AI Data Analytics Professional Program

Excel Fundamentals & Data Preparation

Explore data categories and types, apply XLOOKUP, SUMIFS, COUNTIFS, AVERAGEIFS, and MAXIFS, import data from multiple sources, clean and transform datasets, and automate formula generation and data preparation using GenAI tools.

Data Summarization & Pivot Analysis

Organize and summarize data with PivotTables and PivotCharts, apply sorting, filtering, grouping, and slicers for interactive analysis, and generate actionable insights using AI-assisted analytics tools.

Data Visualization

Create effective charts including bar, column, line, pie, scatter, histogram, box plot, and line charts; select appropriate visualizations for different business scenarios; generate AI-assisted charts and automated visual insights.

Descriptive Statistics & Data Exploration

Measure central tendency using mean, median, and mode; analyze data spread using range, IQR, variance, standard deviation, and skewness; measure relationships using Pearson correlation; and generate statistical summaries and interpretations with GenAI.

Outlier Detection & Data Quality

Detect and interpret outliers using statistical methods; apply IQR and z-score–based outlier treatments; and improve data quality using AI-assisted data validation and preprocessing.

Data Storytelling & Business Communication

Combine data, visuals, and narratives to communicate insights; perform KPI driven analysis; develop compelling business stories supported by data; and generate AI-assisted narratives and executive summaries.

Dashboard Design & Reporting

Design interactive Excel dashboards using PivotTables, charts, and slicers; present KPIs and business metrics effectively; and build AI-assisted dashboards and decision-ready reports using ChatGPT and other GenAI tools.

Data Extraction & Power Query

Extract data from Excel, CSV, TXT, PDF, databases, and web sources; apply ETL (Extract, Transform, Load) techniques using Power Query; clean, transform, standardize, and prepare data for analysis; and automate data preparation using AI-assisted workflows.

Data Modeling

Design efficient data models using Star and Snowflake schemas; build relationships, hierarchies, and date tables; apply merge, append, pivot, and unpivot operations; and optimize models for performance, scalability, and data quality.

DAX for Business Analytics

Create calculated columns and measures using DAX; apply aggregation, filtering, and relationship functions; build advanced business calculations; and generate and validate DAX formulas using AI assistants.

Data Visualization & Storytelling

Create impactful charts and interactive visualizations; design KPI-focused reports and business dashboards; apply data storytelling techniques to communicate insights; and generate AI-assisted narratives and business summaries.

Interactive Dashboards & Reporting

Build interactive dashboards using slicers, filters, drill-through, bookmarks, and mobile layouts; design executive-ready reports for business stakeholders; and publish, share, and manage Power BI reports securely.

AI-Powered Business Intelligence

Leverage analytics and Power BI AI capabilities; use Copilot and GenAI tools for DAX, dashboards, and insight discovery; apply AI-generated narratives, anomaly detection, and key influencer analysis; accelerate AI-assisted decision-making.

Advanced Business Analytics

Define and monitor business KPIs; analyze trends, customer behavior, and business performance; and develop decision-ready insights for business stakeholders.

Relational Databases & SQL Fundamentals

Understand relational database concepts, tables, keys, and relationships; set up MySQL; retrieve data using SQL queries with filtering, sorting, grouping, and aggregation; and apply built-in SQL functions with AI-assisted query generation.

Data Retrieval & SQL Analytics

Write SQL queries using SELECT, WHERE, GROUP BY, HAVING, ORDER BY, and CASE; apply string, date, and aggregate functions; generate analytical reports and summaries; and debug and optimize queries using AI assistants.

Multi-Table Queries & Advanced SQL

Combine data using INNER, LEFT, RIGHT, CROSS, and SELF JOINs; apply UNION, Common Table Expressions (CTEs), subqueries, and window functions; solve analytical problems; and generate AI-assisted SQL queries and solutions.

Database Design & Schema Management

Design Entity-Relationship (ER) diagrams; apply normalization (1NF, 2NF, and 3NF) to create efficient database structures; create and manage database objects using DDL commands; and validate schemas using AI-assisted tools.

Data Management & Database Programming

Insert, update, delete, and clean data using DML; create views, indexes, stored procedures, user-defined functions, and triggers; automate database operations; and optimize SQL code using AI-assisted recommendations.

Database Security & Transaction Management

Apply transaction management using COMMIT, ROLLBACK, and SAVEPOINT; understand ACID properties and database locking; manage database security using roles, permissions, and DCL commands; and ensure data consistency.

SQL Analytics & AI-Augmented Workflows

Handle semi-structured JSON data using SQL; build SQL-based ETL workflows and reporting pipelines; automate SQL generation, optimization, debugging, and documentation using GenAI tools; and develop AI-assisted analytics workflows.

Python Programming Fundamentals

Understand Python syntax, variables, data types, and operators; write programs using input, output, expressions, and type conversions; develop logical problem-solving skills through programming exercises and practical coding tasks.

Decision Making & Iterative Programming

Implement decision-making using if, elif, and else statements; build iterative programs using for and while loops; apply nested loops and loop control statements to solve business problems efficiently and accurately.

Functions & Modular Programming

Create reusable programs using user-defined functions; work with parameters, return values, local and global variables; apply modules and lambda functions to build modular, reusable, and efficient Python applications.

Data Structures & Data Manipulation

Manipulate strings using indexing and slicing; store and organize data using lists, tuples, sets, and dictionaries; perform searching, updating, sorting, aggregation, and data retrieval operations efficiently in Python.

Object-Oriented Programming (OOP)

Build applications using classes and objects; implement constructors, instance variables, class variables, and methods; apply object-oriented principles to create structured, reusable, and maintainable Python programs.

Exception Handling & File Operations

Handle runtime errors using exception handling techniques; read, write, append, and manage files using Python; build robust programs with effective error handling and reliable file management for business applications.

AI-Assisted Python Development

Use Colab Gemini for code generation and debugging; generate Python code with GitHub Copilot in Visual Studio Code; and interpret errors, optimize code, and improve productivity with AI-assisted workflows.

Data Preparation with NumPy & Pandas

Prepare and structure data using NumPy arrays, Pandas Series, and DataFrames; combine, merge, reshape, and summarize datasets; and handle missing values, duplicates, and inconsistent data for accurate business analysis.

Data Wrangling & Data Management

Perform CRUD operations on DataFrames; clean, transform, and prepare data for analysis; create derived features; and optimize datasets for analytics using efficient data wrangling and management techniques.

Data Visualization

Create effective visualizations using Matplotlib, Seaborn, and Plotly; select appropriate charts to explore trends, distributions, and relationships; and develop visuals that communicate business insights effectively.

Descriptive Statistics & Data Analysis

Apply descriptive statistical techniques including measures of central tendency, dispersion, skewness, kurtosis, and correlation; summarize datasets; and generate statistical insights to support business decisions.

Python & SQL Integration

Connect Python applications to relational databases; retrieve, manipulate, and analyze SQL data using Python; and build integrated analytics workflows by combining SQL queries with Python-based data processing.

Exploratory Data Analysis (EDA)

Perform structured univariate, bivariate, and multivariate analysis; identify patterns, trends, correlations, and anomalies; and apply best practices to conduct systematic exploratory data analysis for business insights.

AI-Assisted Exploratory Analytics

Accelerate exploratory data analysis using AI-powered profiling and visualization tools; generate AI-assisted code, visualizations, and insights; and validate outputs for reliable business analytics workflows.

Probability & Statistical Thinking

Apply probability concepts to solve real-world business problems; understand simple, joint, and conditional probability, permutations, and combinations; and apply Bayesian thinking for data-driven reasoning and decision-making.

Sampling & Data Distribution

Apply sampling techniques including simple random, stratified, systematic, and cluster sampling; understand normal distribution and the Central Limit Theorem; and interpret sampling distributions to assess data reliability.

Inferential Statistics & Hypothesis Testing

Formulate null and alternative hypotheses; perform Z-tests and T-tests to validate business assumptions; interpret p-values, confidence levels, and statistical significance; and apply AI-assisted statistical interpretation for decisions.

Regression & Predictive Analytics

Build and interpret simple linear regression models; analyze relationships between variables using regression techniques; and apply regression for prediction, forecasting, and data-driven business decision-making.

Business Analytics with Statistics

Prepare analytical datasets for predictive modelling; create statistical visualizations and communicate analytical findings; and interpret statistical outputs to generate actionable business insights for informed decision-making.

AI-Assisted Statistical Analysis

Leverage GenAI tools to interpret statistical results and explain analytical findings; validate AI-generated statistical interpretations using analytical reasoning; and improve decision-making through AI-assisted statistical analysis.

Prompt Engineering for Analytics

Design effective prompts for data preparation, analysis, visualization, and reporting; apply prompt engineering to improve AI output quality and accuracy; and automate common analytics tasks and workflows using Generative AI.

AI Validation & Responsible AI

Critically evaluate AI-generated code, analyses, and business insights; validate outputs using analytical reasoning and domain knowledge; and apply responsible AI practices including bias awareness, transparency, and human oversight.

Experimentation & Behavioral Analytics

Apply A/B testing concepts to compare business outcomes; perform funnel and cohort analysis to understand customer behavior and retention; and interpret experimentation results to support data-driven business decisions.

LLM Integration & AI-Augmented Analytics

Integrate Gemini API with Python applications for AI-assisted analytics; automate coding, data analysis, visualization, and insight generation using LLMs; and develop AI-augmented analytics workflows with executive-ready business narratives.

Business Problem Definition & Project Planning

Identify and define real-world business problems; establish analytical objectives, business questions, and hypotheses; select an industry-focused capstone project; and plan an end-to-end analytics approach for execution.

Data Preparation & Analytics Foundation

Acquire, clean, transform, and organize business datasets; apply version control and structured data management practices; and prepare analysis-ready datasets using AI-assisted workflows for reliable business analytics.

End-to-End Data Analysis

Perform analysis using Excel, SQL, Python, and Power BI; conduct exploratory data analysis, KPI analysis, and descriptive analytics; and integrate insights from multiple analytics tools into a unified business solution.

Statistical Validation & Predictive Analytics

Apply hypothesis testing to validate business assumptions; build and interpret simple linear regression models; and evaluate analytical findings using statistical evidence and business reasoning for confident decisions.

Dashboard Development & Business Storytelling

Develop interactive dashboards and executive reports; communicate analytical findings through effective data storytelling; and present actionable recommendations using business-focused visualizations for informed decision-making.

AI-Augmented Analytics Workflow

Leverage ChatGPT, Gemini, GitHub Copilot, YData Profiling, Sweetviz, and AutoViz to accelerate analytics; validate AI outputs using analytical reasoning; and apply responsible AI throughout the analytics lifecycle.

Executive Communication & Project Showcase

Prepare professional project documentation and executive presentations; defend analytical methodology, findings, and recommendations during the capstone showcase; and demonstrate end-to-end analytics competency through a portfolio project.

Project Presentation & Stakeholder Evaluation

Present and demonstrate the end-to-end project workflow, outcomes, and key learnings to stakeholders for assessment of functional completeness, integration depth, and design quality.