Your Ultimate Handbook for Mastering Data Analytics
Data Analytics is a course that focuses on examining raw data to uncover patterns, draw conclusions, and support decision-making. It covers key areas such as statistical analysis, data visualization, predictive modeling, and business intelligence. The course equips learners with the knowledge and tools to collect, process, and interpret complex datasets, extract meaningful insights, and communicate findings effectively to drive strategic actions across various organizational contexts.
Propgram Duration
12 Months
Time Commitment
12-15 Hrs/Week
Placement Support
900+ Companies
Enrollment
Highly Selective
How Can We Turn You Into an Expert in data analysts?
In-depth
Knowledge
Real World
Simulations
Placement
Assistance
Industry Requirements
What Tech Companies search for?
Amazon
Philips Engineering Solutions
International Business Machines
Microsoft Corporation
Reliance
One97 Communications
Samsung Electronics
Salesforce Inc.
Wipro Limited
Work Now Locally
Zensar
Tata Consultancy Services
Persistent
ANI Technologies Pvt. Ltd.
Groww (Nextbillion Technology)
Go Digit General Insurance
Required Skills
THE ONLY data analytics COURSE THATMakes You Industry-Ready & Future-Proof
Be job-ready for roles like Data Analyst, Data Engineer, and Business Intelligence Analyst with this comprehensive data analytics program built for today’s data-driven world.
Prepare for industry-recognized certifications like Microsoft PL-300, Google Data Analytics, and AWS Data Analytics—with structured modules, hands-on projects, quizzes, and expert mentoring to boost your success.
Gain real-world experience with tools like SQL, Excel, Power BI, and Python. Clean and analyze datasets, build dashboards, and uncover insights that drive decisions. Practice like a pro—hands-on.
Our Curriculum
Expert-Design Course Structure
Python Basics
Why Learn This
This course covers fundamental Python programming concepts essential for beginners and those seeking to strengthen their foundation. Students will learn core Python syntax, built-in data structures, and basic operations while developing practical coding skills applicable to various domains including scripting, automation, and application development.
1.Basic syntax, String formatting, Multiple arguments, End and separator parameters
2.Single-line comments, Multi-line comments, Docstrings, Best practices
3.Lists, Dictionaries, Tuples, Sets, Numbers, Strings, Boolean
4.Concatenation, Slicing, Formatting, String methods
5.Input function, Command-line arguments, File handling, Standard streams
6.F-strings, Format method, String modulo operator, Template strings
7.Copy module, Deep copying collections, Custom object copying, Performance considerations
8.List slicing, Dictionary copy method, Shallow copy limitations, Nested data structures
9.Arithmetic, Logical, Comparison, Assignment, Bitwise, Identity, Membership
Business Statistics
Why Learn This
Business Statistics provides essential analytical tools for data-driven decision making in corporate environments. This course focuses on statistical methods used to analyze business data, interpret results, and draw meaningful conclusions. Students will learn fundamental statistical concepts, probability theory, hypothesis testing, and regression analysis to solve real-world business problems and support strategic planning.
1.Types of data, Descriptive vs. Inferential statistics, Statistical thinking in business, Measuring central tendency and dispersion
2.Basic counting principles, Probability rules, Normal distribution, Binomial and Poisson distributions
3.Central limit theorem, Standard error, Sampling techniques, Confidence intervals
4.Point and interval estimation, Null and alternative hypotheses, Type I and Type II errors, p-values and significance levels
5.Interpreting scatter plots, Identifying relationships, Outliers detection, Visualization techniques
6.One-way and two-way ANOVA, F-test interpretation, Chi-square test of independence, Goodness-of-fit tests
7.Handling missing data, Mean/median imputation, Regression imputation, Multiple imputation methods
8.Identifying data quality issues, Handling outliers, Data transformation, Standardization and normalization
9.Correlation coefficients, Simple linear regression, Multiple regression analysis, Model evaluation and diagnostics
Introduction to Data Analytics
Why Learn This
Data Analytics is an essential discipline that enables organizations to extract meaningful insights from raw data. This course provides a comprehensive foundation in data analytics concepts, methodologies, and applications across industries. Students will learn the different types of analytics approaches, data interpretation techniques, and how analytics drives business decision-making and strategic planning.
1.Definition, History of data analytics, Analytics lifecycle, Industry applications
2.Business value creation, Data-driven decision making, Competitive advantage, Performance optimization
3.Classification of analytics methods, Comparison of approaches, Use case selection, Implementation considerations
4.Historical data analysis, Summary statistics, Data visualization, KPI development
5.Root cause analysis, Correlation vs. causation, Investigation methods, Anomaly detection
6.Forecasting techniques, Predictive modeling, Machine learning applications, Pattern recognition
7.Optimization methods, Decision modeling, Recommendation systems, Automated decision-making
8.Cost reduction, Faster decision making, Risk mitigation, Revenue growth opportunities
9.Visualization principles, Chart selection, Interactive dashboards, Storytelling with data
10.Categorical vs numerical data, Mean/median/mode, Standard deviation, Range and IQR
11.Histograms, Distribution shapes, Outlier identification, Visual data analysis
12.Statistical summaries, Data profiling, Variable relationships, Cross-tabulation analysis
13.Sample selection methods, Distribution of sample means, Statistical inference, Margin of error
Excel: Basics
Why Learn This
Microsoft Excel is a powerful spreadsheet application widely used for data analysis, financial modeling, and business reporting. This comprehensive course takes students from Excel fundamentals to advanced techniques, covering essential functions, data manipulation, visualization, and automation to enhance productivity and analytical capabilities in professional environments.
1.Interface navigation, Workbook structure, Basic formatting, Keyboard shortcuts
2.Delimiter selection, Fixed width splitting, Advanced options, Data cleanup
3.String joining methods, Multiple cell combinations, Error handling, Text manipulation
4.Syntax and parameters, Formula construction, Alternative methods, Practical applications
5.Text extraction techniques, Combined function usage, Character counting, Format conversion
6.$A$1 notation, Mixed references, Formula copying, Reference management
7.Input restrictions, Drop-down lists, Custom validation rules, Error alerts
8.Date functions, Duration calculations, Working days, Custom date formats
9.Rule types, Visual indicators, Formula-based conditions, Managing multiple rules
10.Cell styles, Style inheritance, Format clearing options, Style management
11.Visual hiding techniques, Custom formatting rules, Zero-height rows, White text methods
12.Logical tests, TRUE/FALSE conditions, Nested IF statements, Error trapping
13.Text outputs, Multiple conditions, Combining with other functions, Format considerations
14.Data source preparation, Table structure, Field configuration, Refresh options
15.Data selection, Field arrangement, Value summarization, Layout options
16.Data source management, External data connections, Field selection, Dynamic ranges
17.Value field settings, Custom calculations, Summarization methods, Number formatting
18.Filter fields, Slicer implementation, Sort orders, Top/bottom filters
19.Chart type selection, Field mapping, Visual customization, Interactive elements
20.Date grouping, Numeric grouping, Custom groups, Hierarchy creation
21.Data refresh techniques, Automatic updates, Change detection, Source modifications
22.Style applications, Custom formatting, Banded rows/columns, Conditional formats
23.Slicer creation, Multi-selection, Visual customization, Connections to multiple PivotTables
24.Chart types overview, Data selection, Chart elements, Design principles
25.Basic chart creation, Data range selection, Chart positioning, Quick analysis tools
26.Discontinuous range selection, Custom data series, Series formula editing, Organization techniques
27.Step-by-step process, Chart customization, Advanced options, Finalizing charts
28.Element editing, Data range changes, Design adjustments, Format options
29.Positioning techniques, Sheet relocation, Chart objects, Alignment tools
30.Dimension adjustment, Aspect ratio, Precision sizing, Print considerations
31.Type conversion, Mixed chart types, Appropriate visualization selection, Format preservation
32.Column, Bar, Line, Pie, Scatter, Area, Surface, Radar, Treemap, Sunburst
33.Axis scaling, Data labels, Trendlines, Secondary axes
34.Legend positioning, Custom legend entries, Format customization, Visibility options
Excel: Advanced
Why Learn This
Advanced Excel skills are critical for data analysis, financial modeling, and business intelligence. This comprehensive course builds on Excel fundamentals to explore sophisticated features including advanced charting, data analysis techniques, range management, lookup functions, and pivot table mastery, enabling professionals to handle complex data manipulation tasks efficiently.
1.Custom chart templates, Advanced formatting options, Theme customization, Professional chart styling
2.Axis titles, Data labels, Error bars, Trendlines, Secondary axes, Chart annotations
3.Global text styles, Font schemes, Conditional text formatting, Text rotation and alignment
4.Custom number formats, Currency display options, Data alignment techniques, Decimal precision control
5.Background customization, Gridlines configuration, Plot area borders, 3D effects management
6.Custom marker styles, Data point customization, Series overlap settings, Gap width adjustments
7.Pie chart variations, Donut charts, Exploded views, Percentage vs. value display options
8.Data selection strategies, Optimal data arrangement, Category grouping techniques, Proper data proportions
9.Chart sheet creation, Sheet organization, Navigation enhancements, Printing optimization
10.Label position options, Data label content selection, Custom label formatting, Multi-level labeling
11.Single slice explosion, Multiple slice adjustments, Percentage offset control, Visual emphasis techniques
12.Analysis principles, Excel analysis capabilities, Statistical methods, Data interpretation approaches
13.Descriptive analysis, Inferential analysis, Predictive modeling, What-if scenario planning
14.Data collection, Data cleaning, Analysis methodology, Result interpretation and presentation
15.Named range benefits, Dynamic ranges, Name scope management, Formula readability improvements
16.Autocomplete techniques, Efficient formula building, Name selection shortcuts, Cross-sheet referencing
17.Naming conventions, Invalid characters, Reserved names, Name length limitations
18.Different methods for creating names, Range name dialog, Name from selection, Table-based naming
19.Formula-defined constants, Workbook constants, Global constants, Constant referencing
20.Name manager interface, Bulk name operations, Name auditing, Scope visualization
21.Workbook-level names, Sheet-level names, Scope conflicts, Precedence rules
22.Name modification techniques, Reference updates, Scope changes, Name dependency management
23.Converting cell references to names, Apply names dialog, Formula updates, Name application options
24.Name-based formulas, Name validation, Error handling with names, Complex name references
25.Name listing methods, Usage identification, Reference tracing, Dependency visualization
26.Preserving name references, Relative vs. absolute name usage, Cross-sheet formula copying, Name adaptation
27.Table advantages, Structured references, Dynamic expansion, Table vs. range functionality
28.Table creation process, Header row options, Table sizing considerations, Source data preparation
29.Naming conventions, Name uniqueness, Table name usage, References using table names
30.Structured references, Column header references, Formula construction, Table element navigation
31.Structured reference syntax, Header-based formulas, Formula readability, Dynamic column references
32.Auto-fill behavior, Column formula consistency, Formula adjustments, Reference management
33.Manual resizing, Automatic expansion, Header row preservation, Data inclusion management
34.Duplicate detection settings, Unique value identification, Column-based deduplication, Data preprocessing
35.Table conversion process, Reference preservation, Format retention, Post-conversion management
36.Banded rows/columns, Header row formatting, Total row customization, First/last column emphasis
37.Built-in style gallery, Custom table styles, Conditional formatting integration, Corporate style matching
38.TRIM, CLEAN, SUBSTITUTE, REPLACE, Text parsing techniques, Character removal strategies
39.Space elimination, Control character removal, Special character handling, Consistent text formatting
40.Text parsing functions, Regular expressions, Text-to-columns alternatives, Pattern-based extraction
41.PROPER, UPPER, LOWER, concatenation techniques, Case transformation, Text standardization
42.Custom date formats, International date standards, Date component extraction, Date conversion techniques
43.Advanced rules, Icon sets, Data bars, Color scales, Multi-condition formatting, Formula-based conditions
44.Multi-level sort criteria, Custom sort lists, Case-sensitive sorting, Sorting with formulas, Sort by color/icon
45.Advanced filter criteria, Custom filters, Filter by selection, Complex conditions, Wildcard filtering
46.VLOOKUP, HLOOKUP, INDEX-MATCH, XLOOKUP, Approximate vs. exact matching, Lookup array optimization
47.Advanced pivot table techniques, Calculated fields, Report layouts, Grouping options, Multiple data sources, Power Pivot integration
SQL Database Management
Why Learn This
SQL is the standard language for relational database management systems. This course covers Oracle Database fundamentals, data manipulation, query optimization, and database administration to help you effectively manage and retrieve data.
1.Database concepts, Architecture, Installation
2.Basic queries, Column aliases, Concatenation
3.WHERE clause, ORDER BY, Comparison operators
4.Character, Number, Date functions
5.TO_CHAR, TO_DATE, CASE statements
6.SUM, AVG, COUNT, GROUP BY, HAVING
7.INNER, OUTER, SELF joins
8.Single-row, Multiple-row subqueries
9.UNION, INTERSECT, MINUS
10.INSERT, UPDATE, DELETE, MERGE
11.CREATE, ALTER, DROP commands
12.Views, Sequences, Indexes, Synonyms
13.Privileges, Roles, Security policies
14.Table maintenance, Constraints
15.System catalogs, Metadata queries
16.Bulk operations, Performance considerations
17.Timestamp with timezone, Conversions
18.Correlated subqueries, Inline views
19.REGEXP functions, Pattern matching
READY FOR DATA ANALYST ROLES
Covering all modules above makes you ready to apply for Data Analyst roles
Tableau Course Material
Why Learn This
Tableau is a powerful data visualization tool that enables users to create interactive and shareable dashboards. This course covers connection to various data sources, data manipulation techniques, and visualization best practices to help analysts effectively communicate insights through visual representations.
1.Navigation overview, Interface fundamentals
2.Visualization recommendations, Chart selection guide
3.Import methods, Data source configuration
4.CSV handling, Delimiter options, Text formatting
5.Database connections, SQL queries, Live connection vs extract
6.OLAP cubes, Dimensions, Measures
7.Custom hierarchies, Drill-down functionality
8.Data grouping, Range creation, Distribution analysis
9.Inner joins, Left joins, Data relationships
10.Multi-source visualization, Primary and secondary sources
Learn Tableau Basic Reports
Why Learn This
Tableau Basic Reports focuses on essential reporting techniques to transform data into meaningful visualizations. This course covers parameters, grouping methods, sets, data organization, and formatting options to help analysts create professional, insightful reports for effective data communication.
1.Dynamic inputs, User interactivity, Parameter controls
2.Basic grouping techniques, Manual grouping, Group visualization
3.Advanced grouping methods, Automatic grouping options
4.Modifying existing groups, Group management, Renaming
5.Creating custom sets, Fixed sets, Condition-based sets
6.Set operations, Unions, Intersections, Set actions
7.Report structure, Layout options, Best practices
8.Label formatting, Customization, Dynamic labeling
9.Organizing fields, Folder management, Workspace optimization
10.Sort options, Custom sorts, Hierarchical sorting
11.Summary calculations, Table calculations, Aggregation methods
Learn Tableau Charts
Why Learn This
Visualization charts are fundamental to effective data storytelling and analysis. This course covers a comprehensive range of Tableau chart types from basic to advanced, teaching you when and how to use each visualization technique. Master these charts to transform complex data into clear, compelling visual insights.
1.Filled time series, Stacked areas, Showing volumes over time
2.Categorical comparisons, Standard bars, Horizontal and vertical orientation
3.Statistical distribution, Quartiles, Outlier detection
4.Multi-dimensional data, Size and color encoding, Scatter variations
5.Ranking changes over time, Position shifts, Comparative rankings
6.Performance against targets, Compact metrics, Stephen Few's design
7.Packed bubbles, Circle packing, Proportional visualization
8.Multiple measures, Mixed chart types, Dual axes
9.Two y-axes, Comparing different scales, Time series comparison
10.Process stages, Conversion visualization, Drop-off analysis
11.Sales pipeline, Step reduction, Conversion rates
12.Project timelines, Task duration, Schedule visualization
13.Multi-category comparison, Clustered bars
14.Color intensity matrices, Value concentration, Pattern recognition
15.Cell coloring, Tabular data visualization, Conditional formatting
16.Frequency distribution, Data ranges, Value clustering
17.Running totals, Accumulation visualization, Progressive counts
18.Time series trends, Continuous data, Connection visualization
19.Dot-line combination, Space-efficient bars, Categorical comparison
20.80/20 rule, Sorted bars with line, Cumulative percentage
21.Part-to-whole relationships, Proportional slices, Categorical breakdown
22.Correlation analysis, X-Y plotting, Point distribution
23.Component parts, Accumulating values, Part-to-whole over categories
24.Numeric displays, KPI visualization, Pure text representation
25.Hierarchical data, Nested rectangles, Size and color dimensions
26.Text frequency, Term importance, Word sizing by value
27.Sequential additions/subtractions, Running total, Financial flows
YOU'RE NOW READY FOR SRE ROLES
Covering all modules above makes you ready to apply for Data Analyst roles
Learn Tableau Advanced Reports
Why Learn This
This course covers advanced Tableau reporting techniques essential for data analysts and visualization professionals. Students will master dual axis visualizations, reference lines, advanced mapping, and custom backgrounds while developing practical skills applicable to complex data storytelling, geographic analysis, and professional dashboard creation.
1.Combining multiple measures, Synchronized axes, Mixed mark types, Custom visualizations
2.Combining related data sources, Axis alignment, Integration techniques, Relationship building
3.Separate axis control, Independent scaling, Custom range settings, Multi-measure displays
4.Static and dynamic references, Constant lines, Statistical markers, Performance indicators
5.Range highlighting, Confidence intervals, Target zones, Comparative regions
6.Statistical distributions, Percentile markers, Box plots, Distribution curves
7.Geographic visualization basics, Coordinate mapping, Regional data display, Choropleth maps
8.Custom markers, Size encoding, Color encoding, Multi-dimension geographic visualization
9.Google integration, Interactive mapping, Street views, Custom location overlays
10.Custom map styles, Mapbox integration, Interactive backgrounds, Geographic layers
11.Web Map Service connection, Custom geographic data sources, Enterprise mapping
Learn Tableau Calculations & Filters
Why Learn This
Tableau Calculations & Filters provides essential techniques for data manipulation and analysis in Tableau. This course focuses on creating calculated fields, ranking methods, running totals, and implementing various filtering approaches. Students will learn fundamental calculation concepts, filter types, and optimization techniques to transform raw data into actionable insights and create dynamic, interactive visualizations.
1.Creating custom calculations, Formula syntax, Common functions, Logic operations, Aggregation methods
2.Standard ranking methods, Rank functions, Parameter-based ranking, Sorting with ranks
3.Complex ranking scenarios, Multi-level ranking, Dynamic rank calculations, Custom rank display
4.Progressive summation, Table calculations, Running total options, Quick table calculations
5.Filter types overview, Filter mechanics, Order of operations, Filter architecture
6.Interactive dashboard controls, User-facing filters, Quick filter customization, Filter actions
7.Categorical filtering, Include/exclude methods, Custom lists, Wildcard filtering
8.Logical filtering, IF/THEN conditions, Formula-based filters, Dynamic conditional filters
9.N-value filtering, Top/bottom parameters, Dynamic top N analysis, Comparative filtering
10.Numeric range filters, Continuous vs. discrete filtering, Relative filtering, Value distribution
11.Performance optimization, Filter hierarchy, Context setting, Dependent calculations
12.Cross-dimensional filtering, Matrix analysis, Categorical segmentation, Comparative slicing
13.Connection-level filtering, Pre-processing data, Source optimization, Extract preparation
14.Optimizing extracts, Incremental extracts, Extract efficiency, Data reduction techniques
Learn Tableau Dashboards
Why Learn This
Tableau Dashboards are powerful tools for combining multiple visualizations into cohesive, interactive business intelligence displays. This course provides a comprehensive foundation in dashboard design, layout, interactivity, and storytelling techniques. Students will learn essential dashboard development skills, best practices for user experience, and methods to create compelling data narratives for stakeholders.
1.Dashboard fundamentals, Worksheet integration, Size and layout options, Design principles
2.Grid vs. floating elements, Container types, Size control, Visual hierarchy, White space management
3.Mobile-responsive design, Device-specific layouts, Dashboard sizing, Optimization for different screens
4.Global filters, Local filters, Filter actions, Interactive filtering techniques, Parameter controls
5.Text objects, Image integration, Web page objects, Blank objects, Navigation buttons, Layout containers
6.Sequential narratives, Point structure, Story design, Progressive data revelation, Guided analytics
Tableau Server
Why Learn This
This course covers essential Tableau Server concepts necessary for publishing, sharing, and managing Tableau content in enterprise environments. Students will learn cloud and on-premises deployment options, publishing workflows, and content management while developing practical administration skills applicable to organizational business intelligence and data governance requirements.
1.Cloud-based deployment, Subscription management, User permissions, Content organization, Collaboration features
2.Architecture components, Server roles, Site management, Authentication methods, Deployment options
3.Publishing workflows, Data source management, Extract refreshes, Subscription setup, Report distribution, Automated delivery
Introduction to Power BI
Why Learn This
Introduction to Power BI provides essential foundations for data visualization and business intelligence using Microsoft's Power BI platform. This course focuses on setup, data connectivity, and basic reporting techniques for effective data analysis. Students will learn fundamental Power BI concepts, connection methods, visualization basics, and portal navigation to create insightful reports and dashboards for business data analysis.
1.Installation process, Desktop vs. Service, Power BI ecosystem, First steps for beginners
2.Core components, Design philosophy, Data transformation workflow, Report distribution
3.Account creation, Subscription options, License types, Organization setup
4.Data connector types, Native integrations, Connection methods, Data refresh options
5.Cloud service connections, API integration, Authentication methods, Service data access
6.File import process, Data preview, Column type detection, Import settings
7.Excel workbook connections, Data model import, Refresh settings, OneDrive integration
8.Sample datasets, Learning resources, Practice data, Quick start templates
9.Visualization types, Report canvas, Layout options, Interaction settings
10.Navigation structure, Workspace management, Content organization, Sharing options
Viz and Tiles
Why Learn This
This module focuses on data visualization techniques and tools that transform raw data into meaningful visual representations. Students will learn how to create, format, and arrange various types of visualizations to effectively communicate insights. The module covers essential skills for building interactive dashboards, implementing filters, and customizing visualizations to meet specific analytical needs.
1.Visualization fundamentals, Types of visualizations, Visual perception principles, Choosing appropriate visualizations
2.Visualization purposes, Data-to-visual mapping, Effective use cases, Visualization limitations
3.Report setup, Data source connections, Report configuration, Layout planning
4.Visualization placement, Layout strategies, Visual hierarchy, Dashboard organization
5.Appearance customization, Color schemes, Labeling strategies, Design consistency
6.Bar/column charts, Line charts, Pie/donut charts, Scatter plots and bubble charts
7.Text elements integration, Geographic visualization, KPI gauges, Report saving and sharing
8.Slicer creation, Filter types, Cross-filtering, Interactive filtering techniques
9.Sorting mechanisms, Visualization duplication, Format consistency, Layout adjustments
10.Custom visual sources, Installation process, Custom visual configuration, Visual marketplace exploration
YOU'RE NOW READY FOR Power BI
Covering all modules above makes you ready to apply for Power BI roles
Reports and Dashboards
Why Learn This
This course explores the creation and management of professional reports and interactive dashboards for effective data storytelling. Students will learn techniques for report customization, dashboard design, content distribution, and natural language querying. The course emphasizes practical skills for building compelling visual analytics solutions that drive business insights and decision-making.
1.Report customization, Layout adjustments, Visual formatting, Content organization, Print settings
2.Page management, Organizing multi-page reports, Restructuring content, Report navigation
3.Filter creation, Filter types, Scope configuration, Interactive filtering, Cross-filtering
4.Cross-highlighting, Drill-through actions, Tooltip customization, Synchronizing visuals
5.Print formatting, Export options, Page setup, Print resolution, Output optimization
6.Export workflows, Slide configuration, Maintaining interactivity, Presentation formatting
7.Dashboard planning, Layout design, Visual arrangement, Information hierarchy, User experience
8.Dashboard creation, Template usage, Theme application, Mobile optimization
9.Pinning workflows, Tile configuration, Size adjustment, Position management
10.Live connections, Auto-refresh settings, Interactive elements, Full page integration
11.Cross-dashboard referencing, Content reuse, Visual consistency, Update behavior
12.Excel integration, Data connection, Element selection, Refresh settings
13.Element configuration, Data updates, Format management, Link maintenance
14.Custom tiles, Text elements, Media integration, Web content, Custom visuals
15.Automated analysis, Insight generation, Pattern detection, Visual suggestions
16.Default configuration, Navigation settings, User experience, Landing page setup
17.Natural language queries, Question formulation, Query optimization, Result interpretation
18.Q&A interface, Query syntax, Visual generation, Follow-up questions
19.Dataset optimization, Synonym configuration, Question suggestions, Phrasing improvements
20. Query setup, Cortana configuration, Voice command customization
Publishing Workbooks and Workspace
Why Learn This
This course focuses on collaborative aspects of business intelligence through effective sharing and publishing of Power BI content. Students will learn enterprise content distribution methods, workspace management, and embedding techniques for integrating reports into organizational platforms. The course covers essential skills for content governance, team collaboration, and audience-targeted analytics delivery in professional environments.
1.Sharing permissions, Distribution methods
2.Public publishing workflow, URL generation
3.Version control, Update processes, Audience management, Usage monitoring
4.Dashboard permissions, Direct sharing, Link distribution, Access level configuration
5.Workspace setup, User role assignment, Collaboration settings, Content organization
6.Collaborative editing, Content management, Team workflows, Development lifecycle
7.App creation, Content packaging, Distribution settings, Audience targeting
8.QR generation process, Mobile access, Scan functionality, Dynamic linking
9.SharePoint integration, Web part configuration, Authentication flow, Interactive embedding
Other Power BI Components and Table Relationship
Why Learn This
This module explores advanced Power BI components and data modeling techniques essential for comprehensive business intelligence solutions. Students will learn mobile app functionality, desktop application capabilities, and data relationship concepts. The course covers both consumption and development aspects of the Power BI ecosystem, providing skills for creating end-to-end analytics solutions across multiple platforms.
1.Mobile platform overview, App navigation, Mobile-optimized features, Offline capabilities
2.Installation process, Device compatibility, App configuration, Authentication setup
3.iPad interface, Touch interactions, Layout adaptation, Optimization techniques
4.Mobile workspace access, Content navigation, Permission management, Collaboration features
5.Mobile sharing options, Link generation, Access control, Recipient management
6.Desktop application overview, Development environment, Advanced features, Desktop workflow
7.System requirements, Installation process, Application setup, Configuration options
8.Data source connections, Import vs. DirectQuery, Advanced connectors, Connection parameters
9.Data filtering, Column selection, Row limitations, Performance optimization
10.Data cleaning operations, Column transformations, Custom calculations, Query editor features
11.Relationship creation, Cardinality settings, Filter direction, Model optimization
12.Desktop-to-service workflow, Publishing process, Gateway configuration, Refresh settings
13.Export functionality, File management, Version control, Development transitions
DAX Functions
Why Learn This
This course provides comprehensive coverage of Data Analysis Expressions (DAX) functions essential for advanced data modeling and analytics in Power BI. Students will learn various function categories and their applications for creating calculated columns, measures, and tables. The course develops practical skills for implementing complex calculations, time intelligence, and custom business logic to enhance data models and deliver sophisticated business intelligence solutions.
1.Latest additions, Function enhancements, Usage improvements, Compatibility considerations
2.Calendar manipulation, Time period calculations, Date formatting, Date table creation
3.Year-to-date analysis, Period comparisons, Rolling calculations, Fiscal period handling
4.Context manipulation, Filter propagation, Relationship traversal, Custom filtering logic
5.Data type evaluation, Error handling, Value testing, Metadata access
6.Conditional expressions, Boolean operations, Branching logic, Comparison techniques
7.Arithmetic operations, Statistical calculations, Scientific functions, Rounding methods
8.Hierarchy navigation, Path analysis, Level identification, Recursive calculations
9.String manipulation, Concatenation operations, Text extraction, Format conversion
Gain Real-World Data Analytics Experience!
Career-Boosting Projects
Data Analytics Dashboard
Build an interactive analytics dashboard using Python, Pandas, and visualization tools like Tableau or PowerBI. Extract insights from complex datasets and create compelling visualizations that drive data-informed decision-making.
Market Trend Analysis
Analyze market data to identify emerging trends and patterns using R or Python. Apply time series analysis, regression models, and forecasting techniques to predict future market movements and provide actionable business recommendations.
Customer Segmentation Analysis
Use clustering algorithms and demographic data to segment customers into meaningful groups. Implement K-means and RFM analysis to identify high-value customer segments and develop targeted marketing strategies.
Predictive Analytics Model
Develop machine learning models to predict business outcomes using historical data. Apply classification, regression, and ensemble methods to forecast sales, customer churn, or product demand with scikit-learn and TensorFlow.
Social Media Analytics
Analyze social media data to extract user sentiment and engagement patterns. Use NLP techniques and sentiment analysis to process text data, identify trends, and measure campaign effectiveness across multiple platforms.
Financial Data Analysis
Perform comprehensive analysis of financial data to identify investment opportunities and risks. Create financial models, conduct ratio analysis, and develop interactive reports to support investment decisions and portfolio management.
Data Analytics Curriculum
Your Journey With Careertronic
Onboarding Session
Intro Session
Start in a customized cohort and forge meaningful connections who will be your allies on this journey.
Select the right mentor for guidance and gain invaluable insights to boost your career.
Connect with a Learning Coordinator
Live Learning Experience
Live Classroom
Engage with instructors and connect with your peers in real-time
Practice with
Assignments & Home Works
1:1
Guidance from Pro Mentors
Cloud Sandbox
Hands-on practice in real-world cloud environment
AI-Assisted
Problem-solving support
Situational
Problem & Solution
Teaching-Assistance
1:1 Teaching Assistant over chat & video call
Live Classroom
Engage with instructors and connect with your peers in real-time
Practice with
Assignments & Home Works
1:1
Guidance from Pro Mentors
Cloud Sandbox
Hands-on practice in real-world cloud environment
AI-Assisted
Problem-solving support
Situational
Problem & Solution
Teaching-Assistance
1:1 Teaching Assistant over chat & video call
Practice with
Assignments & Home Works
1:1
Guidance from Pro Mentors
Situational
Problem & Solution
Live Classroom
Engage with instructors and connect with your peers in real-time
Cloud Sandbox
Hands-on practice in real-world cloud environment
AI-Assisted
Problem-solving support
Teaching-Assistance
1:1 Teaching Assistant over chat & video call
Training & Placement Support
Module-Based Mocks
Practically apply your skills through interview simulations post-module.
Resume Building
Build an impactful, professional resume with expert mentorship.
Placement Training
Focused training to excel in tech recruitment processes.
Placement Support
End-to-end assistance to secure your dream job.
Meet Mentors & Instructors
Tap into the wisdom of Data Analytics Experts
Anshuman Singh
Naman Balla
Anshuman Singh
Anshuman Singh
Aman Sharma
This course is a must for anyone preparing for system design interviews! The real-world case studies on Uber, Netflix, and WhatsApp helped me understand how large-scale applications work. The explanations on microservices and database scaling were crystal clear. Highly recommended!.
Priya Desai
Great content with detailed coverage of caching, message queues, and load balancing. The instructor explained concepts in a structured way, making them easy to grasp. I just wish there were more coding exercises to practice system design problems.
Rahul Verma
As a backend developer, this course helped me improve my architectural thinking. Learning about CAP theorem, database sharding, and security best practices gave me a deeper understanding of system scalability. Definitely worth it!.
Data Analytics Program
- Skills
- Certification
- Placement Support
Data Analytics Program + Project Certificate
- Skills
- Certification
- Placement Support
- Portfolio
- 5+ Projects Certificate
Frequently Asked Questions
General
Who can take up this course?
The course is specifically designed for Engineering students doing bachelor and master degree who wish to expand their knowledge in Automation Industrial persons and faculty members who would like to develop capabilities in Automation Individuals seeking career in domains in Industrial automation applications Graduates who seek job in electrical, instrumentation, automation domain.
What is included in your course?
This course is designed to include all requirements for a power electronic / Automation engineer or those required for research level jobs.
What will the student gain from your course?
With the evolution of automation technologies, the importance of Instrumentation, Control and Automation usage is increased significantly. Therefore, it is essential for an electrical / instrumentation engineer to understand this field thoroughly. In this course, students will get detailed theoretical knowledge and design insights with their control schemes. With this knowledge, students will be able to design, simulate and analyze the machine or process better.
How is this course going to help a student get a job?
As mentioned earlier as well, this course is designed to not only cover the basic concepts but also applications in the industrial systems. Further, from basic switching mode converter to details on the modulation scheme principle, all basics are covered in detail. Additionally, the techniques to control the industry scale products are also discussed. The challenges and projects given in this course, which students will be solving are indegineously designed to train them in handling any industrial problem. Therefore, the skill sets obtained by the student as a part of this course will help him to not only crack the entrance or technical interview for such jobs but also to lead any industrial challenge as a part of his job profile related to this field.
What are the job opportunities in this field?
Today, Automation components are prominently used in majorly all industrial system since the industrial revolution in Industry 4.0 has taken by storm. The major players in this area are ABB, Siemens, Fuji, Rockwell, Emerson, Mitsubishi, Alstom, Hitachi etc. These companies supply and use various products like PLC, HMI, DCS, SCADA, HMI, IIoT, Field Instrumentation, Analyzers etc. The techniques taught in this course will be directly applied to design and analyse these systems and thus in above mentioned industries.