The total program duration depends on the batch schedule and learning pace. 
Course Duration & Schedule (based on pace): 

• 24 weeks — 4 days/week (16 hours/week) 

• 32 weeks — 3 days/week (12 hours/week) 
  Each sprint includes a 2-hour live mentor-led session and a 2-hour hands-on practice session. 

The duration may be extended due to holidays or unforeseen disruptions beyond NIIT’s control. 

The curriculum is designed to build job-ready analytical and GenAI-integrated skills through progressive, hands-on learning.

Learners cover:  

• Data Analytics and Storytelling using Excel: Data cleaning, visualization, PivotTables, and descriptive statistics for effective data communication. 

• Data Analytics and Managing Data using SQL: Database design, normalization, queries, triggers, and stored procedures using MySQL. 

• Data Analytics and Automation using Python: Core programming concepts, object-oriented programming (OOP), data handling, and Python libraries such as NumPy and Pandas, along with GenAI API integration. 

• Exploratory Data Analysis with Python and GenAI: Systematic EDA using Python, including univariate, bivariate, and multivariate analysis, with AI-assisted visualization and insights. 

• Advanced Data Visualization and BI with Power BI: ETL using Power Query, data modeling with DAX, and creation of interactive dashboards to drive decision-making. 

• Applied Statistical Analysis for Data Insights: Application of inferential statistics and regression techniques to identify patterns and derive meaningful business conclusions. 

• Applied Data Analytics – Capstone: A comprehensive, real-world project integrating all learned skills to present analytical solutions and insights to stakeholders. 

No prior coding or analytics background is required. The program is built for beginners and designed for employability. Learn step-by-step progressively to become a day-1 job-ready data analyst.  

Learners begin with tools like Excel and SQL before progressing to Python, Power BI, statistics, and Generative AI. Each concept is introduced through guided mentorship, practical assignments, and personalized feedback to ensure smooth progression from fundamentals to advanced applications. 

The program follows a “Learn by Doing” approach that progressively builds skills through real-world application, mentor-led guidance, and structured practice. 

Each learning sprint includes: 

• 2 hours of live, mentor-led online sessions focused on concept explanation and interactive problem-solving. 
• At least 2 hours of guided hands-on practice, where learners' complete assignments, apply techniques, submit assignments, and receive personalized feedback. 

The learning experience integrates: 

• Progressive – One task at a time: Do 100+ practice assignments designed to strengthen conceptual understanding through direct application. 
• Mentor support and personalized feedback — helping learners refine problem-solving and analytical thinking. 
• Industry-oriented capstone project — demonstrating readiness for professional data roles. 
• Flexible scheduling — batches may vary in duration (24 or 32 weeks) depending on the learner’s pace and schedule. 

Yes. The program culminates in a Capstone Project where learners apply all acquired skills — from data preparation, analysis, and visualization to storytelling and presentation — to solve a real-world business problem. The capstone demonstrates end-to-end analytical thinking, problem-solving ability, and professional presentation skills, preparing learners for real data-driven roles.

The learners take up five competency-focused projects. These projects equip learners to demonstrate course-level skills. They prepare learners to become confident before taking up the final capstone project.

This program is designed to build the core competencies i.e., the knowledge and skills required to perform the tasks required for the Data Analyst, Business Analyst, Business Intelligence Analyst, Marketing Analyst job roles at an entry level.  

Upon successful completion of the program, learners will be able to: 

• Analyze, clean, and visualize data using Excel, SQL, Python, and Power BI. 

• Conduct Exploratory Data Analysis (EDA) and apply statistical methods for inference, prediction, and decision-making. 

• Build and present interactive dashboards and data stories that communicate insights effectively. 

• Utilize Generative AI tools to boost productivity and efficiency. 

• Execute a Capstone Project showcasing end-to-end analytical and problem-solving capabilities. 

Learners must have a system that supports online learning without disruption. 

System Requirements: 

• Hardware: Laptop/desktop with Intel i3 or AMD Ryzen 3 (or higher), 8 GB RAM, 50 GB of free space, functional webcam and microphone. 
• Software: Windows 10/macOS or higher, latest Web browser (Chrome or Edge), MS Office or equivalent, and a PDF reader. 
• Internet: Stable broadband/Wi-Fi with at least 5 Mbps speed; a backup connection is recommended. 
• Security: Updated antivirus and permission to install necessary tools or libraries.