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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

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