About the Course

This course provides a hands-on approach to data analysis using Python, focusing on the tools and techniques that make Python a powerful language for data-driven decision-making. Participants will learn how to manipulate, analyze, and visualize data effectively using widely adopted libraries such as NumPy, pandas, Matplotlib, Seaborn, SciPy, and Statsmodels.

By the end of the training, participants will be equipped to work with real-world datasets, derive meaningful insights, and communicate statistical findings effectively.

Target Participants

This training is designed for Data Analysts, Statisticians, Researchers, Monitoring and Evaluation Professionals, and professionals interested in leveraging Python for statistical modeling and data-driven decision-making.

Course Duration

One week

What You Will Learn

By the end of this course, participants will be able to:
• Understand the fundamentals of statistical modeling and its applications
• Gain proficiency in Python libraries for data analysis, including NumPy, pandas, SciPy, and Statsmodels
• Perform Exploratory Data Analysis (EDA) to uncover patterns and insights
• Develop, evaluate, and interpret statistical models for predictive and inferential analysis
• Apply advanced statistical techniques such as hypothesis testing, regression analysis, and time series modeling
• Interpret and present statistical findings using appropriate visualizations and reports
• Automate repetitive data analysis tasks using Python

Course Outline

Introduction to Python Programming

• Python for data analysis
• Installing Python and setting up the environment (Anaconda, Jupyter Notebook)
• Basic Python syntax, data types, and operations
• Working with lists, dictionaries, and tuples

Essential Python Libraries for Data Analysis

• Overview of key libraries: NumPy, pandas, Matplotlib, Seaborn, SciPy, Statsmodels, and Scikit-learn
• Installing and importing libraries

Data Analysis Workflow

• Steps in a data analysis project
• Loading datasets and exploring data structures

Data Manipulation in Python (pandas)

• Introduction to pandas
• Series and DataFrame structures
• Loading and saving data (CSV, Excel, JSON)
• Inspecting datasets using .head(), .info(), and .describe()

Data Cleaning and Preprocessing

• Handling missing data (imputation, deletion)
• Filtering, sorting, and subsetting data
• String operations and data transformations

NumPy for Numerical Data

• Creating and manipulating arrays
• Mathematical operations and aggregations
• Broadcasting and vectorized computations

Exploring and Summarizing Data

• Descriptive statistics
• Grouping and aggregation
• Pivot tables and summaries

Data Visualization

• Line plots, bar charts, histograms, and scatter plots
• Customizing charts (titles, labels, legends)
• Advanced visualizations with Seaborn: heatmaps, pair plots, and box plots

Comparison Tests

• Parametric vs non-parametric tests
• Test selection decision framework
• Tests for means, medians, and proportions

Tests of Association

• Correlation analysis
• Parametric and non-parametric association tests

Predictive Modeling Using Python

• Introduction to regression analysis
• Model assumptions and diagnostics
• Linear regression (simple and multiple)
• Non-parametric regression
• Categorical dependent variables: logit and probit regression models

Automating Data Tasks with Python

• Writing reusable functions
• Automating repetitive data preprocessing and analysis tasks

Training Approach

• Hands-on practical exercises
• Presentations and demonstrations
• Group work and discussions
• Case studies based on real-world datasets

Certification

Upon successful completion of the course, participants will be awarded a Certificate of Completion.