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I finished a 28 hours Machine learning with python (Basic course) on Udemy, and it was very beneficial.

My aim, is to be able to understand what is ML and how to use its concepts while working with data.

I am confused about where to continue. My goal is to be a data engineer/analyst or at least data engineer.

I am searching for courses on coursera, because of its credibility.

I found a course from University of Michigan: Applied data science with Python.

But I am confused if I should take something else in order to start my data science job search. I know it is going to be a long process to finish.

Any suggestions on how to proceed in this journey as I am really confused.

Feel free to remove my question if it was not supposed to be asked here.

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Dataquest is a great place to start. It will help you bridge the gap from just learning to actually coding as a data analyst, data engineer, or data scientist. Unlike most online courses, there are no videos, and they provide an interactive coding and learning environment that makes it very easy to learn the practical techniques you'll actually need to work on real-world data and problems.

The best part about Dataquest is their guided projects that are part of every topic you learn. If you want to eventually get a job as a data analyst, scientist, or engineer, you are going to need a portfolio of projects to show what you know, and Dataquest helps guide you through the entire process.

fast.ai is another great place to learn about machine learning, and deep learning in particular, all for free. They teach using a top-down approach that will get you working on your own projects and data in no time.

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  • $\begingroup$ Dataquest is really expensive for me. Fast.ai is still a long term project for me. First of all, I need to refine my knowledge in both Python and Data Science Technics. Thanks for the answer. $\endgroup$ – alim1990 Jul 25 at 6:38
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There a three dimensions of knowledge you need to be a generally competent data scientist:

  1. Statistical / Mathematical knowledge

  2. Programming / CS skills

  3. Domain knowledge

I think studying and courses help in 1) and to a degree in 2) but not at all in 3. If you feel confident enough in 1/2 that you can solve some real life cases it would be best to start doing projects (Kaggle, your own projects, etc.).

Otherwise you should focus on applied courses that combine 1 & 2 in a target oriented way. I am very partial to the Kaggle micro-courses because they are small, practical and fast but you need some basis to do these.

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  • $\begingroup$ I worked on 2 kaggle competitions (house pricing and titanic), and my results was between top 20%. I tried with several models and even made ensemble modelling. So where should I continue sir? Kaggle mini courses, or just getting my own data and start the real work. $\endgroup$ – alim1990 Jul 20 at 8:18
  • $\begingroup$ The two competitions you named are two of the most basic competitions involving mainly core ML concepts. Kaggle has several competitions (and datasets) to encourage you to try out new challenges like CV, outlier detection, time series, etc. Try to find the kinds of challenges you'd want to solve in your job later on. $\endgroup$ – Fnguyen Jul 20 at 8:25
  • $\begingroup$ What about taking the Predict Future Sales competition on kaggle? $\endgroup$ – alim1990 Jul 25 at 6:55
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First of all, Congrats that you have started your journey of becoming a Data engineer/analyst. According to me there is no clear path on how to become a Data analyst.

Applied data science with Python course is great but i would urge you to start working on problem statement of any domain (NLP,CV) and take part in competition where you will learn how to visualise data and work with large amount of dataset which are key requirement on becoming Data engineer/analyst. In this process you will anyway get to know what you need to learn and apply to solve the problem statement.

Remember this will be a long journey so keep hustling!

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  • $\begingroup$ I worked on 2 kaggle competitions (house pricing and titanic), and my results was between top 20%. I tried with several models and even made ensemble modelling. So where should I continue sir? Kaggle mini courses, or just getting my own data and start the real work. $\endgroup$ – alim1990 Jul 20 at 8:16
  • $\begingroup$ These two datasets are very common. You should try working on different dataset and refer Kaggle mini courses if needed. $\endgroup$ – prashant0598 Jul 20 at 8:22

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