Data Analytics


Data Analytics Master Class

Welcome to our Data Analytics Master Class!

Get ready to learn about Data Analytics and how you can start earning from it.

Register for your spot now! 

The full course outline is at the bottom of the page. 

Make sure to join our Telegram group for enquiries by clicking the button below. 

There are two main classes

1. Online Classes – Ksh 15,000 
2. Physical Classes – Ksh 30,000/=

You can pay in three ways

Online Classes
1. First Half Workshop – Ksh 10,000/=
2. Second Half Workshop – Ksh 7,000/=
3. Full Workshop – Ksh 15,000/=

Physical Classes
1. First Half Workshop – Ksh 20,000/=
2. Second Half Workshop – Ksh 13,000/=
3. Full Workshop – Ksh 30,000/=

Note: Physical Classes are only available in Nairobi


Business no. – 600100
Account no. – 0100007332259

Stanbic Bank


Enter your personal details below:

    Data Analytics Course Outline

    This is what you will learn on the workshop

    Week 1: Introduction to Data Analytics

    Session 1: Overview of Data Analytics

    1. Introduction to Data Analytics

    2. Importance of Data Analytics in various industries

    3. Types of data and their characteristics

    Session 2: Basics of Excel for Data Analytics

    1. Introduction to Excel for data analysis

    2. Data importing, sorting, and filtering

    3. Basic functions (SUM, AVERAGE, COUNT, etc.)

    Session 3: Data Visualization with Excel

    1. Introduction to data visualization in Excel

    2. Creating charts and graphs

    3. Visual representation of data trends

    Week 2: Data Cleaning and Preparation

    Session 4: Data Cleaning Techniques

    1. Importance of data cleaning

    2. Identifying and handling missing data

    3. Removing duplicates and inconsistencies

    Session 5: Data Preparation in Excel

    1. Data formatting and transformation

    2. Data validation and error handling

    3. Introduction to pivot tables for data summarization

    Session 6: Project: Data Cleaning and Preparation in Excel

    1. Apply data cleaning and preparation techniques on real datasets

    Week 3: Introduction to SQL

    Session 7: Introduction to Databases and SQL

    1. Basics of databases and SQL

    2. Understanding tables, rows, and columns

    3. SQL syntax (SELECT, WHERE, GROUP BY, etc.)

    Session 8: Querying Data using SQL

    1. Writing basic SQL queries

    2. Filtering and sorting data

    3. Joining tables for data retrieval

    Session 9: Project: SQL Data Querying

    1. Practice SQL queries on sample databases

    Week 4: Exploratory Data Analysis (EDA)

    Session 10: Exploratory Data Analysis Concepts

    1. Importance of EDA in Data Analytics

    2. Statistical summary of data

    3. Data visualization for EDA using Excel

    Session 11: EDA with Visualization Tools

    1. Introduction to visualization tools (Tableau, Power BI)

    2. Creating interactive visualizations for EDA

    3. Analyzing data patterns and relationships

    Session 12: Project: Exploratory Data Analysis

    1. Perform EDA on datasets using visualization tools

    Week 5: Introduction to Statistical Analysis

    Session 13: Basic Statistical Concepts

    1. Overview of statistical analysis

    2. Measures of central tendency and dispersion

    3. Probability distributions

    Session 14: Hypothesis Testing

    1. Introduction to hypothesis testing

    2. Types of tests (t-test, chi-square, etc.)

    3. Conducting hypothesis tests in Excel

    Session 15: Project: Basic Statistical Analysis in Excel

    1. Apply basic statistical tests on datasets in Excel

    Week 6: Data Analytics Techniques

    Session 16: Regression Analysis

    1. Introduction to regression analysis

    2. Linear regression in Excel

    3. Interpretation of regression results

    Session 17: Classification Techniques

    1. Basics of classification

    2. Introduction to decision trees

    3, Implementing classification algorithms in Excel

    Session 18: Project: Regression and Classification in Excel

    1. Apply regression and classification techniques on sample datasets

    Week 7: Advanced Data Analytics

    Session 19: Time Series Analysis

    1. Understanding time series data

    2. Time series visualization in Excel

    3. Forecasting using time series data

    Session 20: Clustering and Segmentation

    1. Introduction to clustering algorithms

    2. K-means clustering in Excel

    3. Segmenting data for analysis

    Session 21: Project: Time Series Analysis and Clustering

    1. Apply time series analysis and clustering techniques on datasets

    Week 8: Capstone Project and Conclusion

    Session 22: Capstone Project

    1. Work on a comprehensive data analytics project

    2. Apply all learned concepts and techniques

    Session 23: Project Presentation

    1. Present the capstone project findings

    2. Review key concepts and takeaways

    3. Next steps in Data Analytics

    Thank you for Registering!