01Data Analytics Foundations
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Topics:
Analytics concepts, data types, terminology, the analysis
lifecycle, and responsible AI
Introduction to Data Analytics
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Fundamentals and importance of data analytics
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Difference between data, information, and insights
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Role of a data analyst and data-driven decision-making
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Applications in business, finance, marketing, and healthcare
Types of Data Analytics
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Descriptive analytics — What happened?
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Diagnostic analytics — Why did it happen?
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Predictive analytics — What might happen?
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Prescriptive analytics — What should we do?
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Real-world business use cases
Data Types, Structures, and Sources
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Structured, semi-structured, and unstructured data
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Quantitative and categorical data
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Discrete, continuous, nominal, and ordinal data
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Data sources: Excel, databases, APIs, and surveys
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Rows, columns, records, variables, and data granularity
Essential Analytics Terminology
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Dimensions, measures, metrics, and KPIs
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Population, sample, and sampling
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Missing values, duplicates, and outliers
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Data quality, accuracy, and consistency
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Trends, patterns, correlation, and anomalies
Business Problems and Analytical Questions
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Understanding business objectives and requirements
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Formulating analytical questions and hypotheses
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Defining KPIs and measurable success criteria
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Translating business problems into data analysis tasks
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Using AI to support analytical planning
Data Analysis Lifecycle
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Problem definition and data collection
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Data profiling, cleaning, and transformation
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Exploratory Data Analysis (EDA)
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Statistical analysis and interpretation
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Data visualization and reporting
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Communicating insights and monitoring KPIs
Introduction to AI in Data Analytics
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Applications of generative AI in data analytics
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AI-assisted SQL, Python, formulas, and reporting
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Prompt engineering for analytical tasks
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Validating AI-generated results and identifying limitations
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Data privacy and responsible AI usage
Practical Exercise
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Explore a sample sales dataset
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Identify data types and quality issues
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Formulate business questions and select KPIs
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Prepare a basic data analysis plan using AI assistance
Learning Outcome
Students will understand core data analytics concepts and
terminology, identify data types, sources, and quality issues,
formulate analytical questions and define KPIs, understand the
end-to-end data analysis lifecycle, and use AI responsibly in
data analysis tasks.
02Excel for Data Analysis+
Topics:
Spreadsheet skills, formulas, data cleaning, PivotTables, and
AI-assisted analysis
Excel Workspace and Formula Basics
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Navigating the interface, worksheets, rows, and columns
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Entering, formatting, sorting, and filtering data
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Writing basic formulas and using built-in functions
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Relative and absolute cell references and when to use each
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Generating Excel formulas with AI assistance
Analytical Functions in Excel
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Logical functions: IF, AND, OR, and IFERROR
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Conditional functions: SUMIF, SUMIFS, COUNTIF, and COUNTIFS
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Lookup functions: XLOOKUP and VLOOKUP
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Text and date functions for preparing fields
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Using AI to understand and troubleshoot formulas
Data Cleaning in Excel
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Finding missing values and duplicate records
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Fixing inconsistent text and formatting
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Reshaping text with Text to Columns and Flash Fill
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Data validation and error identification
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AI-assisted data-cleaning suggestions
PivotTables, Charts, and KPIs
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PivotTables and PivotCharts for quick summaries
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Grouping and aggregating data
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Charts that support analytical reporting
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Introduction to KPIs and business metrics
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AI-assisted interpretation of summaries and trends
Business Analysis in Excel
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Sales, revenue, profit, and growth analysis
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Comparing categories and time periods
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Identifying trends and anomalies
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Preparing analytical reports
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AI-assisted analytical questions and hypotheses
AI-Assisted Excel Analytics
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Introduction to generative AI tools for spreadsheet analysis
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Prompt engineering for formulas and analysis
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Explaining formulas and troubleshooting errors
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Generating summaries and analytical insights
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Verifying AI-generated calculations and conclusions
Learning Outcome
Students will use Excel to clean, summarize, analyze, and
visualize data, with AI assistance.
03SQL and Relational Databases
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Topics:
PostgreSQL, querying, aggregations, joins, window functions, and
AI-assisted SQL
Databases and PostgreSQL Basics
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What a database is and why analysts use one
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Relational databases, tables, and relationships
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Introduction to PostgreSQL
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Database setup and connection
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Introduction to AI-assisted SQL learning
SQL Querying Fundamentals
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SELECT and DISTINCT to retrieve and de-duplicate values
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WHERE conditions to filter rows
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ORDER BY and LIMIT to sort and restrict results
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Comparison and logical operators
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Using AI to generate and explain basic SQL queries
Aggregations and Grouping
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COUNT, SUM, AVG, MIN, and MAX for summarizing data
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GROUP BY and HAVING to summarize by category
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Conditional aggregation
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Aggregating data for business reporting
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AI-assisted query generation and verification
Joins Across Tables
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Primary keys and foreign keys that link tables
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INNER JOIN to match related records
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LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN
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Joining multiple tables in one query
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AI-assisted explanation of join logic
Subqueries and Conditional Logic
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Subqueries in WHERE and FROM
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EXISTS and IN for membership checks
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CASE expressions for conditional results
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Common Table Expressions (CTEs) for readable queries
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AI-assisted query decomposition and debugging
Date, String, and NULL Handling
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Date and time functions
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String manipulation functions
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NULL values and COALESCE
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Data filtering and transformation
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AI-assisted data-cleaning queries
Window Functions for Analytics
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Window functions and how they differ from GROUP BY
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ROW_NUMBER, RANK, and DENSE_RANK for ranking
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LAG and LEAD for row-to-row comparison
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Running totals and moving averages
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AI-assisted analytical query development
Query Performance and Data Quality
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Introduction to indexes and query plans
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Identifying duplicate and inconsistent records
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Data quality checks using SQL
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Basic query optimization concepts
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Using AI to review query logic and suggest improvements
AI-Assisted SQL Analytics
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Prompt engineering for text-to-SQL tasks
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Generating queries from business questions
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Explaining complex queries using AI
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Debugging AI-generated SQL
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Validating query results against business requirements
Learning Outcome
Students will use SQL to retrieve, combine, transform, and
analyze data from relational databases.
04Python Programming Essentials
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Topics:
Python setup, Jupyter Notebook, control flow, data structures,
functions, and files
Python and Jupyter Notebook Basics
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Python installation and environment setup
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Jupyter Notebook fundamentals
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Variables, data types, and operators
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Input, output, and basic expressions
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AI-assisted explanations of Python code
Control Flow and Data Structures
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Conditional statements for decision logic
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Loops and iteration
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Lists, tuples, sets, and dictionaries
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Comprehensions for concise code
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AI-assisted coding exercises and debugging
Functions and File Handling
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Defining and calling functions
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Parameters, return values, and scope
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Lambda functions
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Reading and writing CSV and text files
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Exception handling and AI-assisted debugging
Learning Outcome
Students will learn the Python fundamentals required for data
analysis.
05NumPy and Pandas+
Topics:
Numerical computing, DataFrames, data manipulation, and
AI-assisted Python analysis
NumPy Arrays and Operations
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NumPy arrays and array creation
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Array dimensions, shape, and data types
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Indexing and slicing
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Vectorized operations for fast computation
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AI-assisted explanations of numerical operations
Numerical Analysis with NumPy
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Broadcasting across arrays of different shapes
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Aggregation functions
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Missing and invalid numerical values
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Random number generation
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Basic statistical operations
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AI-assisted numerical problem-solving
Pandas Series and DataFrames
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Series and DataFrames as core data structures
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Reading CSV and Excel files
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Reading Parquet files
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Inspecting datasets using head, info, and describe
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Selecting rows and columns
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AI-assisted Pandas code generation
Data Manipulation with Pandas
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Filtering and sorting
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Creating and modifying columns
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apply, map, and vectorized operations
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GroupBy and aggregation
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Pivot tables and reshaping
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AI-assisted data manipulation
Combining and Transforming Data
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Merge and join operations
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Concatenating datasets
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Melt and pivot operations
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Working with dates and strings
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Identifying and handling missing values
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AI-assisted transformation suggestions and validation
AI-Assisted Python Data Analysis
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Prompt engineering for Python programming
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Generating NumPy and Pandas code
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Debugging errors and interpreting tracebacks
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Reviewing AI-generated code for correctness
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Evaluating code efficiency and alternative approaches
Learning Outcome
Students will manipulate datasets using NumPy and Pandas and
use AI tools to assist with Python-based analysis.
06Data Cleaning and EDA+
Topics:
Data quality, cleaning with Pandas, and exploratory data analysis
Data Cleaning Fundamentals
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Identifying and treating missing values
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Duplicate detection
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Inconsistent categories and formats
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Data type conversion
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Identifying invalid and out-of-range values
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AI-assisted data-quality recommendations
Advanced Cleaning with Pandas
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Handling missing data
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Detecting and treating outliers
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Standardizing text and categorical values
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Date and time cleaning
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Data validation rules
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AI-assisted cleaning code and test cases
Introduction to Exploratory Data Analysis
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Purpose and stages of EDA
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Univariate, bivariate, and multivariate analysis
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Numerical and categorical variable analysis
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Distribution and variability
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Generating and testing hypotheses with AI assistance
Relationships, Trends, and Anomalies
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Correlation and relationships between variables
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Time-based patterns and trends
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Segment comparisons
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Outlier and anomaly identification
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Distinguishing genuine patterns from misleading visualizations
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AI-assisted insight generation and validation
AI-Assisted Exploratory Data Analysis
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Using generative AI to plan EDA
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Generating Python code for analytical questions
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Summarizing dataset characteristics
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Suggesting patterns, anomalies, and follow-up questions
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Verifying AI-generated findings
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Data privacy and responsible AI practices
Learning Outcome
Students will clean datasets, assess data quality, and conduct
exploratory data analysis.
07Data Visualization and Storytelling
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Topics:
Matplotlib, Seaborn, chart selection, and analytical reporting
Visualization with Matplotlib
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Plotting fundamentals
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Line, bar, histogram, and scatter plots
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Figure and axis customization
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Labels, titles, legends, and annotations
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AI-assisted plotting code and chart selection
Statistical Visualization with Seaborn
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Histograms and density plots
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Box plots and violin plots
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Count plots and categorical comparisons
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Scatter plots and regression plots
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Correlation heatmaps
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AI-assisted interpretation of visual patterns
Data Storytelling and Reporting
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Converting analytical results into business insights
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Selecting key findings
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Chart selection and visual hierarchy
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Communicating evidence and limitations
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Creating concise analytical reports
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AI-assisted report structure and explanations
Learning Outcome
Students will create clear visualizations and communicate
analytical findings.
08Statistics and Business Metrics
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Topics:
Descriptive statistics, probability, hypothesis testing, and KPIs
Statistics Fundamentals
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Population and sample
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Mean, median, and mode
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Range, variance, and standard deviation
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Percentiles and quartiles
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Understanding statistical summaries
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AI-assisted statistical explanations
Probability and Distributions
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Basic probability concepts
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Independent and dependent events
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Conditional probability fundamentals
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Normal and other common distributions
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Sampling and sampling variability
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AI-assisted interpretation of probability problems
Correlation and Hypothesis Testing
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Correlation versus causation
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Hypotheses and statistical significance
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p-values and confidence intervals
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Introduction to hypothesis testing
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Common interpretation errors
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AI-assisted explanations with verification of assumptions and
calculations
Business Metrics and Decision-Making
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KPIs and business objectives
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Revenue, profit, growth, and conversion metrics
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Customer and operational metrics
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Comparing performance across segments
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Translating business questions into measurable metrics
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AI-assisted metric suggestions and validation
Learning Outcome
Students will apply statistical concepts, interpret results,
and understand business metrics.
09Power BI and Business Intelligence
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Topics:
Power Query, data modeling, DAX, dashboards, and AI-assisted BI
Power BI Fundamentals
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Power BI Desktop interface
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Connecting to data sources
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Importing Excel and CSV files
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Introduction to Power Query Editor
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Data profiling and transformation
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AI-assisted explanations of data preparation
Data Transformation with Power Query
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Filtering and removing columns
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Handling missing and inconsistent values
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Changing data types
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Splitting and merging columns
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Combining datasets
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AI-assisted transformation troubleshooting
Data Modeling and DAX
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Tables and relationships
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Star schema fundamentals
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Fact and dimension tables
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Calculated columns and measures
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Basic DAX functions
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AI-assisted explanation and validation of DAX formulas
Dashboards and Reporting
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Choosing suitable visualizations
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Building charts, tables, and KPI cards
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Slicers, filters, and interactions
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Dashboard layout and usability
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Communicating insights to business users
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AI-assisted dashboard design and narrative generation
AI-Assisted Business Intelligence
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Overview of AI capabilities in business intelligence
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Natural-language questions about data
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AI-generated summaries and explanations
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AI-assisted DAX and analytical exploration
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Identifying limitations, hallucinations, and misleading
conclusions
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Verifying AI-generated insights against underlying data
Learning Outcome
Students will prepare data models and build interactive
dashboards in Power BI.
10AI for Data Analytics+
Topics:
Generative AI, prompt engineering, AI-assisted workflows, and
responsible AI
AI-Powered Data Analytics
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Role of AI throughout the analytics lifecycle
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Generative AI and large language models (LLMs)
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AI-assisted data understanding and analytical planning
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Prompt engineering fundamentals
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Limitations of AI-generated analytical outputs
Prompt Engineering for Analysts
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Writing clear and specific prompts
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Providing context, goals, and output requirements
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Breaking complex analytical tasks into smaller steps
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Iterative prompting and refining outputs
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Evaluating AI-generated code, queries, and explanations
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Protecting confidential and sensitive data
AI-Assisted Analytics Workflow
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Translating a business problem into analytical questions
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Planning data collection and preparation
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Using AI to assist with SQL and Python tasks
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Cleaning, analyzing, and visualizing data
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Validating results and documenting assumptions
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Combining AI assistance with independent analytical reasoning
AI-Assisted Business Insights
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Summarizing analytical results
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Identifying patterns and formulating hypotheses
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Generating evidence-based recommendations
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Preparing business reports and presentation outlines
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Distinguishing correlation from causation
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Reviewing AI-generated conclusions for accuracy and bias
Responsible AI and Best Practices
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Data privacy and confidentiality
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Bias and fairness considerations
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Hallucinations and incorrect outputs
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Reproducibility and verification
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Documenting assumptions and limitations
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Human review and accountability in data-driven decisions
Learning Outcome
Students will use AI responsibly across the analytics
lifecycle and verify AI-generated outputs.
11Capstone Planning and Presentation
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Topics:
Project planning, data preparation, final presentation, and course
review
Capstone Planning and Data Preparation
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Selecting and understanding the business problem
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Defining project objectives and success criteria
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Identifying and acquiring suitable data
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Planning the analytics workflow
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Dividing the project into manageable tasks
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Identifying suitable opportunities for AI assistance
Final Presentation and Course Review
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Presenting the business problem and analytical approach
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Demonstrating the analysis and visualizations
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Explaining key findings and recommendations
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Demonstrating where and how AI was used
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Answering questions and receiving feedback
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Reviewing the complete data analytics workflow
Learning Outcome
Students will plan an end-to-end analytics project, integrate
the skills learned, and present it.
