learn data analytics

Master Data Analytics

Course Contents :

  • SQL Server Management Studio (SSMS)
  • SQL Server Reporting Services (SSRS)
  • Power BI
  • Python

Module 1: SQL Server for Data Analysis

Learning Objectives:

  • Understand relational databases and optimize analytical queries
  • Create reusable data objects for BI pipelines
  • Clean and prepare data for reporting and modeling

Expanded Topics Covered:

  • Installing and navigating SQL Server Management Studio (SSMS)
  • Understanding tables, views, indexes, and primary/foreign keys
  • Data types, constraints, and normalization principles (1NF–3NF)
  • Writing optimized SELECT, WHERE, GROUP BY, ORDER BY queries
  • JOIN types: INNER, OUTER, CROSS, SELF joins in real-world context
  • Advanced filtering with subqueries and EXISTS/IN clauses
  • CTEs, temporary tables, and table variables
  • Analytical/window functions for ranking, running totals, lag/lead comparisons
  • Stored Procedures and Functions for parameterized analytics
  • Indexing and Query Optimization Basics
  • Exporting query results to Excel or Power BI

Introduction to SQL Server Reporting Services (SSRS):

  • Overview of SSRS architecture and components
  • Report lifecycle: design, deployment, scheduling, and delivery
  • Creating basic tabular and matrix reports
  • Adding parameters for dynamic report filtering
  • Incorporating charts, gauges, and KPIs
  • Deploying and managing reports on a report server
  • Drill-down and drill-through report features
  • Export options: PDF, Excel, Word, and more

Types of Reports in SSRS:

  • Tabular Reports: Simple tables to display data rows and columns
  • Matrix (Cross-Tab) Reports: Pivot-style reports for summarizing data dynamically by rows and columns
  • Chart Reports: Visual representations including bar, pie, line, area, and scatter charts
  • Gauge Reports: KPI-style visuals such as speedometers and dials for performance measurement
  • Subreports: Embedded reports within a main report to modularize complex reporting
  • Drill-Down Reports: Expandable/collapsible sections to reveal or hide detailed data
  • Drill-Through Reports: Linked reports that allow users to click and navigate to detailed reports
  • Parameter-Driven Reports: Reports where users input parameters to filter data dynamically
  • Snapshot Reports: Static reports capturing data at a specific point in time for audit purposes

Practical Exercises:

  • Normalize a denormalized sales table into relational format
  • Write advanced queries to analyze customer lifetime value (CLV)
  • Create stored procedures to generate periodic business reports
  • Design and deploy a sales report using SSRS incorporating charts and parameters
  • Build drill-down and drill-through reports for interactive data exploration

Project:
Design a relational database for a retail business and generate customer retention, repeat purchase, and profit margin reports through SQL queries. Create multiple SSRS reports (tabular, matrix, chart) to visualize key metrics for management review.

Module 2: Power BI for Data Visualization

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Learning Objectives:

  • Develop advanced, interactive dashboards and reports for business decision-making
  • Design scalable and efficient data models supporting complex business logic
  • Master DAX (Data Analysis Expressions) for powerful calculations and KPIs
  • Implement data refresh, security, and sharing in enterprise environments

Expanded Topics Covered:

Power BI Ecosystem and Architecture:

  • Overview of Power BI Desktop, Power BI Service (cloud), Power BI Mobile
  • Power BI Gateway: Connecting on-premises data sources securely to Power BI Service
  • Licensing models: Free, Pro, Premium, and Embedded options

Data Connectivity and Preparation:

  • Connecting to various data sources: Excel, CSV, SQL Server, Azure, SharePoint, APIs
  • Using Power Query Editor for ETL tasks:
    • Data cleansing techniques (remove duplicates, replace errors, fill down/up)
    • Transformations (pivot/unpivot, merge, append, split columns)
    • Advanced M language basics for custom transformations
  • Creating parameters and templates in Power Query for reusability

Data Modeling and Relationships:

  • Understanding data schemas: star schema vs snowflake schema
  • Creating and managing relationships between tables (one-to-many, many-to-many)
  • Handling role-playing dimensions and inactive relationships
  • Calculated tables and columns
  • Managing data granularity and filter context

Advanced DAX (Data Analysis Expressions):

  • Calculated Columns vs Measures: When and how to use each
  • Common DAX functions: SUM, AVERAGE, COUNTROWS, RELATED, LOOKUPVALUE
  • Filter context and row context explained with practical examples
  • Time Intelligence Functions: TOTALYTD, SAMEPERIODLASTYEAR, DATEADD, PARALLELPERIOD
  • Variables in DAX for optimized and readable formulas
  • Using CALCULATE to modify filter context
  • Creating dynamic measures with SWITCH and IF functions
  • Building running totals, moving averages, and custom KPIs
  • Error handling in DAX

Visualization Techniques:

  • Choosing the right visual: bar, column, line, pie, scatter, tree map, waterfall, funnel
  • Custom visuals: importing and managing marketplace visuals
  • Using slicers and filters effectively to enhance interactivity
  • Drill-down, drill-through, and tooltip pages for detailed insights
  • Bookmarks and buttons for storytelling and guided navigation
  • Themes and conditional formatting for better design and accessibility
  • Using the Decomposition Tree visual for root cause analysis

Report Publishing and Sharing:

  • Publishing reports to Power BI Service and creating Workspaces
  • Data refresh strategies: scheduled refresh, incremental refresh, and on-demand refresh
  • Using dataflows to centralize ETL and data prep in the cloud
  • Setting up Row-Level Security (RLS) to restrict data visibility by user role
  • Sharing reports and dashboards via links, apps, and embedding in SharePoint/Teams
  • Exporting reports to PDF and PowerPoint for offline sharing

Performance Optimization:

  • Best practices for data modeling to improve performance
  • Using Aggregations and Summarization for large datasets
  • Monitoring query performance and optimizing DAX calculations
  • Optimizing visuals to reduce load times and improve responsiveness

Power BI Integration:

  • Connecting Power BI with Excel and Power Automate for automation
  • Embedding Power BI reports in Power Apps and other Microsoft 365 tools
  • Introduction to Power BI REST API for advanced integration

Practical Exercises:

  • Create a sales and inventory dashboard with dynamic filters and KPIs
  • Build custom measures using DAX for profitability analysis
  • Design drill-through reports to analyze product performance
  • Implement RLS to show regional sales data only to authorized users
  • Schedule data refresh for a dashboard connected to a live SQL Server database

Project:
Develop a comprehensive business intelligence dashboard integrating sales, marketing, and customer service data. Use advanced DAX calculations and interactive visuals to deliver insights on revenue trends, customer segmentation, and operational efficiency. Implement security and data refresh best practices for enterprise deployment

Module 3: Python for Data Analytics

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Learning Objectives:

  • Master Python fundamentals for data analysis and automation
  • Utilize powerful libraries for data manipulation, visualization, and reporting
  • Build end-to-end analytics workflows and automate repetitive tasks
  • Work with time series, APIs, and interactive dashboards

Topics Covered:

Python Setup and Environment:

  • Installing Python via Anaconda distribution for data science
  • Setting up Jupyter Notebooks for interactive coding and documentation
  • Overview of IDEs: VS Code, PyCharm, JupyterLab
  • Managing packages with pip and conda
  • Using virtual environments for project isolation

Core Python Programming Concepts:

  • Data types: strings, numbers, lists, tuples, dictionaries, sets
  • Control structures: loops, conditionals, list comprehensions
  • Functions: defining, arguments, return values, lambda functions
  • Error and exception handling
  • File I/O: reading/writing CSV, TXT, and JSON files
  • Creating reusable Python modules and automation scripts

Data Handling and Manipulation:

  • Pandas:
    • DataFrames and Series basics
    • Reading and writing Excel, CSV, JSON, and SQL data
    • Handling missing data, duplicates, and outliers
    • Data filtering, sorting, grouping, pivoting
    • Merging, joining, and reshaping data
    • Working with datetime objects and time series
  • NumPy:
    • Creating and manipulating arrays
    • Broadcasting, vectorized operations, and performance optimization
    • Array indexing and slicing

Data Visualization:

  • Matplotlib: Basic static plotting (line, bar, scatter, histograms)
  • Seaborn: Statistical visualizations (heatmaps, boxplots, pairplots)
  • Plotly: Creating interactive and dynamic visualizations
  • Plot formatting: colors, labels, legends, and layout customization
  • Multi-plot dashboards and combining visual insights

Time Series Analysis:

  • Working with datetime and time-indexed data in Pandas
  • Resampling and frequency conversion (daily, monthly, quarterly)
  • Moving averages and rolling statistics
  • Decomposing time series to identify trend and seasonality
  • Forecasting using simple techniques (e.g., exponential smoothing)
  • Time series visualizations and annotations

Advanced Data Wrangling and Automation:

  • Processing large datasets with chunksize
  • Cleaning and transforming nested data (JSON, XML)
  • Automating Excel reporting with OpenPyXL and XlsxWriter
  • Scheduling Python scripts for automation (Task Scheduler or cron)
  • Logging and error handling in automated data pipelines

Text Data and API Integration:

  • Basic text processing: cleaning, tokenization, word frequency analysis
  • Calling REST APIs using requests library
  • Working with JSON data from APIs
  • Integrating external data (e.g., financial, weather, or CRM platforms) into analytics

Interactive Dashboards and Reporting:

  • Building web-based dashboards with Streamlit or Dash
  • Creating filters, sliders, and charts for interactive insights
  • Exporting reports to PDF, Excel, or HTML
  • Deploying dashboards internally or to the web

Practical Exercises:

  • Clean and merge customer and transaction datasets
  • Build trend analysis dashboards with Plotly and Seaborn
  • Perform time series analysis on sales or traffic data
  • Automate an Excel report generation process
  • Fetch live data from a public API and analyze trends

Project:
Create a complete Python analytics pipeline:
Extract sales and customer data from SQL Server, clean and explore data in Pandas, analyze time-based trends, and visualize results with interactive Plotly charts. Automate a formatted Excel report and create a simple dashboard using Streamlit for business users.