Learn Basic Data science and Python Libraries

Covers all Essential Python topics and Libraries for Data Science or Machine Learning Beginner Such as numpy pandas etc.

What you'll learn

NumPy for Data Science

Linear Algebra with NumPy

Data Manipulation with Pandas

Data Cleaning with Pandas

Introduction to Linear Regression

Deal with real problems

Data Visualization with Matplotlib and Seaborn

Requirements

• For Beginners  

Description

This is a complete course for the formation of Data Scientist, with more than 250 exercises from A-Z, covering from the most basic to the most advanced concepts. It focuses on active methodologies, where the student is the protagonist in this process, thus, we bring several solved exercises, notebooks of contents summary and much more, with a focus on learning programming based on practice and simulation of real problems (such as data cleaning, treatment of missings, separation of data in training and testing, grouping and joining of datasets, among others).

In this sense, the course has exercises solved on the main Python libraries for Data Science: NumPy, Pandas, Matplotlib and Seaborn. In addition, it seeks to rescue elementary concepts of Linear Algebra, through the NumPy library.

In general, the course presents exercises that encompass the main functions of NumPy for Data Science, such as aggregation functions, matrix definition, matrix operations, among others. As for Pandas, we seek to offer an overview from the definition of Series and DataFrames, inspection of datasets, boolean selection, filtering of rows of columns, removal of rows and columns, treatment of missing data, grouping and joining functions, opening and writing files, descriptive statistics functions, among other topics.

Finally, there are several problems related to data visualization, with the libraries Matplotlib and Seaborn, from classic datasets.

 Notions of time series and finance are also introduced. There are also examples of how to prepare a dataset for a Machine Learning project.

Welcome to my course Basics Data Science with Numpy, Pandas, and Matplotlib     

This course will teach the basics of Python Data Structures and the most important Data Science libraries like NumPy and Pandas with step-by-step examples!

In this course, we will learn step by step with starting with basics understanding of jupyter notebook and how to write a code in jupyter notebook

and understanding every function of jupyter notebook then we will learn basic pythons such as Then we will go ahead with the basic python data types like strings, numbers, and its operations. 

We will deal with different types of ways to assign and access strings, string slicing, replacement, concatenation, formatting, and strings.

Dealing with numbers, we will discuss the assignment, accessing, and different operations with integers and floats. The operations include basic ones and also advanced ones like exponents. Also, we will check the order of operations, increments, and decrements, rounding values, and typecasting.

Then we will proceed with basic data structures in python like Lists tuples and set. For lists, we will try different assignments, access, and slicing options. Along with popular list methods, we will also see list extension, removal, reversing, sorting, min and max, existence check, list looping, slicing, and also inter-conversion of lists and strings.

So let's start with the lessons. See you soon in the course lecture

Who this course is for:

This course is for who want to learn basic of data science and their libraries and python programming