Master data analysis, statistics, and machine learning with Python
Data is not the issue—it’s the ability to analyze it, visualize it, and draw reliable conclusions that is lacking. Python has become the gold standard in data science, with libraries like NumPy, Pandas, Matplotlib, and Scikit-learn. This training covers the entire analytical process: from data manipulation to multivariate modeling, including visualization and inferential statistical testing.
Is it for you ?
General Public.
Prerequisites
Have basic knowledge of Python
What You'll Walk Away With
- ✓ Manipulate and analyze data using NumPy and Pandas
- ✓ Visualize data in a clear and professional manner using Matplotlib
- ✓ Apply statistical and machine learning models to real-world data
Training content
1 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
2 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
3 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
4 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
5 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
6 Plotting your first graphs
- Bar charts
- Histograms
- Curves
- Pie charts
- Scatter plots
- Area charts
- 3D charts and animations
- Charts with multiple axes
- Box plots
7 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
8 Descriptive statistics with Python
- Introduction to statistics
- Prerequisites
- Introduction to statistics
- Why use Python for statistics?
- Setting up your work environment
- Descriptive statistics
9 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
10 The power of data visualization
- Introduction to Matplotlib
- Adding colors to your graphs
- Using histograms
- Using box plots
- Studying the relationships between two variables
11 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
12 Supplementary book
This module offers you access to digital course materials.
Master Data Science with Python
📌 Practical information
100% online training. Accessible anytime from anywhere for one year. If you have any questions about registration, the language of instruction, or the cancellation terms, please consult our FAQ