Objectives of the training
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 PythonTrainers
Course architecture
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
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
Pedagogical details
Type of training
Training Notes
100% remote training. Accessible anytime, anywhere. One year of access to training and digital manual.
Private or personalized training
Do you have several employees interested in the same training course? Whether in person at your offices or remotely in virtual mode, we offer private training courses tailored to your team's needs. Group rates are available. Contact us for more details or request a quote online.
Request a quotePrivate or personalized training
Do you have several employees interested in the same training course? Whether in person at your offices or remotely in virtual mode, we offer private training courses tailored to your team's needs. Group rates are available. Contact us for more details or request a quote online.
Request a quote