Part 1:- Excel (16 Hrs)

Excel Basic

• Interface navigation and workbook management
• Basic formulas (SUM, AVERAGE, COUNT, MAX, MIN)
• Cell referencing (relative, absolute, mixed)
• Basic formatting and data entry

Advanced Functions

• Logical functions (IF, AND, OR, NOT, IFERROR, IFS)
• Lookup functions (VLOOKUP, HLOOKUP, XLOOKUP, INDEX, MATCH)
• Text functions (CONCATENATE, TEXTJOIN, LEFT, RIGHT, MID, TRIM)
• Date & Time functions (TODAY, NOW, DATE, DATEDIF)
• Statistical functions (COUNTIF, SUMIF, AVERAGEIF, COUNTIFS, SUMIFS)

Data Cleaning & Preparation

• Removing duplicates
• Text to Columns
• Find & Replace
• Data Validation
• Flash Fill
• Handling missing data

Data Analysis Tools

• Pivot Tables & Pivot Charts
• Conditional Formatting
• Advanced Sorting & Filtering
• Subtotals and Grouping
• What-If Analysis (Goal Seek, Scenario Manager, Data Tables)
• Power Query (Get & Transform)

Data Visualization

• Chart types and when to use them
• Creating effective dashboards
• Slicers and Timelines
• Sparklines
• Custom charts

Reporting & Dashboard

• How to Report & Build Dashboard

Part 2:- Python (40 Hrs)

Python Fundamentals

• Installation (Python, Anaconda, Jupyter Notebook)
• Variables and data types
• Operators (arithmetic, comparison, logical)
• Strings and string methods
• Lists, tuples, sets, dictionaries
• Conditional statements (if, elif, else)
• Loops (for, while)
• Functions and lambda functions
• Error handling (try, except)

NumPy Library

• NumPy arrays vs Python lists
• Array creation and indexing
• Array operations and broadcasting
• Mathematical functions
• Statistical operations
• Reshaping and manipulating arrays

Pandas Library

• Series and DataFrames
• Reading data (CSV, Excel, JSON)
• Data inspection (head, tail, info, describe)
• Indexing and selecting data (loc, iloc)
• Filtering and sorting
• Handling missing values
• Data type conversions
• Adding and removing columns/rows
• GroupBy operations
• Merging, joining, and concatenating DataFrames
• Pivot tables and cross-tabulations
• Applying functions to data
• String operations

Data Cleaning with Python

• Identifying and handling duplicates
• Dealing with missing data (dropna, fillna)
• Data type conversions
• Renaming columns
• String cleaning and normalization
• Outlier detection and handling

Exploratory Data Analysis (EDA)

• Descriptive statistics
• Correlation analysis
• Distribution analysis
• Frequency tables

Data Visualization with Matplotlib & Seaborn

• Line plots, bar charts, histograms
• Scatter plots and box plots
• Heatmaps and pair plots
• Customizing plots (labels, titles, legends)
• Subplots and multiple visualizations
• Seaborn styling and themes

Reporting

• How to Report
• Make a Fully Notebook

Part 3:- Mathematics (16 Hrs)

Linear Algebra (Core Topics)

Vectors & Operations

• Vector addition, subtraction, scalar multiplication
• Dot product and vector norms
• Vector projections
• Linear independence and basis vectors

Matrices & Operations

• Matrix multiplication and transpose
• Identity and inverse matrices
• Determinants
• Matrix rank

Systems of Linear Equations

• Gaussian elimination
• Solutions types (unique, infinite, no solution)

Statistics (Core Topics)

Descriptive Statistics

• Mean, median, mode
• Variance and standard deviation
• Percentiles and quartiles
• Skewness and kurtosis
• Box plots and outlier detection

Probability Distributions

• Normal distribution and Z-scores
• Binomial distribution
• Poisson distribution
• Uniform distribution
• Exponential distribution

Sampling & Central Limit Theorem

• Sampling distributions
• Standard error
• Central Limit Theorem

Confidence Intervals

• Confidence intervals for means
• Confidence intervals for proportions
• Margin of error

Hypothesis Testing

• Null and alternative hypotheses
• P-values and significance levels
• Type I and Type II errors
• Z-tests and t-tests
• Chi-square tests
• ANOVA (one-way)

Correlation & Regression

• Pearson correlation coefficient
• Spearman rank correlation
• Simple linear regression
• Multiple linear regression
• R² and adjusted R²
• Residual analysis
• Assumptions of regression

A/B Testing

• Hypothesis setup
• Sample size calculation
• Statistical vs practical significance

Probability (Core Topics)

Probability Fundamentals

• Sample space and events
• Probability rules (addition, multiplication)
• Complement rule

Conditional Probability

• Conditional probability formula
• Independence of events
• Bayes' Theorem
• Applications (false positives, diagnostic tests)

Random Variables

• Discrete vs continuous random variables
• Expected value (mean)
• Variance and standard deviation
• Probability distributions (PMF, PDF, CDF)

Common Distributions

• Bernoulli and Binomial
• Poisson
• Normal (Gaussian)
• Exponential

Derivatives

• Basic derivative rules
• Chain rule
• Partial derivatives
• Gradient (for optimization)

Optimization

• Finding maxima and minima
• Critical points
• Gradient descent basics

Integrals

• Definite and indefinite integrals
• Area under curves
• Applications in probability (PDF integration)

Part 4 :- SQL(30 Hrs)

SQL Fundamentals

• What is SQL and databases
• Database structure (tables, rows, columns)
• Data types
• Installing SQL environment (MySQL, PostgreSQL, or SQL Server)

Basic Queries

• SELECT statement
• WHERE clause and filtering
• DISTINCT keyword
• ORDER BY (ASC, DESC)
• LIMIT/TOP
• Comparison operators (=, !=, >, <, >=, <=)
• Logical operators (AND, OR, NOT)
• IN, BETWEEN, LIKE operators
• IS NULL, IS NOT NULL

Aggregate Functions

• COUNT, SUM, AVG, MAX, MIN
• GROUP BY clause
• HAVING clause
• Difference between WHERE and HAVING

SQL Joins

• INNER JOIN
• LEFT JOIN (LEFT OUTER JOIN)
• RIGHT JOIN (RIGHT OUTER JOIN)
• FULL OUTER JOIN
• CROSS JOIN
• SELF JOIN
• Multiple joins

Data Manipulation

• INSERT INTO
• UPDATE
• DELETE
• CREATE TABLE
• ALTER TABLE
• DROP TABLE

Advanced SQL Concepts

• Subqueries (nested queries)
• CASE statements
• UNION and UNION ALL
• Common Table Expressions (CTEs)
• String functions (CONCAT, SUBSTRING, UPPER, LOWER, TRIM)
• Date functions (DATEADD, DATEDIFF, DATEPART, FORMAT)
• Type casting and conversion

Window Functions (Advanced)

• ROW_NUMBER()
• RANK() and DENSE_RANK()
• PARTITION BY
• LAG() and LEAD()
• Running totals

Part 5 :- Power BI (18 Hrs.)

Power BI Introduction

• What is Power BI and its components
• Power BI Desktop vs Power BI Service
• Interface overview
• Installing Power BI Desktop

Connecting to Data Sources

• Importing data from Excel, CSV
• Connecting to databases (SQL Server, MySQL)
• Web data sources
• Other connectors

Power Query (Data Transformation)

• Power Query Editor interface
• Data cleaning operations
• Removing duplicates and errors
• Filtering and sorting
• Changing data types
• Splitting and merging columns
• Pivoting and unpivoting
• Appending and merging queries
• Adding custom columns
• Grouping data
• Applied steps management

DAX (Data Analysis Expressions)

• Calculated columns vs measures
• Basic DAX functions (SUM, AVERAGE, COUNT, MIN, MAX)
• CALCULATE and FILTER functions
• Time intelligence functions (TOTALYTD, SAMEPERIODLASTYEAR, DATEADD)
• ALL, ALLEXCEPT, ALLSELECTED
• RELATED and RELATEDTABLE
• Variables in DAX
• Iterator functions (SUMX, AVERAGEX)
• Conditional functions (IF, SWITCH)
• Text and date functions

Data Visualization

• Chart types in Power BI (bar, column, line, pie, donut)
• Cards and KPIs
• Tables and matrices
• Maps and filled maps
• Slicers and filters
• Gauges and waterfall charts
• Scatter plots and bubble charts
• Treemaps and funnels
• Custom visuals from marketplace
• Formatting and customization

Creating Interactive Dashboards

• Report design principles
• Page layout and themes
• Buttons and navigation
• Bookmarks
• Drill-through and drill-down
• Tooltips (custom tooltips)
• Sync slicers across pages
• Mobile layout