Roadmap_courses.md

Data Science Circle roadmap 2020

> Constantly adding new content..

To be able to understand the concepts of DS and ML you MUST master statistics and Math.

for data camp courses we have access to it, if you don’t Microsoft Azure tools gives 2 free months,also github student pack give 3 months google how to get it.

This track is divided into 3 phases..

1. Beginner: you get a basic understanding of data analysis, tools and techniques.

2. Intermediate: dive deeper in more complex topics of ML, Math and data engineering.

3. Advanced: where we learn more advanced Math, DL and Deployment.

Let's start...

Beginner

1. Descriptive Stats.
   course
   book
   

2. Probability
   course
   book
   playlist from khan academy
   

3. Python
   basics
   OOP
   more in OOP
   

4. Pandas
   Tutorial
   Docs
   course1
   course2
   

5. Numpy
   Tutorial
   Docs
   

6. Scipy
   Tutorial
   Docs
   

7. Data Cleaning: One of the MOST important skills that you need to master to become a good data scientist, you need to practice on many datasets to master it.
   Read this
   Course 1
   Notebook1
   Notebook2
   Notebook3
   Kaggle Data cleaning
   

8. Data Visualization
   Course1
   Course2
   Course3
   

9. EDA **Note: it's already mentioned in the above probability course
   Course1
   

10. SQL and DB
    Intro
    DB
    

11. Regex
    Tutorial
    

12. Common tools
    Anaconda
    Github
    course
    

At The end of Beginner phase apply all what you've learned on a project.

Intermediate.

This level is a bit longer.

1. Math for ML: consists of Linear Algebra, Calculus and PCA.
   Specialization
   

2. ML
   IBM ML with Python
   Hands on ML book
   ML Algorithms in Practice
   ML scientist
   Project
   

3. Web Scraping&APIs
   course
   intro2
   Tutorial
   book for both topics
   APIs
   Tutorial
   Article
   Tutorial
   

4. Stats.
   This stats. book
   Think Bayes
   

5. Advanced SQL
   course
   joins
   

6. Time Series Analysis
   Track
   Book
   fbprohet
   

7. Feature Engineering
   Tutorial
   Article
   Book
   

8. Interpreting ML models *more to be added here
   SHAP
   Kaggle ML explainability
   

After finishing this level apply to 2 or 3 good sized projects.

Advanced

This section is subjected to A lot of editing as I haven't finished it yet..

1. Deep Learning
   Specialization
   Book
   github of Dive into DL

2. Tensorflow..
   Specialization
   

3. Advanced Data Science+ apache spark
   Specialization
   

4. NLP
   Specialization
   

5. Inferential stats
   Specialization, 2nd & 3rd courses
   course
   

6. Bayesian stats.
   1
   2
   3
   

7. Tableau
   Tutorial
   docs
   course
   

8. Model Deployment
   Flask tutorial
   Specialization with TF
   Guided project
   

more to be added here..

10. Probabilistic Graphical Models
    Specialization
    

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...MORE yet to come in this section..

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