Objectives
The objective of this training is to learn data science with Python by developing your skills in data manipulation, visualization, and analysis, while discovering essential tools such as NumPy, Pandas, Matplotlib, and Scikit-Learn, to successfully carry out complete data exploration and value creation projects.
Prerequisite
Have basic knowledge of Python
Your benefits
Content
Python for Data Science
- Python and Data Science
- Choosing Python for Data Science
- The NumPy Library
- The Type and Size of NumPy Vectors
- Initialization and NumPy Arrays
- Accessing Data from a One-Dimensional NumPy Array
- Accessing Data from a Two-Dimensional NumPy Array
- Linear Algebra with NumPy
- NumPy Array versus Python List
- Descriptive Statistics with NumPy
Data Visualization
- Installing Anaconda and Jupyter
- Working with Jupyter
- The Pandas Library
- Accessing Data from a Data Frame
- Filtering data from a Data Frame
- Sorting data from a Data Frame
- Basic statistics with a Data Frame
- Reading large files with Pandas
See more + / -
Inferential statistics with Python
- Using the melt and apply methods
- Extracting information from existing data
- Creating new variables from existing data
- Visualizing data with Matplotlib
- The Normal distribution
- Introduction to hypothesis testing
- Statistical test for comparing two means
Multivariate modeling with Python
- Introduction to linear regression
- Modeling example with linear regression
- Introduction to the Support Vector Machine algorithm
- Modeling example with a Support Vector Machine
- Introduction to the K-Means algorithm
- Example of the K-Means algorithm
- Conclusion
Data visualization with Matplotlib – Create professional and intuitive graphs
- Introduction to data visualization
- Data visualization
- Why use Matplotlib?
- Installing and getting started with Matplotlib
- Different types of graphs
- Getting started with Matplotlib
- Basic structure
- Plotting your first graph
- Saving and displaying graphs
- Displaying multiple curves
Plotting your first graphs
- Bar charts
- Histograms
- Curves
- Pie charts
- Scatter plots
- Area charts
- 3D charts and animations
- Charts with multiple axes
- Box plots
Customizing your charts
- Changing color, style, and markers
- Adding labels and titles
- Adding a legend
- Managing axes
- Managing subcharts
- Adding annotations and text
- Advanced customization
Descriptive statistics with Python
- Introduction to statistics
- Prerequisites
- Introduction to statistics
- Why use Python for statistics?
- Setting up your work environment
- Descriptive statistics
Descriptive statistics
- Importing a sample with Pandas
- Data structure
- Manipulating data with Pandas
- Using advanced Pandas functions
- Using the apply function
- Studying a quantitative variable
- Studying a qualitative variable
The power of data visualization
- Introduction to Matplotlib
- Adding colors to your graphs
- Using histograms
- Using box plots
- Studying the relationships between two variables
Parametric and non-parametric tests
- The reduced centered normal distribution
- Estimating a mean
- Hypothesis testing
- Calculating the p-value
- Statistical modeling
- Statistics vs. machine learning
- Conclusion
Supplementary book
This module offers you access to digital course materials.
Master Data Science with Python
💡 Useful information
100% online training. Accessible anytime, from anywhere, giving a one year of access to the training. If you have any questions regarding registration, the language of instruction, or cancellation policies, please consult our FAQ .
Trainers
Private or personalized training
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