.
Nice submission but you have to take it easy boss. I have a feeling that we have some things in common. When I started with Python last year September, my goal was just to do data analysis. When I 'finished' mastering the fundamentals of descriptive and inferential statistics, I said to myself; this can't be the end of it so I started learning about ML, then DL and I became obsessed with CV and NLP. After getting a solid understanding of DL, I said to myself; this can't be the end so I made some research and found out how ML/DL models can be turned to web/mobile apps so I started learning web dev (backend and fronend) till I gained proficiency in it and started building my portfolio. My next target is to start contributing to open source projects (most especially, NLP).
I think for anyone to be come a master (I mean not working for someone your entire life) in this tech space, one has to be curious and be innovative at the same time.
For anyone that wants to go deep into DS, don't listen to anyone telling you to focus on some aspects and ignore some. Get a standard learning path (check Analytics Vidhiya) and stay focused while you climb the ladder.
Between, could you please quote me with the link to that Edureka video on excel?. I don't really like their courses but let me try this one since you found it interesting. Thanks!
Oddy16: You may still not find it interesting because they were a bit fast in the video. I think they were trying to cover up as much topics as possible. What I normally do is to write down the topics and summary of what it is all about, then search YouTube for where they digested any topic I want to learn well enough.
VBA and Macros are advanced topics in excel. You may have to search for it on YouTube. It's mostly about automation. Excel is so so underrated. I don't think that an Excel expert, I mean real expert, will ever go hungry anywhere. There are so much you can do with that tool.
Nice submission but you have to take it easy boss. I have a feeling that we have some things in common. When I started with Python last year September, my goal was just to do data analysis. When I 'finished' mastering the fundamentals of descriptive and inferential statistics, I said to myself; this can't be the end of it so I started learning about ML, then DL and I became obsessed with CV and NLP. After getting a solid understanding of DL, I said to myself; this can't be the end so I made some research and found out how ML/DL models can be turned to web/mobile apps so I started learning web dev (backend and fronend) till I gained proficiency in it and started building my portfolio. My next target is to start contributing to open source projects (most especially, NLP).
I think for anyone to be come a master (I mean not working for someone your entire life) in this tech space, one has to be curious and be innovative at the same time.
For anyone that wants to go deep into DS, don't listen to anyone telling you to focus on some aspects and ignore some. Get a standard learning path (check Analytics Vidhiya) and stay focused while you climb the ladder.
Between, could you please quote me with the link to that Edureka video on excel?. I don't really like their courses but let me try this one since you found it interesting. Thanks!
Been reading lately previous 2 pages and seeing those post my heart bleeds. Aim for creating this thread was to encourage young ones, enthusiasts and undergraduate trying to switch. Aim is to Encourage Been hearing debates about irrelevance of ML/DL and so many stuffs.. yeah it may be nascent but never useless. Let’s all encourage those in this line whether ML or analytics. Sincerely what sets you apart is your Python and ML/DL skills. As time goes on you will be hearing Full Stack Data scientist. This time around you will combine your web skills with data science.It’s very important you Learn Unlearn and Relearn.Whatever you find yourself doing do it zealously... Determination, hardwork and smartWork matters. Those on ML/DL path,keep Learning and learning. Once you are done,try Analytics. Learn and use analytic tools. Just reminder. I entered Data science by learning and doing ML/DL. Infact God & Python were what got me to where I am today. I had to immerse myself into PowerBI and Tableau by learning more. Any tool that’s linked to data I had to learn. That was the real deal. Yes Nigeria is not ripe for ML/DL but learn it. Along side ML. Learn analytics tool too because here in Nigeria Data analytics gets more opening than Data science. If you are more of freelance and looking at foreign prospect immerse yourself in ML/DL..
Nice submission but you have to take it easy boss. I have a feeling that we have some things in common. When I started with Python last year September, my goal was just to do data analysis. When I 'finished' mastering the fundamentals of descriptive and inferential statistics, I said to myself; this can't be the end of it so I started learning about ML, then DL and I became obsessed with CV and NLP. After getting a solid understanding of DL, I said to myself; this can't be the end so I made some research and found out how ML/DL models can be turned to web/mobile apps so I started learning web dev (backend and fronend) till I gained proficiency in it and started building my portfolio. My next target is to start contributing to open source projects (most especially, NLP).
I think for anyone to be come a master (I mean not working for someone your entire life) in this tech space, one has to be curious and be innovative at the same time.
For anyone that wants to go deep into DS, don't listen to anyone telling you to focus on some aspects and ignore some. Get a standard learning path (check Analytics Vidhiya) and stay focused while you climb the ladder.
Between, could you please quote me with the link to that Edureka video on excel?. I don't really like their courses but let me try this one since you found it interesting. Thanks!
Evening boss. Please, how can ML/DL models be turned to web/mobile apps? Do you have any ebooks or video on it?
Evening boss. Please, how can ML/DL models be turned to web/mobile apps? Do you have any ebooks or video on it?
Evening! For web applications, you have to learn a Python framework (Flask is relatively easy compared to Django). After that, read up Pickling. If you don't know how to start, just google how to deploy ML models using Flask and Heroku. There are many videos on YouTube.
Note: You don't have to master the key concepts of any framework to have your models running on the web. With less than 100 lines of code, you can deploy your models.
You may as well use JavaScript if you are familiar with it.
Evening! For web applications, you have to learn a Python framework (Flask is relatively easy compared to Django). After that, read up Pickling. If you don't know how to start, just google how to deploy ML models using Flask and Heroku. There are many videos on YouTube.
Note: You don't have to master the key concepts of any framework to have your models running on the web. With less than 100 lines of code, you can deploy your models.
You may as well use JavaScript if you are familiar with it.
Thank you. I'll do as you've advised. I'm actually a web developer, so I just need to see an example. I appreciate
Oddy16: It's funny how you listed all of these for people to learn while you have problem with the little I listed. And you do not get it that I am a bookworm cos you don't know whether I am or not.
Someone asked about ML and I explained to him how he can start up with that. You don't even know whether he/she can do analysis already, or interested in the "sharp sharp" you are advising.
Actually, the intro to statistics can help him greatly in analysis. You should never advise someone to dim his/her curiosity no matter what. If he/she is curious about anything, direct him to the right path. He/she has already seen what is needed for data analysis and not here saying ML doesn't make sense in Nigeria.
The interview you attended, if someone else interviewed for that position and has the same skills as yours and maybe has a knowledge of regression analysis, who do you think will be picked?
The ML you are talking about that doesn't make sense, is it not with the same knowledge that some Nigerians are winning some money on Zindi Africa? Zindi Africa is supposed to be for Africans but guess the country that is winning mostly there?....
Not everybody here is sitting at home hustling for data science and analytics jobs. Some have good jobs and want to transition, and it will be really good if they have a very good understanding and be really good at it.
I became very good with Python because I always download and go through notebooks on Kaggle. Why did I go to Kaggle on the first place? Because I was curious about ML. Nobody can do ML without processing the data first and you get to see notebooks that are just for analysis.
I couldn't learn Tableau even after watching videos till I went back to statistics to really understand about data. I remembered asking here which videos I can watch to really understand it. Do you also think that analysis is just about dragging and dropping and drawing charts? Won't you recommend or do some predictions for the company based on what you understood from the data given to you.
Oh, before I submit, let me also said that curiosity about ML also led me to learn about softwares too. I can build websites also. While I have come up with a portfolio on data science and applying for the jobs, I am also building a web project to apply for front-end web developer. Yes, I was curious about ML, and it has led me to acquire more than what I expected.
ML Engineers are also available remotely. You should be curious enough to learn what is relevant beyond your location. Upwork and Fiverr are all global. Someone has brought two questions here from upwork and we were just confounded. A work on excel actually, and we couldn't get it. All of them are available online. The day I watched Edureka 4hrs video on excel, I was dumbfounded to know so many things I can do with excel. You can build bots using excel, finance goal seeks, VBAs, Macros...
We are still here talking charts and functions and still asking what excel can do that pandas can't do. If someone says anything that is foreign here, we try to shut down the person with "it's not applicable in Nigeria bla bla bla". Do you even know the worst thing about us youth in Naija? We are even ready to explore or think beyond our locality, and the worst of the worst? We try to drag others along with us to stay myopic as well, that is why we are where we are today.
Shalom!
Far from it bro. I never meant to shut you down.
Not everyone has the ability to learn the way you do. Some learn by experience.
Many persons learn but forget easily when trying to learn the next framework.
I felt the easiest way into DS is to learn enough to get you an internship (data analyst) before delving deeper into DS (I mean you have the remaining of your sweet life for that).
We are all DS enthusiast but we need to be REALISTIC. DA jobs are more in their numbers than DS jobs (at least in Nigeria). And you most likely would not start your career as a data scientist. Data science role is more demanding than the former. But what do I know.
P.S what I listed aren't half as difficult as what you listed
