Introduction

In today's data-intensive business landscape, the demand for powerful, flexible, and automatable solutions for data analysis and reporting is rapidly increasing, making Python for Business Intelligence and Reporting an indispensable skill for professionals seeking to go beyond off-the-shelf tools and build custom, scalable analytical workflows. Python, with its rich ecosystem of libraries, empowers users to efficiently extract, transform, analyze, and visualize complex datasets, providing unparalleled control and enabling sophisticated insights that drive smarter business decisions. This training course is meticulously designed to equip data analysts, BI developers, data scientists, financial analysts, and IT professionals with cutting-edge knowledge and practical skills in mastering Python fundamentals, leveraging key libraries like Pandas for data manipulation, NumPy for numerical operations, and Matplotlib/Seaborn for advanced data visualization, automating reporting tasks, integrating with databases and APIs, performing exploratory data analysis, and building custom analytical applications to enhance business intelligence capabilities. Participants will gain a comprehensive understanding of how to harness Python's versatility to automate reporting, streamline data processes, and unlock deeper, more impactful business insights.

Duration

10 days

Target Audience

• Data Analysts
• Business Intelligence Developers
• Aspiring Data Scientists
• Financial Analysts
• Marketing Analysts
• IT Professionals working with data
• Statisticians & Researchers
• Business Managers with a technical aptitude
• Excel Power Users looking to automate
• Anyone wanting to build custom data solutions

Objectives

• Understand Python fundamentals and its application in the BI domain.
• Master data manipulation and cleaning techniques using the Pandas library.
• Develop proficiency in numerical computing with the NumPy library.
• Learn to create compelling and customized data visualizations using Matplotlib and Seaborn.
• Understand how to connect Python to various data sources, including databases and APIs.
• Explore techniques for automating reporting tasks and generating dynamic reports.
• Develop skills in performing exploratory data analysis (EDA) to uncover insights.
• Learn about basic statistical analysis and data aggregation in Python.
• Understand version control (Git) basics for collaborative data projects.
• Formulate strategies for building end-to-end data workflows in Python.
• Apply Python programming to solve real-world business intelligence and reporting challenges.

Course Content

Module 1. Python Fundamentals for Data Analysis

• Introduction to Python: Installation, environment setup (Anaconda, Jupyter Notebooks)
• Basic Python syntax: Variables, data types (strings, numbers, booleans)
• Control flow: If-else statements, for loops, while loops
• Data Structures: Lists, tuples, dictionaries, sets
• Functions: Defining and calling functions, scope

Module 2. Introduction to NumPy for Numerical Computing

• What is NumPy?: Advantages for numerical operations
• NumPy Arrays: Creating and manipulating 1D, 2D arrays
• Array indexing and slicing
• Basic array operations: Arithmetic, broadcasting
• Vectorization for efficient computations

Module 3. Pandas for Data Manipulation and Analysis (Part 1)

• Introduction to Pandas: Series and DataFrames
• Creating DataFrames: From dictionaries, lists, CSV files
• Data Loading: read_csv, read_excel, read_sql
• DataFrame Inspection: head(), info(), describe(), shape
• Selecting and Subsetting DataFrames: loc, iloc

Module 4. Pandas for Data Manipulation and Analysis (Part 2)

• Data Cleaning: Handling missing values (fillna, dropna), duplicates
• Data Transformation: Renaming columns, changing data types
• Filtering and Sorting DataFrames
• Grouping and Aggregating Data: groupby(), agg()
• Merging and Joining DataFrames: merge(), concat()

Module 5. Data Visualization with Matplotlib

• Introduction to Matplotlib: Creating basic plots
• Line Plots, Bar Plots, Scatter Plots, Histograms
• Customizing plots: Titles, labels, legends, colors, styles
• Subplots: Arranging multiple plots
• Saving plots to files

Module 6. Advanced Data Visualization with Seaborn

• Introduction to Seaborn: Enhancing Matplotlib visualizations
• Statistical Plotting: Distplots, box plots, violin plots
• Relational Plots: Pair plots, joint plots
• Categorical Plots: Swarm plots, count plots
• Creating aesthetically pleasing and informative charts

Module 7. Connecting Python to Databases

• SQL Database Connectivity: Using sqlite3, psycopg2, pyodbc
• Executing SQL queries from Python
• Loading query results directly into Pandas DataFrames
• Connecting to ODBC/JDBC Data Sources
• Best practices for database interaction

Module 8. Working with APIs for Data Extraction

• Introduction to Web APIs: RESTful services
• Using the requests library for HTTP requests (GET, POST)
• Handling JSON and XML data
• Extracting data from popular APIs (e.g., public data APIs)
• Authentication methods for APIs

Module 9. Exploratory Data Analysis (EDA) in Python

• Understanding Data Distributions: Skewness, kurtosis
• Outlier Detection and Treatment
• Correlation Analysis: Visualizing relationships between variables
• Hypothesis Generation from data patterns
• Practical EDA workflow for BI insights

Module 10. Automating Reports and Generating Static Reports

• F-strings for Dynamic Text and Reports
• Generating reports in plain text or CSV format
• Exporting DataFrames to Excel worksheets with formatting
• Automating chart generation and saving images
• Creating simple reporting scripts

Module 11. Custom Reporting and Dashboarding

• Generating PDF Reports with Python Libraries (e.g., ReportLab, FPDF)
• Building simple web dashboards with Flask/Dash (conceptual overview)
• Integrating visualizations into web applications
• Creating reusable reporting components
• Templating for dynamic report generation

Module 12. Statistical Analysis for BI

• Descriptive Statistics: Mean, median, mode, standard deviation
• Inferential Statistics: T-tests, ANOVA (conceptual)
• Regression Analysis: Simple linear regression with statsmodels or scikit-learn
• Interpreting statistical output for business insights
• Time series analysis basics for forecasting

Module 13. Performance Optimization and Best Practices

• Writing Efficient Python Code: Vectorization, avoiding loops
• Memory Management for Large Datasets
• Using .apply() vs. vectorized operations in Pandas
• Profiling Python code for performance bottlenecks
• Best practices for code readability and maintainability

Module 14. Version Control with Git for Data Projects

• Introduction to Version Control: Why it's essential for collaboration
• Git Basics: init, add, commit, push, pull
• Branching and Merging workflows
• Using GitHub/GitLab for collaborative data projects
• Best practices for managing data scripts and notebooks

Module 15. End-to-End BI Workflow in Python and Future Trends

• Building a Complete Data Pipeline: From extraction to reporting
• Scheduling Python scripts for automated execution (e.g., cron, Windows Task Scheduler)
• Integrating Python with BI tools (e.g., Power BI Python scripts, Tableau Python Server)
• Introduction to advanced topics: Dask for larger-than-memory datasets, Streamlit for apps
• The evolving role of Python in the modern BI stack.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.