Welcome to Data Analytics and Visualization course, your gateway to mastering the tools and techniques needed to turn complex data into clear, actionable insights. Throughout this course, you'll develop hands-on skills in Excel, Power BI, Tableau, Python, SQL, and R to create dynamic dashboards and interactive reports. You'll learn to work with advanced chart types, automate repetitive tasks, and apply business intelligence concepts for data-driven decision-making. Dive into real-world projects like sales performance tracking, market trend analysis, and customer behavior insights. Explore data storytelling with interactive visuals, including Pareto charts, sunburst diagrams, and geographical maps. Discover how to integrate tools and automate workflows to enhance efficiency. By the end, you’ll be equipped to deliver compelling dashboards that drive results.
Power BI
Microsoft Power BI has emerged as the world's leading business intelligence and data analytics platform, enabling professionals across industries to connect, transform, model, and visualize data with unprecedented ease and depth. This comprehensive 60-hour course is designed to take learners from absolute beginners to confident, enterprise-level Power BI practitioners.
Course Features:
- Language: English
- Track: Data Science
- Duration: 60 hours
- Level: Master
- Learning Mode: Learn at ALC or Learn Online
- Stream: Any Stream
- Jurisdiction: Maharashtra
- Certificate of Completion
Eligibility
- Learner should preferably a std. 10th Pass student (Not Compulsory)
- It is desirable that Learner should have done MS-CIT Course (Not Compulsory)
KLiC Certificate in Power BI
Introduction
What you'll learn ?
The primary aim of this course is to equip learners with the full spectrum of Power BI competencies - from initial setup and data connectivity through to advanced DAX engineering, enterprise security, AI-driven analytics, and professional dashboard storytelling. Upon completion, learners will be capable of independently building, publishing, and governing production-quality Power BI solutions in real organizational contexts.
Foundational Objectives (First 15 Sessions)
- Understand the Power BI ecosystem, key components, and the role of Power BI in the modern data analytics landscape.
- Install, configure, and confidently navigate all key areas of the Power BI Desktop interface including Report View, Data View, Model View, and all ribbon tabs.
- Connect to diverse data sources - including Excel workbooks, CSV files, databases, and cloud sources - and understand when to load data versus transform it in Power Query.
- Use Power Query Editor to perform professional-grade data cleaning operations: fixing data types, removing nulls and blanks, filtering, sorting, splitting columns, and handling inconsistencies.
- Apply advanced Power Query transformations including Pivot, Unpivot, column reordering and renaming, derived column creation, and reference query design.
- Construct robust data models using a Star Schema approach, defining primary and foreign key relationships, cardinality (One-to-Many, Many-to-Many), cross-filter directions, and active vs inactive relationships.
- Build a rich library of core Power BI visualizations - column charts, bar charts, line charts, pie/donut charts, tables, matrix visuals, cards, KPI visuals, multi-row cards, maps, and scatter plots.
- Design professional, visually consistent reports using design principles, theme selection, custom branding, alignment tools, shape containers, and typography best practices.
- Implement interactive reporting features including filter levels (visual, page, report), basic and advanced slicers, slicer synchronization across pages, drill-through, and custom visual integration.
- Master foundational DAX including calculated columns, measures, aggregation functions (SUM, COUNT, DISTINCTCOUNT, AVERAGE, MAX, MIN), and business measures (Total Revenue, Profit, Profit Margin).
- Apply essential DAX functions for conditional logic (IF, Nested IF, SWITCH), filter manipulation (CALCULATE, FILTER), running totals, ranking (RANKX), and Top N analysis.
- Implement Time Intelligence calculations - YTD, MTD, QTD, SAMEPERIODLASTYEAR, Year-over-Year growth, Rolling 3-Month Average - using a properly configured Date Table.
- Build KPI dashboards with Card, KPI, and Gauge visuals; use conditional formatting with color indicators, dynamic icons, and rule-based formatting to highlight performance.
- Understand Power BI storage modes - Import, DirectQuery, and Dual - along with composite models, capacity considerations, and enterprise connectivity patterns.
- Publish reports to the Power BI Service, manage workspaces, assign roles, distinguish Dashboards from Reports, pin tiles, share reports, and configure scheduled data refresh.
Advanced and Enterprise Objectives (Last 15 Sessions)
- Configure and manage data refresh pipelines - manual refresh, scheduled refresh, credentials, failure notifications, and advanced incremental refresh with RangeStart/RangeEnd parameters.
- Set up and test both Personal and On-Premises Data Gateways for secure connectivity to local data sources.
- Implement Row-Level Security (RLS) using both static roles and dynamic RLS with the USERPRINCIPALNAME() DAX function; test, assign, and validate RLS configurations in both Desktop and Service.
- Diagnose and resolve Power BI performance bottlenecks using the Performance Analyzer, optimize model size by removing unused columns, reducing cardinality, optimizing data types, and designing efficient star schemas.
- Configure incremental refresh policies, understand columnar storage and compression, and manage large datasets efficiently using partitioning and data reduction techniques.
- Leverage AI-powered visuals in Power BI - Key Influencers, Decomposition Tree, Smart Narrative - along with Microsoft Copilot for AI-driven report generation, measure suggestion, and natural language insights.
- Apply Object-Level Security (OLS) for column-level and table-level data governance; combine with RLS for complete enterprise-grade access control using external tools like Tabular Editor.
- Use Field Parameters for dynamic analysis, enabling user-driven measure and dimension switching in reports.
- Write, read, and modify M language code in the Power Query Advanced Editor; apply functional programming patterns including Let-In structure, conditional logic, Text/Date/Number functions, and custom reusable functions.
- Explore the Microsoft Fabric platform: OneLake architecture, Lakehouse vs Data Warehouse design, creating Fabric pipelines, and integrating Power BI with Fabric workspaces.
- Solve complex real-world business problems using advanced DAX patterns: dynamic SWITCH TRUE logic, dynamic ranking, customer segmentation, percentile segmentation, ABC analysis, and multi-metric comparison.
- Integrate Python with Power BI for data transformation using Pandas, feature engineering, K-Means clustering, sales trend prediction with simple forecasting, and result visualization.
- Engineer advanced dashboards following professional report design standards: Star Schema modeling, organized measure layers, reusable measures, fact/dimension identification, and KPI design thinking.
- Complete a full-scale Capstone Project that integrates all course learnings into a business-grade Power BI analytical report with advanced features, scenario analysis, and data storytelling.
Course Outcomes
On successful completion of this 60-hour Power BI course, learners will demonstrate the following competencies:
Knowledge Outcomes
- Explain the architecture and components of the Power BI ecosystem including Power BI Desktop, Power BI Service, Power BI Gateway, Power BI Mobile, and Microsoft Fabric.
- Describe the data lifecycle in Power BI — from raw source data through ingestion, transformation, modeling, calculation, visualization, and governed publishing.
- Explain the principles of Star Schema data modeling and the distinction between Fact Tables, Dimension Tables, and Bridge Tables.
- Articulate the functional differences between Import, DirectQuery, and Dual storage modes, and justify selection criteria for each in enterprise scenarios.
- Explain DAX evaluation contexts (Row Context and Filter Context) and how CALCULATE modifies the filter context.
- Describe the architecture of Microsoft Fabric, the role of OneLake as a unified data lake, and the comparative use cases for Lakehouse vs Data Warehouse.
- Identify best practices in Power BI report design, performance optimization, and enterprise data governance.
Skill Outcomes
- Connect Power BI to multiple data sources and use Power Query to transform, clean, and shape data ready for analysis.
- Build and validate a data model with correct relationships, cardinality, and cross-filter direction for accurate reporting.
- Write DAX measures including aggregations, time intelligence functions, conditional logic, CALCULATE/FILTER, RANKX, running totals, and advanced SWITCH TRUE patterns.
- Create interactive, professional-grade reports and dashboards using standard and custom Power BI visuals with slicers, drill-through, and conditional formatting.
- Configure Row-Level Security and Object-Level Security to govern data access based on user identity.
- Implement scheduled data refresh, gateway configuration, and incremental refresh for enterprise-scale reporting.
- Leverage AI visuals (Key Influencers, Decomposition Tree) and Microsoft Copilot for accelerated analysis and report creation.
- Write and modify M language code in the Advanced Editor to build reusable, dynamic, and parameterized Power Query solutions.
- Integrate Python scripts within Power BI for machine learning tasks including clustering and forecasting.
- Deliver a complete end-to-end Power BI report from raw data to executive-ready dashboard incorporating Star Schema modeling, DAX measures, advanced features, and narrative storytelling.
Competency Outcomes (Role-Readiness)
| Role / Profile | Competency Achieved |
|---|---|
| Business Analyst | Design data models, build KPI dashboards, create interactive reports for business decision support. |
| Data Analyst | Transform raw data, engineer DAX measures, perform time series analysis, and deliver insight-rich reports. |
| Financial Analyst | Build P&L, budget variance, YTD/MTD financial reports with Target vs Actual analysis using KPI visuals. |
| Marketing Analyst | Analyse campaign performance, customer segmentation, and funnel analysis using AI-driven visuals and DAX. |
| BI Developer | Architect Star Schema models, configure RLS/OLS, manage gateway and refresh pipelines, write advanced M and DAX. |
| Data Engineer (Fabric) | Build Lakehouse pipelines, manage OneLake architecture, and integrate Power BI with Microsoft Fabric. |
| Report Consumer / Manager | Understand how to use, filter, share, and act on Power BI dashboards and reports in the Power BI Service. |
Syllabus
- Course Introduction : Course overview, credit structure, learning approach, what learners will build
- Overview of Power BI and Key Features : What is Power BI, key capabilities, comparison with Excel and Tableau
- Understanding Power BI Ecosystem : Desktop, Service, Mobile, Gateway, Embedded, Fabric - roles and relationships
- Download and Installation : Step-by-step installation of Power BI Desktop from Microsoft Store and website
- Navigating the Power BI Interface : Report Canvas, Pages, Panels overview, switching between views
- Home Tab : Data connections, Queries, Calculations, Insert, Sensitivity
- Insert Tab : Visual types, Text boxes, Buttons, Shapes, Images
- Modeling and View Tabs : Relationships, Calculations, Page view, Gridlines, Snap to Grid
- Optimize and Help Tab : Performance optimization settings, DAX editor, Help resources
- Report View, Data View, Model View : Purpose and usage of each view with hands-on exploration
- Session Overview and Dataset Introduction : The sample dataset used throughout the course, business context setup
- Understanding Data Sources in Power BI : File sources, database sources, online services, other sources overview
- Excel File Integration in Power BI : Connecting to Excel, selecting sheets/tables, handling named ranges
- Connecting to CSV Files : CSV connection, encoding and delimiter handling, previewing data
- Data Preview and Navigator Window : Navigator interface, selecting multiple tables, preview pane
- Load vs Transform - What to Choose : Decision framework: when to load directly vs transform first
- Understanding the Fields Pane : Fields pane layout, field types, table organization, hidden fields
- Exploring the Report View Workspace : Canvas, Pages, Visuals positioning, undo/redo, zoom controls
- Visualizations Pane Deep Dive : Visual types list, Format pane, Analytics pane, Add data fields
- Exploring the Filters Pane : Visual filters, Page filters, Report filters - adding and clearing
- Creating First Column Chart and Table : Drag-and-drop approach, axis configuration, data label basics
- Sorting and Basic Formatting : Sorting by value/field, ascending/descending, basic color and size
- Saving and Managing Report Files : Saving as .pbix, auto-save, file management, backup practices
- Why Data Cleaning Matters : Impact of dirty data on reports, common data issues, business costs
- Opening Power Query Editor : Transform Data button, the Power Query interface overview
- Understanding Applied Steps : Steps pane, reordering steps, deleting steps, formula bar
- Fixing Data Types : Auto-detection, changing types manually, date/time types, common errors
- Removing Unnecessary Columns : Choose Columns, Remove Columns, Remove Other Columns
- Filtering and Sorting Data : Basic filters, text-condition filters, number filters, multi-column sorting
- Handling Null and Blank Values : Null vs blank distinction, Replace Nulls, Fill Down, Remove Rows
- Column Splitting : Split by delimiter, split by position, split by number of characters
- Text Transformations : Trim, Clean, UPPERCASE, lowercase, Capitalize Each Word
- Column Merging and Custom Columns : Merge Columns, Add Custom Column with M expressions
- Close and Apply : Applying changes, pending queries, refresh behavior in Desktop
- Introduction to Data Transformation : What transformation means, transformation vs loading
- Understanding Data Structure for Reporting : Wide vs narrow tables, why normalized structures perform better
- Query Transformation vs DAX Transformation : Which transformations belong in Power Query vs DAX (decision guide)
- Query Folding Concept : What query folding is, why it matters for DirectQuery performance
- Excel and Power BI Integration for Structured Reporting : Named ranges, Excel table best practices for Power BI
- Derived Columns with Business Logic : Custom Column with IF/Text logic, conditional columns
- Reordering and Renaming Columns : Column organization, renaming for clarity, display names
- Best Practices for Naming Conventions : Field naming standards, avoiding spaces, special characters
- Pivot Column : Converting row values to column headers - concept and demo
- Unpivot Columns : Converting column headers to rows - normalization demo
- Creating Reference Queries : Reference vs Duplicate, concept of reusable transformation pipelines
- Introduction to Data Modeling : Why modeling matters, flat table risks, model-based approach benefits
- Model View Interface : Diagram view, table list, relationship lines, zoom and layout tools
- Primary Key Concept : Uniqueness requirement, identifying primary keys in dimension tables
- Foreign Key Concept : Foreign keys in fact tables, referential integrity
- Creating Relationships : Drag-and-drop method, auto-detect, Manage Relationships dialog
- Common Relationship Errors : Ambiguous relationships, circular dependencies, resolving errors
- Cardinality Types : One-to-Many (most common), Many-to-One, One-to-One, Many-to-Many
- Cross-Filter Direction : Single direction (recommended default), both direction (risks)
- Active vs Inactive Relationships : When to have multiple relationships, USERELATIONSHIP function
- Editing and Deleting Relationships : Modifying existing relationships, impact assessment
- Star Schema Introduction : Fact tables, dimension tables, the Star Schema pattern
- Introduction to Visualizations : Types of questions, matching question to visual type
- Column and Bar Charts : Basic setup, axis configuration, clustered vs stacked, 100% stacked
- Aggregation Setup : SUM, COUNT, AVERAGE, MIN, MAX on visual data fields
- Formatting Axis and Legends : Axis title, data labels, color, legend position
- Creating Line Charts : Line chart setup, trend analysis, forecasting overlay
- Handling Date Hierarchy : Year/Quarter/Month/Day drill-down in line charts
- Pie and Donut Charts : When to use, category limits, formatting
- Choosing the Right Visual : Decision guide: comparison vs trend vs composition vs distribution
- Visual Performance Considerations : Number of data points, visual complexity vs load time
- Creating Tables : Building a basic table, formatting columns, conditional formatting basics
- Creating Matrix Visual : Rows, Columns, Values structure; expanding and collapsing rows
- Matrix vs Table Difference : When matrix is more powerful; row/column grouping
- Using Cards and Multi-Row Cards : Single KPI Card setup, multi-row card for multiple metrics
- Introduction to Report Design : What separates professional from amateur reports
- Visual Hierarchy Concept : Size, color, and position to guide reader attention
- Consistency and Alignment Rules : Spacing, padding, color consistency across visuals
- Choosing Report Themes : Default themes, custom themes, importing JSON themes
- Aligning and Distributing Visuals : Align Left/Center/Right/Top/Bottom, Distribute Horizontally/Vertically
- Using Shapes for Layout : Rectangle containers, dividers, section backgrounds
- Adding Text and Titles Professionally : Typography basics, consistent title formatting, font selection
- Using Backgrounds and Branding : Background images, subtle background styling, logo placement
- Page Navigation Buttons : Action buttons, page navigation, bookmark actions
- Multi-Page Report Design : Summary page, detail pages, navigation flow
- Report-Level Formatting Best Practices : Canvas size, page padding, mobile layout
- Introduction to Interactivity : Why interactive reports are more useful, user journey design
- Understanding Filter Levels : Visual-level, Page-level, and Report-level filters - hierarchy
- Visual-Specific Data Filtering : Applying filters per visual, clearing filters, include/exclude
- Page-Level Filters : Affecting all visuals on a page
- Report-Level Filters : Affecting all pages, use cases
- Creating Basic Slicers : List slicer, Dropdown slicer, field configuration
- Slicer Formatting : Horizontal vs Vertical orientation, styling, selection controls
- Syncing Slicers Across Pages : Sync Slicers panel, visible vs active settings, cross-page sync demo
- Drill-Through Setup : Drill-through target page, back button, drill-through fields
- Drill-Down in Visuals : Hierarchical drill-down in charts, expand all levels
- Custom Visuals from AppSource : Finding, installing, and using approved custom visuals
- Cross-Report Drill-Through : Linking across different .pbix files for modular reporting
- What is DAX? : DAX definition, syntax basics, expressions vs functions, data types
- Importance of DAX in Power BI : Why DAX is essential, relationship with the data model
- Measures vs Calculated Columns : When to use each, storage difference, evaluation context basics
- Creating a Calculated Column : Syntax, table context, examples with text and number operations
- Creating a Measure : Measure creation dialog, implicit vs explicit measures
- Difference Between Column and Measure : Row context vs filter context, storage and performance
- SUM Function : SUM() syntax, use cases, combining with relationships
- COUNT and DISTINCTCOUNT : Counting rows vs counting unique values, business applications
- AVERAGE Function : AVERAGE(), AVERAGEX() introduction
- Creating Business Measures : Total Revenue, Total Cost, Profit, Profit Margin % measures
- Business Calculations in Power BI : Business scenario setup, KPI requirements, measure design approach
- Using the IF Function : IF() syntax, single condition, business examples
- Using the Nested IF Function : Multiple conditions with nested IF, managing complexity
- Using the SWITCH Statement : SWITCH() as a cleaner alternative to nested IF
- Introduction to CALCULATE Function : Most important DAX function - modifying filter context
- Using the FILTER Function : FILTER() as table function, combining with CALCULATE
- Creating Running Total : CALCULATE with DATESYTD or FILTER for running totals
- RANKX Function : RANKX() syntax, ascending/descending, dense vs standard ranking
- Top N Analysis : Creating Top N filter, static Top N, dynamic Top N with parameter
- Using the Dynamic Top N Function : What-If parameter for dynamic Top N slicer control
- Fundamentals of Time Intelligence : Why Time Intelligence requires a Date Table, continuous dates
- Date Table Setup : Requirements: continuous dates, no gaps, correct date column
- Creating a Date Table : CALENDAR(), CALENDARAUTO(), DATESBETWEEN() approaches
- Marking as Date Table : Mark as Date Table in Desktop - why and how
- YTD Calculation : TOTALYTD(), DATESYTD() with CALCULATE, fiscal year end
- MTD and QTD Calculations : TOTALMTD(), TOTALQTD(), DATESMTD(), DATESQTD()
- Previous Period Comparison : What SAMEPERIODLASTYEAR does, business use case
- SAMEPERIODLASTYEAR Function : Syntax, applying to Revenue/Profit, building comparison measures
- Year-over-Year Growth % : YoY % = (Current - Previous) / Previous - formula and visual
- Rolling 3-Month Average : DATESINPERIOD() for rolling calculations, moving averages
- Introduction to Calculation Groups : What Calculation Groups are, benefits for reducing measure count
- External Tools for Model Optimization : Tabular Editor and DAX Studio introductory overview
- KPI and Its Importance : Defining KPIs, lagging vs leading indicators, KPI best practices
- Target vs Actual Analysis : Business context, setting up targets, actual vs target framework
- Creating Target Measures : Hardcoded targets, budget table approach, average-based targets
- Variance Calculation : Absolute variance, percentage variance, positive/negative interpretation
- KPI Visual Design : Choosing between Card, KPI Visual, Gauge for different scenarios
- Using Card Visual : New Card visual features, reference labels, callout value
- Using KPI Visual : Trend axis, status indicators, target configuration
- Using Gauge Visual : Min/Max/Target settings, gauge formatting
- Color Indicators (Conditional Formatting) : Background color, font color based on value thresholds
- Using Dynamic Icons : Icon sets, custom icon rules, directional arrows
- Rule-Based Conditional Formatting : Greater than/Less than rules, between ranges
- Advanced Conditional Formatting : DAX-based conditional formatting, gradient color scales
- Performance Categorization : Segment measures into RAG (Red/Amber/Green) categories
- Import vs DirectQuery vs Dual : Definitions, data flow, where data lives in each mode
- Architecture Differences : VertiPaq engine (Import), pass-through queries (DirectQuery)
- Performance Behavior : Import speed advantage, DirectQuery latency, Dual hybrid
- Memory Impact : Model size in Import, memory-free DirectQuery, Dual tradeoffs
- Enterprise Use Case Scenarios : When each mode is appropriate for real businesses
- When to Use Import : Best for: < 1GB datasets, frequent slicing, complex DAX
- When to Use DirectQuery : Best for: live data, large datasets, regulatory requirements
- When to Use Dual : Mixed scenarios, aggregation tables with detail fallback
- Choosing the Right Storage Mode : Decision framework with business criteria
- Common Mistakes in Storage Mode Selection : Anti-patterns and how to avoid them
- Composite Models : Combining Import and DirectQuery in one model
- Capacity Considerations : Impact on Premium vs Pro vs Fabric capacity
- Desktop vs Service : What the Service adds: sharing, scheduling, dashboards, apps
- Power BI Licensing : Free, Pro, Premium Per User, Premium Capacity, Fabric SKUs
- Free vs Pro vs Premium : Feature comparison, collaboration requirements, cost implications
- Fabric SKUs and Capacity Management : F SKU types, capacity units, Fabric licensing model
- Publishing Reports : Publish from Desktop, choosing workspace, overwriting existing reports
- Creating Workspaces : New workspace, workspace types (Classic vs New), naming
- Workspace Roles : Admin, Member, Contributor, Viewer - permissions and access
- Permission Levels : Direct access, links, public embedding, row-level
- Dashboards vs Reports : Key differences: pin tiles vs interactive report
- Pinning Tiles : Pin visual from report to dashboard, from Q&A, from another dashboard
- Dashboard Creation Demo : Building a complete executive dashboard end-to-end
- Sharing and Collaboration : Share link, send by email, workspace access sharing
- Alerts and Subscriptions : Setting data alerts on dashboard tiles, email subscriptions
- Practice Exam : Comprehensive assessment covering all 14 Credit 1 sessions - 30 marks, 120 minutes, MCQ format
- Understanding Data Refresh : Stale data problem, refresh types overview
- Manual Refresh : On-demand refresh in Service, refresh button, limitations
- Scheduled Refresh Overview : Time-based refresh, frequency settings, timezone
- Data Refresh Dependencies : Source credentials, gateway requirement, storage mode dependencies
- Dataset Refresh Settings : Enabling/disabling refresh, failure handling
- Configuring Scheduled Refresh : Step-by-step: Service > Dataset Settings > Scheduled Refresh
- Setting Credentials : OAuth, Basic, Windows, Service Principal authentication
- Configuring Schedule Settings : Frequency: Daily, Weekly; Time slots; Maximum 8/day (Pro)
- Failure Notifications : Email on failure, notification recipients, troubleshooting
- Advanced Refresh : XMLA endpoint refresh, Tabular Editor advanced refresh
- Introduction to Gateway : Why gateway is needed, architecture overview
- Personal vs Enterprise Gateway : When to use each, side-by-side comparison
- Gateway Installation Overview : Step-by-step installation, signing in, adding data sources
- RLS Fundamentals : What RLS does, where it is enforced, business motivation
- Manage Roles Window : Opening Manage Roles, creating roles, role naming
- Business Use Case for RLS : Scenario: Regional Sales Manager seeing only their region
- Creating Static Roles : Defining roles in Desktop with fixed filter conditions
- Defining Role in Desktop : Manage Roles dialog, DAX filter expression syntax
- Writing Basic Filter Rules : Simple equality filter: [Region] = "North"
- Testing Static Roles : View As Roles in Desktop, validating data filtering
- Dynamic Row-Level Security : Using USERPRINCIPALNAME() to build user-aware rules
- USERPRINCIPALNAME Function : What it returns, why it works in Service, lookup table setup
- Creating Dynamic Filter Rule : USERPRINCIPALNAME() lookup in the security table
- Testing Dynamic RLS in Desktop : Simulating user with specific email in View As
- RLS in Power BI Service : Publishing, assigning members to roles in Service
- Testing RLS in Service : Viewing as a specific user/role in Service
- Performance Analyzer Tool : Opening, recording, what it measures: DAX, visual, rendering
- Running Performance Check : Analyzing each visual, identifying slow DAX vs rendering
- Interpreting Results : Reading the JSON trace, identifying bottlenecks
- Model Size Optimization : File size vs in-memory model size, the Vertipaq engine
- Removing Unused Columns : Impact of unnecessary columns, column removal strategy
- Data Type Optimization : Text vs Whole Number vs Decimal, Binary, Date type differences
- Reducing Cardinality : High cardinality columns, grouping/bucketing strategies
- Relationship Optimization : Why bi-directional filters slow down reports
- Proper Star Schema Design : Denormalized vs normalized, why Star Schema is fast
- Reducing Visual Count : Rule of thumb per page, visual interaction cost
- Avoiding High Granularity Tables : Row-level detail impact on model performance
- Using Summary Tables : Pre-aggregated tables as aggregation companions
- Large Dataset Challenges : Refresh time, memory pressure, user experience with big data
- Efficient Data Reduction Techniques : Top N rows, date filtering at source, aggregation strategies
- How Power BI Stores Data : Columnar storage, in-memory VertiPaq engine overview
- Columnar Storage and Compression : Why columns compress well, dictionary encoding
- Cardinality and Its Impact on Storage : High cardinality = less compression = larger model
- Encoding Techniques : Value encoding vs hash encoding, impact on model
- What is Incremental Refresh? : Partitioned refresh, only importing new/changed partitions
- How Partitioning Works : Date-based partitions, historical vs hot data
- RangeStart and RangeEnd Parameters : Creating M query parameters with correct naming and type
- Applying Date Filters in Query : Using parameters in Power Query date filter
- Configuring Incremental Refresh Policy : Store/refresh X days, Detect Data Changes option
- Deploying and Validating : Publishing, checking partitions via XMLA endpoint
- AI in Power BI Overview : What AI features are available, licensing requirements
- What are AI-Powered Visuals? : Key Influencers, Decomposition Tree, Smart Narrative, Q&A, Anomaly Detection
- When to Use AI Features : Business scenarios where AI visuals add analytical value
- AI-Driven Roles Overview : How different roles use AI features in Power BI
- Key Influencers - High Sales Drivers : Setting up Key Influencers, analyzing what increases/decreases a metric
- Top Segment Analysis : Segment explorer within Key Influencers
- Low Sales Insights : Using Key Influencers for negative driver analysis
- What is a Decomposition Tree? : AI splits vs manual splits, visual configuration
- First Level Tree Breakdown : Setting up the analyze and explain fields
- Channel and Category Analysis : Multi-level decomposition for marketing/product analysis
- Smart Narrative : Auto-generated text summaries, customizing the narrative
- Q&A Visual : Natural language questions, synonym setup, field synonyms
- Introduction to Copilot : What Copilot is, how it is powered, licensing requirements
- What Copilot Does in Power BI : Report page creation, measure suggestion, narrative summaries
- Enabling Copilot in Workspace : Premium/Fabric requirement, enabling in admin settings
- Generating Visuals Using Prompts : Natural language prompts, prompt refinement, visual placement
- Modifying Visuals via AI : Using Copilot to edit existing visuals through text instructions
- Comparing Manual vs AI Approach : Speed, accuracy, when AI helps, when manual is better
- Limitations of Copilot : Data model quality dependency, hallucination risks, scope limitations
- AI-Based DAX Generation : Asking Copilot to generate DAX measures, validating output
- Generating Measures Using Prompts : Natural language to DAX, reviewing and accepting suggestions
- Validate AI-Generated DAX : Checking logic, testing against manual measures
- Optimizing DAX with AI Assistance : Using Copilot to simplify complex DAX expressions
- Natural Language Insights : Asking business questions in Copilot, getting narrative answers
- Object-Level Security (OLS) : What OLS is, difference from RLS, column vs table hiding
- OLS vs RLS : RLS = row filtering; OLS = object hiding - complementary use
- Column-Level Security Demo : Hiding salary/confidential columns from specific roles
- Table-Level Security Demo : Hiding entire tables from non-authorized users
- Enterprise Use Case for OLS : Finance/HR data governance scenario
- Dynamic Analysis Using Field Parameters : What Field Parameters are, enabling in Preview features
- What are Field Parameters? : A calculated table enabling dynamic axis/legend switching
- Dynamic Measure Switching : Swapping between Revenue/Profit/Units using a slicer
- Dynamic Dimension Switching : Swapping between Region/Product/Channel on chart axis
- Advanced Field Parameter Use Case : Combining measure and dimension switching
- DAX Studio Overview : Connect, query, trace DAX execution in DAX Studio
- Tabular Editor Overview : Batch editing, calculation groups, best practice analyzer
- Power Automate Integration : Triggering Power Automate flows from Power BI button actions
- M Language Fundamentals : What M is, functional vs procedural, how Power Query generates M
- Power Query as a Functional Language : Immutability, expressions vs statements, lazy evaluation
- Reading M Code in Advanced Editor : Opening Advanced Editor, reading auto-generated code
- Understanding Let-In Structure : let ... in structure, step naming, expression chaining
- Writing and Modifying M Code : Editing directly in Advanced Editor, adding custom steps
- Creating Custom Columns Using M Code : Column formulas with M expressions beyond UI capabilities
- Conditional Logic in M : if...then...else in M, nested conditions
- Text, Date, and Number Functions in M : Text.Contains, Text.Start, Date.Year, Number.Round examples
- Advanced Data Transformation Using M : Table.SelectRows, Table.AddColumn, Table.Group
- Merging Using M Code : Table.NestedJoin, join kinds in M
- Grouping and Aggregations Using M : Table.Group syntax, custom aggregations
- Using Parameters in Power Query : Creating and using query parameters, environment-driven queries
- Query Relationships in Power BI : Duplicate vs Reference, query dependencies, circular references
- Why Fabric is the Future of BI : Unified platform vision, eliminating data silos, SaaS advantage
- Core Components of Fabric : Data Factory, Synapse Data Engineering, Data Warehouse, Power BI, Data Science, Real-Time Intelligence
- OneLake Deep Dive : What OneLake is - one data lake per tenant, ADLS Gen2 compatible
- OneLake vs Traditional Storage : No copies, shortcuts, Delta format, universal access
- Unified Data Access Concept : All Fabric workloads access the same OneLake storage
- Uploading and Exploring Data in Lakehouse : Creating a Lakehouse, uploading files, exploring in Lakehouse Explorer
- Lakehouse vs Data Warehouse : When to use each: schema-on-read vs schema-on-write
- Lakehouse and Data Warehouse Comparison : Tables/Files area, SQL endpoint, T-SQL vs PySpark
- Lakehouse vs Warehouse in Fabric : Side-by-side feature comparison in Fabric context
- Data Engineering in Fabric : Notebooks, Spark jobs, data pipelines
- Creating Lakehouse Pipelines : Copy Activity, data movement from external sources to Lakehouse
- Power BI DirectLake Mode : What DirectLake is, how it bypasses import/DirectQuery limitations
- Dynamic Business Logic Using Slicers : How slicer selections drive measure behavior
- Preparing Data for Dynamic Reporting : Disconnected tables, parameter tables for slicers
- Creating Dynamic Business Logic with Measures : SELECTEDVALUE(), VALUES() in DAX for dynamic logic
- Dynamic Measures with SWITCH TRUE : SWITCH(TRUE(), [measure] > value, ...) pattern
- Enhancing Reports with Dynamic Titles : Concatenated measures for contextual report titles
- Dynamic Metric Selection : User-selectable KPI from a slicer, one visual for multiple metrics
- Multi-Metric Comparison in One Visual : Showing Revenue, Profit, and Units dynamically in same chart
- Customer Segmentation and Ranking : RFM principles, segmentation by spend tier
- Dynamic Ranking : RANKX with ALLSELECTED() for context-sensitive ranking
- Percentile Segmentation : PERCENTILE.INC in DAX, top/bottom 20% customer identification
- ABC Analysis : A: top 80% revenue, B: next 15%, C: remaining - DAX implementation
- Customer Categorization Using SWITCH : Assigning A/B/C labels with SWITCH based on cumulative revenue
- Dynamic Column Creation Using M : Adding columns programmatically based on logic/metadata
- Creating Conditional Columns Using M : if...then...else inside Table.AddColumn
- Dynamic Column Naming and Metadata Logic : Using metadata tables to drive column names and types
- Column Generation Based on Rules : Rule-driven table generation for flexible schemas
- Generating Synthetic Data : Using List.Generate, Table.FromList to create test datasets
- Creating Random Sales Data Using M : Date sequences, random numbers, category assignment
- Generating Date Sequences Dynamically : List.Dates, duration parameters for flexible date generation
- Adding Categories and Customer Segments : Associating generated data with business dimension categories
- Data Reshaping and Automation : Automating repetitive transformation steps with M functions
- Auto Pivot and Unpivot Using M : Programmatic pivot/unpivot without specifying column names
- Dynamic Column Expansion : Expanding record/table columns with unknown schemas
- Handling Changing Data Structures : Schema drift patterns, defensive M coding
- Creating Custom Functions in Power Query : Writing reusable M functions, calling functions from queries
- Python Integration Basics : Requirements: Python 3.x, pandas, scikit-learn, matplotlib
- Enabling Python in Power BI : Setting the Python home directory in Options > Python scripting
- Running Python Scripts in Power Query : Using Python to ingest and pre-process data as a data source
- Cleaning Data Using Pandas : Dropna, fillna, rename, astype, drop_duplicates in pandas
- Feature Engineering in Python : Creating derived features, ratio columns, datetime extraction
- Handling Large Datasets Efficiently : Chunked reading, dtypes optimization, in-place operations
- Introduction to Clustering Logic : What K-Means is, when to use clustering in business analytics
- Applying K-Means on Customer Data : sklearn KMeans, fitting, cluster label assignment
- Interpreting Cluster Results : Cluster profiles, naming segments, business meaning
- Simple Forecasting Using Python : Linear regression trend forecasting with sklearn/statsmodels
- Visualising Predictions in Power BI : Using Python visual in report, matplotlib charts in Power BI
- Comparing Actual vs Predicted : Side-by-side comparison visual, accuracy assessment
- Turning Reports into Products : Product thinking: audience, purpose, access, maintenance
- Beginner vs Professional Dashboards : Before/after examples, what distinguishes expert reports
- Business-First vs Chart-First Approach : Start with the business question, not the visual type
- Designing for Decision Making : Information architecture, progressive disclosure, action orientation
- Star Schema Design (from scratch) : Modeling workshop: identifying facts and dimensions from raw data
- Creating Fact and Dimension Tables : Breaking a flat table into normalized fact/dimension structure
- Building and Validating Relationships : Testing relationship accuracy with DAX sanity checks
- Avoiding Common Modeling Mistakes : Many-to-many pitfalls, ambiguous relationships, snowflake over-engineering
- Measure Layer : Organizing measures into folders, base measures vs. calculation measures
- Creating Reusable Measures : Designing measures that can be referenced by other measures
- Organizing Measures into Folders : Display folders in Desktop, logical grouping strategy
- Best Practices for Naming Measures : Consistent prefixes, verb-noun patterns, emoji indicators
- Model Optimization Review : Final optimization pass: unused columns, relationships, data types
- Data Story Structuring : Hero metric, context, breakdown, action recommendation framework
- Introduction to Project : Project brief, business context, deliverables, evaluation criteria
- Defining the Business Problem : Problem statement formulation, KPI selection, audience definition
- Create Dataset Using M : End-to-end synthetic or real dataset creation using M language
- Data Profiling : Column quality, column distribution, value profiles in Power Query
- Data Cleaning Using M : Applied Steps for production-quality cleaning pipeline
- Data Transformation Strategy : Designing the transformation flow from raw to model-ready
- Fact vs Dimension Identification : Classifying all tables and fields as fact or dimension
- Star Schema Modelling : Building the complete relational model in Model View
- Relationship Validation : Validating all relationships with test measures
- Creating KPI Measures with DAX : Revenue, Profit, Growth %, Variance, Ranking, Segmentation measures
- KPI Design Thinking : Hierarchy of KPIs, leading vs lagging, visual choice
- Advanced Features Integration : Applying Field Parameters, dynamic titles, conditional formatting
- Scenario Analysis : What-If parameter for sensitivity analysis (e.g. price change impact)
- Data Story Structuring : Narrative flow: Executive Summary → Breakdown → Insights → Action
Evaluation Criteria
Evaluation Pattern of KLiC Courses consists of 4 Sections as per below table:
| Section No. | Section Name | Total Marks | Minimum Passing Marks |
|---|---|---|---|
| 1 | Learning Progression | 35 | 14 |
| 2 | Internal Assessment | 35 | 14 |
| 3 | Final Online Examination | 30 | 12 |
| Total | 100 | 40 | |
| 4 | SUPWs (Socially Useful and Productive Work in form of Assignments) | 5 Assignments | 2 Assignments to be Completed & Uploaded |
MKCL’s KLiC Certificate will be provided to the learner who will satisfy the below criteria:
- Learners who have successfully completed above mentioned 3 Sections i.e. Section 1, Section 2 and Section 3
- Additionally, learner should have completed Section 4 (i.e. Section 4 will comprise of SUPWs i.e. Socially Useful and Productive Work in form of Assignments)
- Learner has to complete and upload minimum 2 out of 5 Assignments
Certificate
- MKCL provides certificate (for 30/60/90 hours courses) to the KLiC learner after his/her successful course completion.
Academic Approach
The Academic Approach of the course focuses on the “work centric” education i.e. begin with work (and not from a book !), derive knowledge from work and apply that knowledge to make the work more wholesome, useful and delightful. The ultimate objective is to empower the Learner to engage in socially useful and productive work. It aims at leading the learner to his/her rewarding career as well as development of the society.
Learning methodology
- Learners are given an overview of the course and its connection to life and work.
- Learners are then exposed to the specific tool(s) used in the course through the various real-life applications of the tool(s).
- Learners are then acquainted with the careers and the hierarchy of roles they can perform at workplaces after attaining increasing levels of mastery over the tool(s).
- Learners are then acquainted with the architecture of the tool or Tool Map so as to appreciate various parts of the tool, their functions and their inter-relations.
- Learners are then exposed to simple application development methodology by using the tool at the beginner’s level
- Learners then perform the differential skills related to the use of the tool to improve the given ready-made outputs.
- Learners are then engaged in appreciation of real-life case studies developed by the experts.
- Learners are then encouraged to proceed from appreciation to imitation of the experts.
- After imitation experience, they are required to improve the expert’s outputs so that they proceed from mere imitation to emulation.
- Finally, they develop the integral skills involving optimal methods and best practices to produce useful outputs right from scratch, publish them in their ePortfolio and thereby proceed from emulation to self-expression.
Courses Fee Structure from 01 July, 2025 Onwards
KLiC 60 hour course fee applicable from 01 July, 2025 all over Maharashtra| KLiC Course Duration | MFO: MKCL Share (Including 18% GST) |
ALC Share (Service Charges to be collected by ALC) |
|---|---|---|
| 60 hours | Rs. 500/- | Rs. 2,500/- |
Important Points:
* Above mentioned fee is applicable for all Modes of KLiC Courses offered at Authorised Learning Center (ALC) and at Satellite Center
* Total fee is including of Course fees, Examination fees and Certification fees
* MKCL reserves the right to modify the Fee anytime without any prior notice
* Above mentioned fee is applicable for all Modes of KLiC Courses offered at Authorised Learning Center (ALC) and at Satellite Center
* Total fee is including of Course fees, Examination fees and Certification fees
* MKCL reserves the right to modify the Fee anytime without any prior notice
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