10 Steps to Become a Data Scientist

Table of Contents

1. Python
2. Python Packages
3. Mathematics and Linear Algebra
4. Programming & Analysis Tools
5. Big Data
6. Data visualization
7. Data Cleaning
8. How to solve Problem?
9. Machine Learning
10. Deep Learning

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Introduction

If you Read and Follow Job Ads to hire a machine learning expert or a data scientist, you find that some skills you should have to get the job. In this Repository, I want to review 10 skills that are essentials to get the job

In fact, this Repository is a reference for 10 other Notebooks, which you can learn with them, all of the skills that you need.

1-Python

Python is a modern, robust, high level programming language. It is very easy to pick up even if you are completely new to programming.

You can read and learn following topic on this Notebook:

1. web development (server-side)

2. software development

3. mathematics

4. system scripting.

5. Basics

6. Functions

7. Types and Sequences

8. More on Strings

9. Reading and Writing CSV files

10. Dates and Times

11. Objects and map()

12. Lambda and List Comprehensions

13. OOP

for Reading this section please fork this kernel:

numpy-pandas-matplotlib-seaborn-scikit-learn

2-Python Packages

• Numpy

• Pandas

• Matplotlib

• Seaborn

In this Step, we have a comprehensive tutorials for Five packages in python after that you can start reading my other kernels about machine learning and deep learning.

2-1. Numpy

1. Creating Arrays

2. Combining Arrays

3. Operations

4. Math Functions

5. Indexing / Slicing

6. Copying Data

7. Iterating Over Arrays

8. The Series Data Structure

9. Querying a Series

2-2. Pandas

1. The DataFrame Data Structure

2. Dataframe Indexing and Loading

3. Missing values

4. Merging Dataframes

5. Making Code Pandorable

6. Group by

7. Scales

8. Pivot Tables

9. Date Functionality

10. Distributions in Pandas

11. Hypothesis Testing

12. Matplotlib

13. Scatterplots

14. Line Plots

15. Bar Charts

16. Histograms

17. Box Plots

18. Heatmaps

19. Animations

20. Interactivity

21. DataFrame.plot

2-3. seaborn

1. Seaborn Vs Matplotlib

2. Useful Python Data Visualization Libraries

2-4. SKlearn

1. Introduction

2. Algorithms

3. Framework

4. Applications

5. Data

6. Supervised Learning: Classification

7. Separate training and testing sets

8. linear, binary classifier

9. Prediction

10. Back to the original three-class problem

11. Evaluating the classifier

12. Using the four flower attributes

13. Unsupervised Learning: Clustering

14. Supervised Learning: Regression

for Reading this section please fork this kernel:

numpy-pandas-matplotlib-seaborn-scikit-learn

3- Mathematics and Linear Algebra

for Reading this section please fork this kernel:

Linear Algebra in 60 Minutes

4- Programming & Analysis Tools

for Reading this section please fork and upvote this kernel:

Linear Algebra in 60 Minutes

5- Big Data

for Reading this section please fork this kernel:

A-Comprehensive-Deep-Learning-Workflow-with-Python

6- Data Visualization

for Reading this section please fork this kernel:

1. Data visualization

7- Data Cleaning

for Reading this section please fork this kernel:

Data Cleaning

8- How to solve Problem?

for Reading this section please fork this kernel:

A-Comprehensive-Deep-Learning-Workflow-with-Python

9- Machine learning

for Reading this section please fork this kernel:

A Comprehensive ML Workflow with Python

10- Deep Learning

for Reading this section please fork this kernel:

A-Comprehensive-Deep-Learning-Workflow-with-Python

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Help

I hope you have enjoyed reading my python notebook.

If you have any problem to run notebook please open an issue here in github.

for most of the my notebook you need dataset as input.

To use the correct data, please download the dat set from the Kaggle site and put it in your notebook folder.

Mj Bhamnai

[email protected]

Have Fun!

1. Follow me On GitHub

2. Follow me On Kaggle