NLCI Animation & Development

Data Analytics with AI – Complete Course Syllabus

Industry-oriented curriculum with practical training, AI tools, projects, and career preparation.

This syllabus is designed for NLCI Animation & Development to train students from beginner to job-ready level in Data Analytics, using industry-relevant tools and hands-on projects.

Recommended duration

10–12 months

Suggested training hours

300–360 hours

Level

Beginner to Advanced

Training mode

Practical lab, assignments, projects, and AI-assisted learning

Complete module-wise syllabus

Module 1: Introduction to Data Analytics

10 hours

• Introduction to data analytics and its applications
• Types of data: structured and unstructured
• Data analytics lifecycle and workflow
• Data analyst roles and responsibilities
• Introduction to business intelligence
• Overview of Excel, SQL, Python, Power BI and Tableau

Module 2: Mathematics and Statistics

20 hours

• Mean, median, mode and range
• Variance and standard deviation
• Percentages, ratios and proportions
• Probability fundamentals
• Correlation and regression basics
• Sampling, distributions and hypothesis testing fundamentals
• Statistical interpretation of business data

Module 3: Advanced Excel with AI

35 hours

• Spreadsheet fundamentals and data formatting
• Logical, lookup, text and date functions
• IF, SUMIFS, COUNTIFS, XLOOKUP and INDEX-MATCH
• Sorting, filtering and data validation
• Conditional formatting and duplicate removal
• PivotTables, PivotCharts and slicers
• Power Query for data cleaning and transformation
• MIS reports and interactive dashboards
• AI-assisted formula generation and error checking
• Introduction to Microsoft Copilot features

Module 4: SQL and Database Management

40 hours

• Database concepts and relational databases
• Tables, keys, relationships and constraints
• SELECT, WHERE, ORDER BY and DISTINCT
• Aggregate functions and GROUP BY
• HAVING, CASE and NULL handling
• INNER, LEFT, RIGHT and FULL JOIN concepts
• Subqueries and Common Table Expressions (CTEs)
• String, date and window functions
• Views, indexes and query optimization basics
• Connecting SQL databases to Excel and Power BI
• AI-assisted query writing and debugging

Module 5: Python Programming for Data Analytics

50 hours

• Python installation and development environments
• Variables, data types and operators
• Conditional statements and loops
• Functions, modules and exception handling
• Lists, tuples, dictionaries and sets
• File handling and CSV operations
• NumPy arrays and numerical operations
• Pandas Series and DataFrames
• Data filtering, sorting, grouping and merging
• Handling missing values and duplicate records
• Matplotlib and Seaborn visualizations
• Exploratory Data Analysis (EDA)
• AI-assisted Python coding and debugging

Module 6: Data Cleaning and Preparation

20 hours

• Data collection and importing datasets
• Identifying inconsistent and invalid values
• Missing-value treatment and duplicate removal
• Data type conversion and standardization
• Outlier detection and treatment
• Data transformation and feature creation
• Data quality checks and validation
• Combining multiple data sources

Module 7: Power BI Desktop

40 hours

• Introduction to business intelligence and Power BI
• Importing Excel, CSV, SQL and other supported data
• Power Query Editor and data transformation
• Data modeling and relationships
• Star schema fundamentals
• DAX measures, calculated columns and common functions
• Charts, cards, matrices, maps and KPI visuals
• Filters, slicers, drill-through and tooltips
• Interactive dashboard design
• Power BI Service, publishing and sharing
• AI-assisted insights and Copilot features where available

Module 8: Tableau Data Visualization

20 hours

• Tableau interface and data connections
• Dimensions, measures and data types
• Filters, sorting and groups
• Calculated fields and parameters
• Charts, maps and time-series analysis
• Dashboards and stories
• Interactive visual analytics
• Publishing and sharing visualizations

Module 9: Business Intelligence and Reporting

15 hours

• KPI identification and measurement
• Sales, finance and operations reporting
• Trend and variance analysis
• Month-on-month and year-on-year comparisons
• Dashboard layout and visual storytelling
• Business insights and recommendations
• Report documentation and presentation

Module 10: AI Tools for Data Analytics

15 hours

• Using generative AI assistants for analytics tasks
• Prompt engineering for data analysis
• AI-generated Excel formulas and SQL queries
• Python code generation and debugging
• Automated report summaries and insight generation
• AI-assisted chart selection and interpretation
• Data privacy, accuracy checks and responsible AI use
• Introduction to predictive analytics and forecasting

Module 11: Real-World Projects and Case Studies

30 hours

• Sales performance analysis using Excel
• Student or customer database analysis using SQL
• Exploratory data analysis using Python
• Business dashboard using Power BI
• Interactive visual report using Tableau
• Data cleaning and integration project
• End-to-end analytics capstone project
• Project documentation and presentation

Module 12: Career Preparation and Assessment

10 hours

• Resume and portfolio development
• GitHub or project portfolio fundamentals
• Data analytics interview questions
• Excel, SQL and Python practical tests
• Business case study practice
• Mock interviews and presentation skills

Course duration summary

Module

Hours

1. Introduction to Data Analytics

10

2. Mathematics and Statistics

20

3. Advanced Excel with AI

35

4. SQL and Database Management

40

5. Python for Data Analytics

50

6. Data Cleaning and Preparation

20

7. Power BI Desktop

40

8. Tableau

20

9. Business Intelligence and Reporting

15

10. AI Tools for Analytics

15

11. Projects and Case Studies

30

12. Career Preparation

10

Total

305 hours

Practical assessment and certification

• Module-wise assignments and lab exercises
• Practical examinations in Excel, SQL, Python and Power BI
• Minimum one end-to-end capstone project
• Project presentation and final assessment
• Certificate of course completion, subject to institute requirements

Recommended for NLCI: Deliver this as a 10–12 month program with approximately 305 hours of structured training, adjusted for student practice, revision and project mentoring. The course covers the core tools employers commonly use for entry-level data analytics roles; it does not by itself guarantee a job or professional certification.