Data Analytics with AI: 82-Hour Master Training in Excel, SQL, Power BI & Python at ₹7994
Trained by working IT professionals from leading companies
82 HOUR Data Analytics with AI SYLLABUS
Accelerate your career as a versatile Data Analyst. This all-in-one program covers Microsoft Excel (Beginner to Advanced),
SQL database querying, Microsoft Power BI interactive business intelligence, Python programming fundamentals,
and Python data analytics with Pandas, NumPy, and Matplotlib — enabling you to transform complex data into impactful business intelligence.
Fee: ₹7994
Training Mode: Instructor-led live class
Duration: 82 hours
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What you will learn from this course?
- Module 1: Microsoft Excel - Beginner Foundations
- Excel Interface & Navigation
- Ribbon, Tabs, Groups, Worksheets, Formula Bar, Name Box
- Navigating large workbooks using keyboard shortcuts and Go To
- Workbook file types (.xlsx, .csv) and compatibility
- Data Entry & Cell Formatting
- Data types: text, numbers, dates, times
- AutoFill series (numbers, dates, custom lists) and flash copying
- Cell styling: fonts, borders, fills, horizontal & vertical alignment, wrap text, merge & center
- Number formatting: currency, percentage, date/time, custom codes
- Basic Formulas & Cell Referencing
- Formula structure and order of operations (PEMDAS/BODMAS)
- Relative, Absolute (`$A$1`), and Mixed references
- Essential functions: `SUM()`, `AVERAGE()`, `COUNT()`, `MAX()`, `MIN()`
- Data Organization & Management
- Single and multi-level sorting
- AutoFilter, text filters, number filters, date filters
- Find & Replace with wildcards
- Freeze Panes (top row, first column, custom selections)
- Text functions: `CONCATENATE`, `LEFT`, `RIGHT`, `MID`, `LEN`, `TRIM`, `UPPER`, `LOWER`
- Date functions: `TODAY()`, `NOW()`, `DAY()`, `MONTH()`, `YEAR()`, date arithmetic
- Data validation and drop-down lists
- Module 2: Microsoft Excel - Advanced Analysis & Automation
- Advanced Lookup & Logical Formulas
- `IF()`, Nested `IF()`, `IFS()`, `AND()`, `OR()`, `NOT()`
- `VLOOKUP()`, `HLOOKUP()`, and next-gen `XLOOKUP()`
- Dynamic lookups with `INDEX()` and `MATCH()`
- Error handling functions: `IFERROR()`, `IFNA()`
- Text conversion: `TEXT()`, `VALUE()`, `INDIRECT()`, `OFFSET()`
- Pivot Tables & Pivot Charts
- Creating Pivot Tables, arranging rows, columns, values, filters
- Grouping dates, numbers, and text
- Calculated fields and calculated items
- Interactive slicing with Slicers and Timelines
- Building dynamic Pivot Charts and summary reports
- Data Analysis Tools
- What-If Analysis: Goal Seek, Scenario Manager, Data Tables
- Optimization using Excel Solver
- Descriptive statistics using the Data Analysis ToolPak
- Data Cleaning & Power Tools
- Removing duplicates, Text-to-Columns, Flash Fill
- Handling errors and cleaning whitespace with `TRIM` and `CLEAN`
- Introduction to Power Query in Excel for loading and shaping data
- Introduction to Macros & VBA: Recording, running, and button assignment
- Module 3: Database Querying & Management - SQL
- Relational Database Concepts & MySQL Setup
- Data Types, Constraints (`PRIMARY KEY`, `FOREIGN KEY`, `NOT NULL`, `UNIQUE`, `CHECK`)
- Data Definition Language (DDL): `CREATE`, `ALTER`, `DROP`, `TRUNCATE`
- Data Manipulation Language (DML): `INSERT`, `UPDATE`, `DELETE`, `SELECT`
- Data Filtering & Search: `WHERE`, `LIKE`, `IN`, `BETWEEN AND`, `IS NULL`
- Sorting & Pagination: `ORDER BY` (ASC/DESC), `LIMIT`
- Aggregate Functions: `COUNT()`, `SUM()`, `AVG()`, `MIN()`, `MAX()`
- Data Grouping & Summarization: `GROUP BY`, `HAVING`
- Table Joins: `INNER JOIN`, `LEFT JOIN`, `RIGHT JOIN`, `CROSS JOIN`, `UNION`
- Subqueries and nested analytical queries for business reports
- Module 4: Business Intelligence & Dashboards - Microsoft Power BI
- Power BI Desktop Fundamentals
- Overview of Business Intelligence architecture
- Navigating Power BI Desktop workspace: Report, Data, and Model views
- Connecting to diverse data sources: Excel, CSV, SQL databases, Web
- Power Query for Data Transformation
- Power Query Editor workflows and applied transformation steps
- Cleaning, splitting, merging, appending, and pivoting/unpivoting columns
- Handling missing values, data type errors, and custom column creation
- Data Modeling & DAX (Data Analysis Expressions)
- Star schema design, building and managing table relationships (1-to-many, many-to-many)
- Calculated Columns vs. DAX Measures
- Essential DAX functions: `SUM`, `CALCULATE`, `RELATED`, `FILTER`, `DIVIDE`, time intelligence functions
- Interactive Visualizations & Reports
- Creating Bar, Column, Line, Area, Pie, Matrix, and KPI visuals
- Interactive filtering using Slicers, Visual-level, Page-level, and Report-level filters
- Drill-through actions, tooltips, and dynamic dashboard interactions
- Publishing reports to Power BI Service and sharing dashboards
- Module 5: Programming for Data Analytics - Python
- Python Setup, Jupyter Notebook & Basic Syntax
- Variables, Data Types (Integers, Floats, Strings, Booleans), Type Conversion
- Operators, Mathematical Expressions, String Formatting with f-strings
- Control Flow: Conditional logic (`if-elif-else`) and Looping (`for`, `while`)
- Data Structures: Lists, Tuples, Sets, Dictionaries
- User-Defined Functions, Parameters, and Return Values
- Module 6: Python Data Analytics & AI-Driven Insights (NumPy, Pandas, Matplotlib)
- NumPy for Array Mathematics
- Array creation, indexing, slicing, and reshaping
- Mathematical and statistical operations: Mean, Median, Mode, Standard Deviation, Variance
- Pandas for Data Manipulation & Wrangling
- Pandas Series and DataFrames
- Importing structured datasets from CSV, Excel, and SQL
- Handling missing data, duplicate rows, data type fixing, and transformations
- Data aggregation, grouping, and multi-variable filtering
- Matplotlib for Data Visualization
- Creating line graphs, scatter plots, bar charts, histograms, and pie charts
- Plot customization: titles, labels, legends, grids, and multi-chart subplots
- AI-Driven Analytics & Automated Insights
- Introduction to automated data analysis and pattern recognition
- Predictive trend analysis and AI-assisted dashboard generation
- Comprehensive Business Projects & Case Studies
- Project 1: Advanced Excel Financial Modeling & Sales Dashboard
- Project 2: SQL Business Intelligence & Revenue Analytics Report
- Project 3: Interactive Power BI Executive Dashboard
- Project 4: Python End-to-End Exploratory Data Analysis (EDA) Capstone
