Job Oriented Diploma in Data Analysis

Features

Duration Of Class

Power BI : 30 hours
SQL : 30 hours
Excel and Statistics: 30 hours
Projects: 30 hours - Minimum 2 Unguided projects

Live project

Live project or live industry case study for Data Analysis Project lifecycle.

Live Project: It will Cover the Business Issue Understanding, Data Understnding, Modeling, Preperation and Vizualization

Class Work Case Studies

During the training program, live case studies from the industry will be shared with student and guided projects, which will give the understanding of the industry.

Placement Policy

Placement support is a complimentory service provided to all Job oriented courses.

Expert Support

Support team available to help you with any technical queries you may have during the course.

Certification

Towards the end of the course, you will be working on a project. Techbodhi certifies you as a Data Analyst based on the project.

Excel
  • Introduction to Excel
    • Interface Overview
    • Data Entry and Shortcuts
  • Formulas and Functions
    • Arithmetic and Logical Functions
    • Lookup Functions (VLOOKUP, HLOOKUP)
    • Text and Date Functions
  • Data Analysis
    • Sorting and Filtering
    • Pivot Tables and Pivot Charts
  • Advanced Features
    • Conditional Formatting
    • Data Validation
    • Working with Macros and VBA Basics
  • Financial Modeling
    • Budgeting and Forecasting
    • Using Solver and Scenario Analysis
Statistics
  • Introduction to Statistics
    • Types of Data and Levels of Measurement
    • Sampling Methods and Data Collection
  • Descriptive Statistics
    • Measures of Central Tendency (Mean, Median, Mode)
    • Measures of Dispersion (Variance, Standard Deviation)
  • Probability Theory
    • Basic Probability Concepts
    • Probability Distributions (Normal, Binomial, etc.)
  • Hypothesis Testing
    • Null and Alternative Hypotheses
    • P-values and Confidence Intervals
  • Regression Analysis
    • Simple and Multiple Linear Regression
    • Correlation vs Causation
  • Time Series Analysis
    • Trend Analysis and Seasonality
    • Forecasting Techniques
SQL
  • Introduction to SQL
    • Basics of Databases and SQL
    • Types of SQL Commands (DDL, DML, DCL)
  • Data Querying
    • SELECT Statements, WHERE Clauses, and Aliases
    • Filtering Data with Conditions
    • Sorting and Ordering Results
  • Joins and Subqueries
    • INNER, LEFT, RIGHT, FULL Joins
    • Nested Queries and Correlated Subqueries
  • Data Aggregation
    • GROUP BY and HAVING Clauses
    • Aggregate Functions (SUM, AVG, COUNT, etc.)
  • Data Manipulation
    • INSERT, UPDATE, DELETE
    • Managing Transactions and Rollbacks
  • Database Design and Optimization
    • Indexing and Views
    • Normalization and Relationships
    • Query Optimization Techniques
Power BI -> Click Here for Detailed Power BI Syllabus
  • Introduction to Power BI
    • Overview of Power BI Desktop and Service
    • Installing Power BI Desktop
    • Interface and Navigation
  • Connecting to Data Sources
    • Importing Data
    • Connecting to Databases and Online Services
    • Importing from Web/API
  • Data Preparation with Power Query
    • Data Cleaning and Transformation
    • Merge and Append Queries
    • Advanced Editor and M Code Basics
  • Data Modeling
    • Understanding Relationships
    • Creating Measures with DAX
    • Common DAX Functions
  • Data Visualization
    • Visual Elements (Tables, Charts, Maps)
    • Using Filters, Slicers, and Drill-through
    • Building Interactive Dashboards
  • Sharing and Publishing
    • Publishing to Power BI Service
    • Sharing Reports and Dashboards
    • Setting up Gateways for Real-time Data
Real-World Projects (Customized per Batch)
  • Retail: Sales Analysis Dashboard to visualize revenue trends and identify top-performing products.
  • Finance: Budgeting and Expense Tracker with advanced DAX and forecasting.
  • HR: Employee Attrition Analysis to identify patterns in workforce turnover.
  • Healthcare: Patient Records Dashboard to monitor critical health KPIs.
  • Energy: Power Consumption Dashboard with peak-load analysis using IoT datasets.
  • Education: Student Performance Analysis to track academic outcomes.