Data Analytics Professional Course
Master Data Analytics with Python, SQL, Excel, Tableau, and Power BI. Learn data cleaning, visualization, and storytelling to become a job-ready data analyst.
📈 Your Market Value After This Course
Salary ranges are indicative estimates and vary by location, employer, experience, and qualifications. They are not guaranteed earnings or placement outcomes.
Fresher / Entry Level
0-2 years experience
- Junior Data Analyst
- BI Trainee
Mid Level
2-5 years experience
- Data Analyst
- Business Intelligence Analyst
Senior / Expert Level
5+ years experience
- Senior Data Analyst
- Analytics Manager
🎯 Job Roles You Can Apply For
⚡ Skills You'll Master
📚 Complete Course Syllabus
Master every aspect with our comprehensive curriculum
Module 1: Introduction to Data Analytics
- What is Data Analytics - Importance & Scope
- Types of Data Analytics - Descriptive, Diagnostic, Predictive, Prescriptive
- Data Analytics Lifecycle
- Data Analyst Roles & Responsibilities
- Tools Overview - Excel, SQL, Python, Power BI, Tableau
Module 2: Excel for Data Analytics
- Excel Basics - Formulas, Functions, Referencing
- Data Cleaning in Excel - Remove Duplicates, Text to Columns, Find/Replace
- Conditional Formatting & Data Validation
- Pivot Tables & Pivot Charts
- Advanced Excel Functions - VLOOKUP, INDEX-MATCH, IF, SUMIFS
- Power Query for Data Transformation
- Dashboards in Excel
- Macros & VBA Basics
Module 3: SQL for Data Analysis
- Introduction to Databases & SQL
- SQL Basics - SELECT, FROM, WHERE
- Filtering Data - AND, OR, IN, BETWEEN, LIKE
- Sorting & Grouping - ORDER BY, GROUP BY, HAVING
- Joins - INNER, LEFT, RIGHT, FULL, CROSS JOIN
- Subqueries & CTEs (Common Table Expressions)
- Aggregate Functions - COUNT, SUM, AVG, MIN, MAX
- Window Functions - ROW_NUMBER, RANK, LEAD, LAG
- Date & String Functions
- Views & Stored Procedures
Module 4: Python for Data Analytics
- Python Basics - Variables, Data Types, Operators
- Control Flow - if-else, Loops (for, while)
- Functions & Lambda Expressions
- Data Structures - Lists, Tuples, Dictionaries, Sets
- List Comprehensions & Generators
- File Handling - Reading/Writing CSV, JSON, Excel
- Error Handling & Debugging
Module 5: Data Manipulation with Pandas
- Introduction to Pandas - Series & DataFrame
- Reading Data - CSV, Excel, JSON, SQL
- Data Inspection - head, tail, info, describe
- Data Cleaning - Handling Missing Values (dropna, fillna)
- Handling Duplicates & Outliers
- Data Transformation - apply, map, replace
- Filtering & Sorting Data
- Grouping & Aggregation - groupby, agg, pivot_table
- Merging, Joining, Concatenating DataFrames
- Time Series Analysis
Module 6: Data Visualization with Matplotlib & Seaborn
- Introduction to Data Visualization
- Matplotlib - Line, Bar, Scatter, Histogram
- Customizing Plots - Labels, Titles, Legends, Colors
- Subplots & Multiple Plots
- Seaborn - Statistical Visualizations
- Heatmaps, Pairplots, Boxplots
- Plotly - Interactive Visualizations
Module 7: Power BI - Business Intelligence
- Introduction to Power BI - Components
- Connecting to Data Sources - Excel, SQL, Web, Cloud
- Power Query - Data Transformation & Cleaning
- Data Modeling - Relationships, Calculated Columns
- DAX (Data Analysis Expressions) - CALCULATE, FILTER, SUMX
- Creating Visuals - Charts, Maps, Cards, Slicers
- Interactive Dashboards & Reports
- Power BI Service - Publishing & Sharing
- Power BI Mobile & Embedding
Module 8: Tableau - Data Visualization
- Introduction to Tableau - Products & Features
- Connecting to Data Sources
- Data Preparation & Cleaning in Tableau
- Worksheets & Dashboards
- Chart Types - Bar, Line, Scatter, Map, Pie
- Calculated Fields & Parameters
- Filters, Sets, Groups, Hierarchies
- Tableau Public vs Desktop vs Server
- Storytelling with Tableau
Module 9: Statistics for Data Analytics
- Descriptive Statistics - Mean, Median, Mode, Variance, Std Dev
- Measures of Spread - Range, IQR, Percentiles
- Probability Distributions - Normal, Binomial, Poisson
- Correlation & Covariance
- Hypothesis Testing - t-test, Chi-square, ANOVA
- Regression Analysis - Linear & Logistic Regression
- Sampling Techniques & Confidence Intervals
Module 10: Data Storytelling & Reporting
- What is Data Storytelling
- Components of a Data Story
- Dashboard Design Principles
- Choosing Right Visualizations
- Creating Executive Summaries
- Presenting Data to Stakeholders
Module 11: Real-World Projects
- Project 1: Sales Data Analysis using Excel & Pivot Tables
- Project 2: Customer Data Analysis using SQL
- Project 3: Data Cleaning & Analysis with Python Pandas
- Project 4: Interactive Dashboard in Power BI
- Project 5: Sales Performance Dashboard in Tableau
- Capstone Project - Complete Data Analytics Report
⭐ Why Choose Viroha Academy?
Explore the learning support and course features listed below.
Expert Trainers
Learn from industry professionals with 10+ years of Data Analytics experience
Hands-on Projects
Work on 7+ real-time analytics projects with real datasets
Industry Certification
Get globally recognized Data Analytics certification
100% Placement Support
Tie-ups with 400+ companies for analytics roles
Resume Building
Professional resume & portfolio with analytics projects
Mock Interviews
Regular mock interviews with detailed feedback
💻 Real-Time Projects
Build impressive portfolio with industry-relevant projects
Sales Performance Analysis
Analyze sales data to identify trends, top products, regional performance, and create interactive dashboards.
Customer Segmentation Analysis
Perform customer segmentation using Python to identify customer groups and behavior patterns.
Financial Data Dashboard
Create a comprehensive financial dashboard in Power BI/Tableau for KPI tracking and reporting.
🚀 Career Support
Contact Viroha Academy to discuss the career guidance available for this course.
Ask our team about course-specific career preparation and available support.
Contact Viroha AcademyOur Top Hiring Partners
❓ Frequently Asked Questions
Got questions? We've got answers
🚀 Ready to Start Your Data Analytics Journey?
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