Data Analyst Course in Kenya

Module 1.1: Introduction to Python

• Overview of Python: history, features, and use cases.
• Installing Python and setting up the development environment.
• Basic syntax: variables, data types, and simple operations.
• Hands-on: Writing a simple Python program to understand basic concepts.

Module 1.2: Sequences and File Operations

• Introduction to sequences: lists, tuples, sets, and dictionaries.
• File handling: reading and writing files in Python.
• Intro to Mathematical Concepts: Basic probability—how to generate random numbers and use them to simulate random events.
• Hands-on: A program simulating coin tosses or dice rolls to explore simple probability.

Module 1.3: Functions and Object-Oriented Programming

• Defining and using functions: arguments, return values, and scope.
• Object-oriented concepts: classes, objects, inheritance, and polymorphism.
• Hands-on: Writing classes to represent basic statistical concepts (e.g., creating a class for a random variable or distributions).

Module 1.4: Working with Modules and Handling Exceptions

• Importing and using Python modules.
• Exception handling: try, except, and finally blocks.
• Hands-on: Use Python’s built-in statistics module for basic statistical operations (mean, median, variance).

Module 2.1 : Array Manipulation using NumPy

• Introduction to NumPy and its applications.
• Creating and manipulating arrays.
• Mathematical Concepts: Introduction to descriptive statistics—mean, median, mode, variance, and standard deviation using NumPy.
• Probability distributions (e.g., normal distribution) using NumPy’s random module.
• Hands-on: Use NumPy to calculate statistical measures from data arrays, simulate random data based on distributions.

Module 2.2: Introduction to Pandas

• Basic Pandas operations: importing/exporting data, Series, and DataFrames.
• Cleaning and transforming data.
• Mathematical Concepts: Exploring data distributions (frequency distribution, data dispersion).
• Hands-on: Using Pandas to compute statistics (mean, standard deviation) from real datasets.

Module 2.3: Advanced Data Manipulation with Pandas

• Merging, joining, and reshaping data.
• GroupBy, pivot tables, and handling time-series data.
• Mathematical Concepts: Correlation and covariance between datasets.
• Hands-on: Use Pandas to explore relationships between datasets using correlation and apply statistics to grouped data.

Module 2.4: Data Visualization with Matplotlib and Seaborn

• Basic visualization: line plots, bar charts, and scatter plots.
• Mathematical Concepts: Visualizing statistical distributions (histograms, box plots) and relationships between variables (scatter plots).
• Hands-on: Plotting histograms, scatter plots, and box plots to visualize data distributions and compare variables.

Module 2.5: Advanced Data Visualization

• Creating subplots, multiple axes, and advanced customizations in Matplotlib.
• Statistical visualizations in Seaborn (e.g., pair plots, heatmaps).
• Mathematical Concepts: Visualizing probability distributions, regression analysis, and statistical relationships between variables (pair plots, correlation heatmaps).
• Hands-on: Plotting probability density functions and visualizing relationships between multiple variables using Seaborn.

Module 2.6: Data Gathering and Web Scraping using Python

• Introduction to web scraping: HTML parsing and scraping static pages with BeautifulSoup.
• Using Selenium for scraping dynamic websites.
• Hands-on: Scraping data from websites and performing simple data analysis/statistical insights from the scraped data.

Module 3.1: Introduction to SQL & Databases

• What is SQL? Overview and Importance
• Relational Database Concepts (Tables, Rows, Columns)
• Common Database Management Systems (MySQL, PostgreSQL, Oracle, SQL Server)
• SQL Syntax Rules
• Connecting to a Database (Using tools like MySQL Workbench or pgAdmin)

Module 3.2: Basic SQL Queries

• The SELECT Statement
• Selecting Specific Columns
• Using DISTINCT to Remove Duplicates
• Simple WHERE Clauses for Filtering
• Using ORDER BY for Sorting Data
• Basic LIMIT clause (for databases like MySQL/PostgreSQL)

Module 3.3: Filtering & Sorting Data

• Using Comparison Operators (=, !=, >, <, >=, <=)
• Logical Operators (AND, OR, NOT)
• Filtering with BETWEEN, IN, and LIKE
• Handling NULL values (IS NULL, IS NOT NULL)
• Sorting Data with ORDER BY (Ascending & Descending)

Module 3.4: Aggregate Functions & Grouping

• Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
• Grouping Data with GROUP BY
• Filtering Groups with HAVING
• Using GROUP BY with Multiple Columns
• Understanding the Difference between WHERE and HAVING

Module 3.5: Joins & Data Relationships

• Introduction to Joins and Data Relationships
• INNER JOIN for Matching Data
• LEFT JOIN for Including Non-matching Records
• RIGHT JOIN and FULL OUTER JOIN (if supported by the database)
• Using Aliases for Table Names
• Joining Multiple Tables
• Best Practices for Writing Joins

Module 4.1: Introduction to Power BI

• Overview of Power BI components (Desktop, Service, Mobile).
• Copilot Introduction: How to use AI-powered features for data exploration.
• Installing Power BI Desktop and setting up the environment.
• Navigating the Power BI interface: ribbons, panes, and views.
• Hands-on Lab: Importing sample datasets and exploring Copilot.

Module 4.2: Data Preparation & Transformation

• Connecting to various data sources (Excel, SQL, Web APIs).
• Data profiling, data types, and handling missing values.
• Advanced transformations: merging, appending, pivoting, unpivoting data.
• Using Power Query Editor for complex data shaping.
• Hands-on Lab: Transforming raw data into a clean dataset.

Module 5.1: Data Modeling & DAX Fundamentals

• Designing star and snowflake schemas.
• Creating and managing table relationships.
• DAX basics: calculated columns, measures, and quick measures.
• Using DAX for basic calculations like SUM, AVERAGE, COUNT.
• Hands-on Lab: Building a data model for sales data analysis.

Module 5.2: Advanced DAX Functions & Optimization

• Advanced DAX functions: CALCULATE, FILTER, ALL, RELATED.
• Time intelligence functions for date-based calculations (e.g., YTD, QTD).
• Performance optimization techniques: DAX query tuning, minimizing model size.
• Hands-on Lab: Creating advanced DAX measures for business insights.

Module 6.1: Visualizations & Reporting Best Practices

• Building advanced visuals: combination charts, gauges, maps, and custom visuals.
• Creating dynamic reports with slicers, bookmarks, and drill-through.
• Designing for user experience: layout, color schemes, and storytelling.
• Best practices for dashboard design and performance.
• Hands-on Lab: Creating an interactive sales performance dashboard.

Module 6.2: Power BI Service & Collaboration

• Publishing reports and dashboards to Power BI Service.
• Configuring dataset refresh schedules and managing gateways.
• Setting up Row-Level Security (RLS) for data access control.
• Hands-on Lab: Publishing and sharing reports with user access control.

Module 7.1: Copilot in Power BI

• Using Copilot to generate natural language insights and queries.
• Automating data exploration and report generation.
• Implementing Copilot for predictive analytics and recommendations.
• Hands-on Lab: Using Copilot for advanced data exploration.

Module 7.2: Advanced Data Analysis Techniques

• Utilizing AI visuals like Key Influencers, Decomposition Tree, and Smart Narrative.
• Conducting clustering, anomaly detection, and forecasting.
• Interactive Q&A with natural language queries.
• Hands-on Lab: Applying AI visuals to real-world business data.

Module 7.3: Microsoft Fabric Deep Dive

• Overview of Microsoft Fabric: Synapse, Data Factory, Data Lake, and Power BI.
• Using OneLake for centralized data storage.
• Building Synapse Dataflows for real-time data ingestion.
• Creating data pipelines for ETL processes and integrating with Power BI.
• Hands-on Lab: Setting up a Fabric workspace, creating dataflows, and integrating with Power BI.

Module 8.1: Data Preparation using Tableau Prep

• Overview of Tableau Prep for data cleaning and transformation.
• Importing, filtering, and shaping data for analysis.
• Data profiling and preparing datasets for analysis.

Module 8.2: Data Connection with Tableau Desktop

• Connecting to various data sources (spreadsheets, databases, cloud).
• Understanding live connections vs extracts.
• Managing data joins, unions, and blends.

Module 8.3: Basic Visual Analytics

• Building foundational visualizations like bar charts, line charts, and scatter plots.
• Sorting, filtering, and grouping data.
• Working with visual marks (size, color, labels) for enhanced data presentation.

Module 8.4: Calculations in Tableau

• Creating row-level and aggregate calculations.
• Using string functions, logical functions, and conditional calculations.
• Practical applications of calculations for deriving insights.

Module 9.1: Advanced Visual Analytics

• Incorporating reference lines, trend lines, and forecasts.
• Using parameters to create dynamic visualizations.
• Highlight actions, sets, and advanced tooltips for interactivity.

Module 9.2: Level of Detail (LOD) Expressions in Tableau

• Understanding Fixed, Include, and Exclude LOD calculations.
• Practical scenarios for using LOD expressions in reporting.
• Combining LOD expressions with other calculations.

Module 9.3: Advanced Charts in Tableau

• Creating dual-axis charts, waterfall charts, and bullet graphs.
• Understanding when and how to use advanced charts for storytelling.
• Hands-on practice with custom chart creation.

Module 9.4: Dashboards and Stories

• Building interactive dashboards from multiple sheets.
• Using dashboard actions to filter and highlight data dynamically.
• Creating stories to present data insights in a narrative format.