Address
3rd Floor, 301-Bhawarkua Square, Indrapuri Main Rd, Above Canara Bank, Near Scholar's Career Academy, Indore, Madhya Pradesh 452001
Call Us
+91 9399200960
RoadMap to Become an Excellent Data Analyst
Masters Program in Data Analytics with GenAI
(Duration - 4 to 4.5 Months)
LEVEL-1
Python Programming(Basic to Advanced Level)
- Introduction to Python Basic
- Introduction to Python Object
- Python Data Types
- Conditional Statements
- Iterators
- Loops and its implementations
- Functional Programming
- Modular Programming
- Data Scientists tool pack
- Data Visualization using Matplotlib &Seaborn
- Python Code Debugging and Troubleshooting Techniques
- Python Code Optimization Techniques
- Data Analytics Project using Python
- Project
LEVEL-2
Business Decision Making using Statistics
- Introduction of Statistics in Businesses
- Random Variables, Populations & Samples
- Guesstimates for business planning
- Descriptive & Inferential Statistics
- Probability Concepts
- Probability Distributions
- Binomial, Poisson and Normal Distributions
- Probability for Business Decision Making
- Exploratory Data Analysis(EDA)
- Presentation of Data
- Hypothesis Formation
- Hypothesis Testing
- Z-test, T-test & Chi Square test
- Implementation of hypothesis in business use cases
- Analysis of Variances (ANOVA)
- Multiple Business use cases & Project
LEVEL-3
SQL in Practice
- Data retrieval using SELECT, WHERE, ORDER BY, LIMIT
- Filtering data with operators, LIKE, IN, BETWEEN
- Aggregate functions and GROUP BY, HAVING
- Table relationships and joins (INNER, LEFT, RIGHT)
- Subqueries and Common Table Expressions (CTEs)
- Window functions for analysis (ROW_NUMBER, RANK, LAG, LEAD)
- Data transformation using CASE, string and date functions
- Data cleaning: handling NULLs, duplicates, and data validation
- Query performance basics and optimization techniques
- SQL for business analysis (KPIs, funnels, cohort analysis)
- Real-world SQL projects and interview problem practice
LEVEL-4
Data Visualization
- Introduction to Power BI and data visualization concepts
- Connecting to data sources (Excel, CSV, SQL databases)
- Data cleaning and transformation using Power Query
- Data modeling and relationships
- DAX fundamentals and calculated columns
- Measures and KPIs using DAX
- Common visualizations (charts, tables, maps, cards)
- Interactive dashboards using filters, slicers, and drill-through
- Design best practices for clear and impactful visuals
- Performance optimization for Power BI reports
- Publishing, sharing, and collaborating in Power BI Service
- Real-world dashboards and business case projects
LEVEL-5
Advanced Excel in Businesses & MIS
- Advanced formulas and functions (IF, IFS, VLOOKUP/XLOOKUP, INDEX-MATCH)
- Data cleaning and preparation techniques
- Conditional formatting for analysis and reporting
- Pivot Tables and Pivot Charts for summarization
- Advanced Pivot Table calculations and slicers
- Data validation and error handling
- Text, date, and logical functions for analysis
- What-if analysis (Goal Seek, Scenario Manager)
- Dashboards and interactive reports in Excel
- Power Query for data transformation and automation
- Performance optimization for large datasets
- Real-world Excel analysis projects
LEVEL-6
Generative AI for Modern Data Analysts
- Introduction to Generative AI and its role in data analytics
- Using GenAI for data exploration and insight generation
- Prompt engineering for analytical use cases
- Automating SQL query writing and optimization with GenAI
- Using GenAI for Excel analysis and formula generation
- Power BI integration with AI visuals and copilots
- Data cleaning and transformation using GenAI tools
- Generating reports, summaries, and insights automatically
- Validating and interpreting AI-generated outputs
- Ethical use of AI and data privacy considerations
- Improving productivity with AI-assisted workflows
- Real-world GenAI use cases for data analysts
LEVEL-7
Industry Preparations
- Resume structuring and ATS-friendly resume scaling
- Customizing resumes for data analyst roles
- LinkedIn profile optimization for recruiter visibility
- Personal branding and keyword optimization on LinkedIn
- Portfolio and project presentation for interviews
- Interview-ready communication and storytelling skills
- Common data analyst interview questions and expectations
- Technical and HR mock interview sessions
- Feedback-driven improvement after mock interviews
- Job application strategy and recruiter outreach techniques