Wingslide Technologies Private Limited

Build Career with Our Comprehensive Data Analytics Course

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Program Highlights

Data Analytics

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Data Analytics Course Curriculum

  • What is Data Analytics?
  • Types: Descriptive, Diagnostic, Predictive, Prescriptive
  • Analytics Lifecycle & Use Cases Across Domains
  • Overview of Tools: Excel, SQL, Python, BI Platforms
  • Roles in the Analytics Ecosystem
  • Data Cleaning, Formatting, and Transformation
  • Lookup & Reference Functions: VLOOKUP, XLOOKUP, INDEX-MATCH
  • Pivot Tables, Pivot Charts, Dynamic Dashboards
  • Scenario Analysis, Goal Seek, Solver
  • Power Query for Advanced Data Preparation
  • Excel + Power BI Integration
  • RDBMS Concepts, ER Diagrams
  • SQL Queries: SELECT, WHERE, GROUP BY, JOINs
  • Nested Queries, Subqueries, Views
  • Window Functions: RANK, ROW_NUMBER, LAG, LEAD
  • Data Transformation & Aggregation Techniques
  • Hands-on with MySQL/PostgreSQL
  • Real-world SQL Projects (Sales, HR, Finance datasets)
  • Power BI Desktop Interface
  • Data Modeling & Relationships
  • DAX for Calculated Columns and Measures
  • Creating Interactive Dashboards
  • Slicers, Filters, Drill-downs, Bookmarks
  • Publishing Reports to Power BI Service
  • Role-Level Security (RLS) and Scheduled Refresh
  • Project: Sales & Marketing KPI Dashboard
  • Tableau Desktop Interface Overview
  • Data Connection, Blending, and Joins
  • Building Charts: Bar, Line, Heatmaps, Geo Maps
  • Filters, Parameters, and Calculated Fields
  • Creating Dashboards with Interactions
  • Storytelling with Tableau Stories
  • Project: E-commerce Revenue Dashboard
  • Python Basics: Lists, Dictionaries, Loops, Functions
  • NumPy & Pandas for Data Manipulation
  • Matplotlib & Seaborn for Visualizations
  • EDA Techniques in Python
  • Combining Python with SQL for End-to-End Analytics
  • Descriptive Statistics: Mean, Median, Mode, Std Dev
  • Probability Basics, Distributions
  • Inferential Statistics: Hypothesis Testing
  • Correlation, Regression
  • Real-world Interpretation in Business Scenarios
  • Data Profiling, Missing Values, Outliers
  • Feature Engineering Basics
  • Pattern Detection and Business Hypotheses
  • Telling Stories from Data using Visual Aids
  • EDA with Excel, SQL, Python, and BI Tools
  • ETL vs ELT
  • Manual ETL with SQL & Excel
  • Introduction to ETL Tools: OpenRefine, Talend (optional)
  • Automation Ideas using Power Query, Python
  • End-to-End Mini Project: Data Collection → Cleaning → Visualization
  • Introduction to AWS, GCP, Azure
  • AWS: S3, Athena, Redshift, QuickSight, Glue
  • GCP: BigQuery, Cloud Storage, Looker Studio
  • Azure: Synapse Analytics, Data Lake, Power BI
  • Data Warehousing Concepts (OLAP vs OLTP)
  • Building Cloud-Connected Dashboards
  • Project 1: 
    • Retail Sales Performance Dashboard (Power BI + SQL)
  • Project 2:
    • HR Attrition and Retention Analysis (Excel + Tableau)
  • Project 3:
    • E-commerce Funnel Optimization (Python + Power BI)
  • Project 4:
    • Marketing ROI & Spend Analysis (Excel + SQL)
  • Capstone Project: End-to-End Case Study with Presentation & Review
  • GitHub Portfolio, LinkedIn Projects, Resume Optimization