adiag321/10-Steps-To-Become-A-Data-Scientist

# <p align="center">

📢 10 Steps to Become a Data Scientist </p>
# CLEAR DATA. MADE MODEL.
### 💻💾📓✒📊

1. [Python]
1. [Python Packages]
1. [Mathematics and Linear Algebra]
1. [Programming & Analysis Tools]
1. [Big Data]
1. [Data visualization]
1. [Data Cleaning]
1. [How to solve Problem?]
1. [Machine Learning]
1. [Deep Learning]

# 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)

1. software development

1. mathematics

1. system scripting.

1. Basics

1. Functions

1. Types and Sequences

1. More on Strings

1. Reading and Writing CSV files

1. Dates and Times

1. Objects and map()

1. Lambda and List Comprehensions

1. OOP

for Reading this section **please** fork this kernel:

[numpy-pandas-matplotlib-seaborn-scikit-learn]
# 2-Python Packages

* Numpy

* Pandas

* Matplotlib

* Seaborn
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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

1. Combining Arrays

1. Operations

1. Math Functions

1. Indexing / Slicing

1. Copying Data

1. Iterating Over Arrays

1. The Series Data Structure

1. Querying a Series

### 2-2. Pandas

1. The DataFrame Data Structure

1. Dataframe Indexing and Loading

1. Missing values

1. Merging Dataframes

1. Making Code Pandorable

1. Group by

1. Scales

1. Pivot Tables

1. Date Functionality

1. Distributions in Pandas

1. Hypothesis Testing

1. Matplotlib

1. Scatterplots

1. Line Plots

1. Bar Charts

1. Histograms

1. Box Plots

1. Heatmaps

1. Animations

1. Interactivity

1. DataFrame.plot

### 2-3. seaborn

1. Seaborn Vs Matplotlib

1. Useful Python Data Visualization Libraries

### 2-4. SKlearn

1. Introduction

1. Algorithms

1. Framework

1. Applications

1. Data

1. Supervised Learning: Classification

1. Separate training and testing sets

1. linear, binary classifier

1. Prediction

1. Back to the original three-class problem

1. Evaluating the classifier

1. Using the four flower attributes

1. Unsupervised Learning: Clustering

1. 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:

[Programming & Analysis Tools]

## 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?
The purpose of this section is to solve a few real problem.
so, we have tried to solve some problems such as Quora, Elo, House price prediction.
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]

<img src='https://cdn-images-1.medium.com/max/800/1*dYjDEI0mLpsCOySKUuX1VA.png'>
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# Do You Need Help?

I hope, you have enjoyed reading my python notebooks.

If you have any problem and question to run notebooks please open an issue here in GitHub.

for most of the my notebooks you need **dataset** as input.

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

adiag321 / 10-Steps-To-Become-A-Data-Scientist

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