Practical Data Analytics and Business Intelligence Master Program

Transform your career with the Practical Data Analytics and Business Intelligence Master Program. Gain hands-on expertise in data collection, analysis, and visualization to drive strategic decision-making. Ideal for professionals looking to excel in data-driven environments and deliver impactful business insights.

Virtual classes | 12 Week(s) | FEE: Ksh 120000 / USD 1200

Apply Now

Application Deadline: 7th October 2024

Want to upskill?

Enroll today in our Practical Data Analytics and Business Intelligence Master Program to elevate and expand your expertise.

Program Fee

120000 / USD 1200

Duration

12 Week(s)

Study Mode

Virtual classes

Introduction

The Practical Data Analytics and Business Intelligence Master Program is designed to equip professionals with essential skills in data analytics and business intelligence. Through hands-on training, you'll learn to effectively collect, analyze, and visualize data, enabling you to make informed decisions and drive business success. This program provides practical, real-world applications to help you master data-driven strategies and enhance your career in today’s data-centric landscape

Application Process

Register

Submit your registration by filling in the form online.

Make Payments

Receive Invoice upon registration and make payments.

Join program

Choose a mode of study and attend course.

Program Prerequisites

Participants are expected to have:

Basic computer skills and comfort working online.
Interest in data analysis (prior experience optional).
Familiarity with spreadsheets (e.g., Microsoft Excel).
Foundational statistics knowledge.
Basic understanding of programming concepts.

Program Modules

Fundamentals of data analytics and business intelligence (BI)
Importance of data-driven decision-making
Data analytics lifecycle
Role of BI in organizations
Introduction to key concepts:
- Data warehousing
- Data mining
- Data visualization
Provides a solid foundation for the rest of the course
Case Study: Enhancing Operational Efficiency with Data Analytics

Focus on data preparation and cleaning using Microsoft Excel
Handle common data issues: missing values, duplicates, incorrect data types
Techniques covered: data transformation, normalization, standardization
Use advanced Excel functions and formulas for automating the data cleaning process
Case Study: Optimizing Financial Data Accuracy and Efficiency with Advanced Excel Techniques

Builds on basic Excel skills
Focuses on advanced Excel functionalities for data analysis
Exploration of:
- Pivot tables
- Advanced charting techniques
- Excel's Data Analysis Toolpak
Emphasis on:
- Creating dynamic dashboards
- Generating reports to visualize and interpret data effectively
Case Study: Leveraging Advanced Excel Techniques for Efficient Financial Reporting and Data Visualization

Introduction to SQL (Structured Query Language) as a tool for managing and querying databases
Creating, modifying, and querying databases using SQL
Basic SQL syntax
Joining tables
Filtering data
Aggregating results
Extracting meaningful insights from large datasets
Case Study: Leveraging SQL for Enhanced Customer Segmentation and Targeted Marketing

Expand on basic SQL knowledge with advanced techniques.
Learn about subqueries, window functions, and common table expressions (CTEs).
Explore stored procedures for complex data analysis.
Practice solving real-world data analysis problems with advanced SQL queries.
Case Study: Optimizing Fraud Detection and Risk Assessment in Financial Services with Advanced SQL Techniques

Introduction to Python as a versatile programming language for data analysis
Basics of Python programming:
- Data types
- Control structures
- Functions
Introduction to key libraries for data analysis:
- pandas
- numpy
Enabling efficient data manipulation and analysis
Case Study: Streamlining Financial Data Analysis with Python

Builds on Python fundamentals.
Focuses on data visualization using popular libraries (e.g., matplotlib, seaborn).
Teaches how to create various charts and plots for visual data representation.
Covers best practices for data visualization.
Explores techniques for creating interactive visualizations.
Case Study: Interactive Data Visualization for Investment Analysis with Python

Perform exploratory data analysis (EDA) using Python
Summarize the main characteristics of a dataset using visual methods
Learn to identify patterns, detect anomalies, and generate hypotheses
Explore techniques for data profiling, correlation analysis, and feature selection
Case Study: Enhancing Fraud Detection in Financial Services Through Exploratory Data Analysis with Python

Introduction to Power BI as a tool for data visualization and reporting
Connecting to various data sources
Transforming data for analysis
Creating interactive dashboards and reports
Exploring key features:
- Power Query
- Data modeling
- DAX (Data Analysis Expressions)
Case Study: Streamlining Financial Reporting and Decision-Making with Power BI

Builds on basic Power BI knowledge.
Covers advanced Power BI techniques.
Teaches advanced data modeling.
Explores custom visuals.
Focuses on optimizing performance in Power BI.
Introduces techniques for creating advanced DAX measures.
Explores leveraging Power BI's AI capabilities.
Case Study: Optimizing Financial Reporting and Forecasting with Advanced Power BI Techniques and AI Integration

Focus on integrating Power BI with Excel, SQL, and Python for comprehensive data analytics solutions.
Learn to use Power BI's data connectors to integrate data from multiple sources.
Cover techniques for automating data workflows.
Explore the creation of end-to-end BI solutions.
Case Study: Optimizing Financial Reporting with Power BI, Excel, SQL, and Python