## Python basics Learn Python for Data Science Python For Machine learning and Python Tips and tricks

☑ Uderstand the basics of python programming

☑ learning all the basic mathematical concepts

☑ Understand the basics of Data science and how to perform it using Python

☑ Learn to use different python tools specialisez for data science

☑ Improve your python programming by integrating new concepts

☑ Learning the basics of Machine learning

☑ Perform various analysis with sklearn

☑ Finish the course with a complete understand of all the core concepts of Data science and all the required tools to perform it with python

Learning how to program in Python is not always easy especially if you want to use it for Data science. Indeed, there are many of different tools that have to be learned to be able to properly use Python for Data science and machine learning and each of those tools is not always easy to learn. But, this course will give all the basics you need no matter for what objective you want to use it so if you :

– Are a student and want to improve your programming skills and want to learn new utilities on how to use Python

– Need to learn basics of Data science

– Have to understand basic Data science tools to improve your career

– Simply acquire the skills for personal use

Then you will definitely love this course. Not only you will learn all the tools that are used for Data science but you will also improve your Python knowledge and learn to use those tools to be able to visualize your projects.

**The structure of the course**

This course is structured in a way that you will be able to to learn each tool separately and practice by programming in python directly with the use of those tools. Indeed, you will at first learn all the mathematics that are associated with Data science. This means that you will have a complete introduction to the majority of important statistical formulas and functions that exist. You will also learn how to set up and use Jupyter as well as Pycharm to write your Python code. After, you are going to learn different Python libraries that exist and how to use them properly. Here you will learn tools such as NumPy or SciPy and many others. Finally, you will have an introduction to machine learning and learn how a machine learning algorithm works. All this in just one course.

Another very interesting thing about this course it contains a lot of practice. Indeed, I build all my course on a concept of learning by practice. In other words, this course contains a lot of practice this way you will be able to be sure that you completely understand each concept by writing the code yourself.

**For who is this course designed**

This course is designed for beginner that are interested to have a basic understand of what exactly Data science is and be able to perform it with python programming language. Since this is an introduction to Data science, you don’t have to be a specialist to understand the course. Of course having some basic prior python knowledge could be good but it’s not mandatory to be able to understand this course. Also, if you are a student and wish to learn more about Data science or you simply want to improve your python programming skills by learning new tools you will definitely enjoy this course. Finally, this course is for any body that is interested to learn more about Data science and how to properly use python to be able to analyze data with different tools.

**Why should I take this course**

If you want to learn all the basics of Data science and Python this course has all you need. Not only you will have a complete introduction to Data science but you will also be able to practice python programming in the same course. Indeed, this course is created to help you learn new skills as well as improving your current programming skills.

**There is no risk involved in taking this course**

This course comes with a 100% satisfaction guarantee, this means that if your are not happy with what you have learned, you have **30 days **to get a complete refund with no questions asked. Also, if there is any concept that you find complicated or you are just not able to understand, you can directly contact me and it will be my pleasure to support you in your learning.

This means that you can either learn amazing skills that can be very useful in your professional or everyday life or you can simply try the course and if you don’t like it for any reason ask for a refund.

**You can’t lose with this type of offer !!**

**ENROLL NOW and start learning today 🙂**

## English

Language

Introduction

Introduction

What is Data Science

Installation of Anaconda and Jupyter

Introduction to Jupyter Part 1

Introduction to Jupyter Part 2

Basic Statstics knowledge

The Basics of Data

The basics of statistics part 1

The basics of statistics part 2

The basics of statistics part 3

The basics of statistics part 4

The basics of statistics part 5

The basics of statistics part 6

Python library: NumPy

Introduction to Numpy

Setting up NumPy

Basic calculations Part 1

Basic calculations Part 2

Basic calculations Part 3

Basic calculations Part 4

Basic calculations Part 5

Python library: Pandas

The Basics of Pandas

Setting up Pandas

Pandas operations part 1

Pandas operations part 2

Pandas operations part 3

Pandas operations part 4

Pandas operations part 5

Python library: Scipy

The Basics of SciPy

SciPy operations part 1

SciPy operations part 2

SciPy operations part 3

SciPy operations part 4

SciPy operations part 5

Python library : Matplotlib

Introduction to Matplotlib

Setting up MatPlotlib

Basics of matplotlib part 1

Basics of matplotlib part 2

Basics of matplotlib part 3

Basics of matplotlib part 4

Basics of matplotlib part 5

Python library: Seaborn

Introduction to Seaborn

Setting up seaborn

Seaborn operations part 1

Seaborn operations part 2

Seaborn operations part 3

Seaborn operations part 4

Machine Learning

Introduction to machine learning

Presentation of Different algorithms

Machine learning algorithms part 1

Machine learning algorithms part 2

Machine learning algorithms part 3

Conclusion

Conclusion