Data Analytics Course Online India | NeoNex MindX
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Live Online Data Analytics Course in India

Stop Watching Dashboards. Start Building Ones People Act On.

Learn the complete data analytics workflow, from cleaning raw data and writing SQL queries to analysing trends, testing ideas and presenting business insights through dashboards.

Built for freshers, working professionals and career switchers who want practical skills they can demonstrate in an interview, not just list on a resume.

  • ✓Live instructor-led online sessions
  • ✓Excel, SQL, Python, Power BI, Tableau and statistics
  • ✓Hands-on assignments using business datasets
  • ✓Portfolio-focused analytics projects
  • ✓3–6 month internship component with certification
  • ✓Lifetime LMS access and recorded sessions
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South Asian data analyst working with business dashboards in a bright modern office
Program Snapshot

Data Analytics Program at a Glance

Program Duration5–6 months
Training ModeLive online, interactive sessions
Coding RequirementNo previous coding experience required
Core ToolsExcel, SQL, Python, Power BI, Tableau, Pandas and NumPy
Learning AccessLMS, recordings, notes and resources
Practical LearningAssignments, projects and portfolio outputs
Internship3–6 month component with certification
Batch OptionsWeekday morning, evening and weekend
Course Recommendation

Find Out Whether Data Analytics Fits Your Career Goal

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Direct Answer

What Is a Data Analytics Course?

A data analytics course teaches you how to collect, clean, analyse and visualise data so organisations can understand performance and make better decisions. A complete program covers spreadsheet analysis, databases, programming, statistics, dashboards and business communication, not just one tool.

This online Data Analytics course in India combines Excel, SQL, Python, Pandas, NumPy, Power BI, Tableau, statistics, exploratory data analysis and business intelligence.

You learn how to move from a business question to a reliable dataset, an analysis, a dashboard and a clear recommendation.

Focused data analytics workspace with spreadsheets, SQL, Python code and dashboard visuals
The Learning Gap

You Do Not Need More Random Tutorials. You Need a Complete Analyst Workflow.

Many learners can follow a dashboard video or copy a Python notebook but still struggle when they receive an unfamiliar dataset. This program is designed to close that gap.

Too many tools with no learning orderFollow one structured path across Excel, SQL, Python, statistics, Power BI and Tableau.
Coding feels intimidatingStart with guided Python foundations and use code for practical analysis instead of abstract drills.
Projects look copiedBegin with a business problem, document your decisions and explain the insight behind every chart or query.
Interviews expose weak fundamentalsPractise SQL, statistics, dashboard logic, case questions and project explanation throughout the program.
There is no proof on the resumeBuild notebooks, SQL reports, dashboards and business case studies that can be organised into a portfolio.
Learning Outcomes

What You Will Be Able to Do After the Program

Build reliable data workflows, answer business questions and present analysis that stakeholders can act on.

Prepare Reliable Data

  • ✓Clean and transform messy Excel, CSV, JSON and database data
  • ✓Identify missing values, duplicates and inconsistent records
  • ✓Build repeatable data-preparation and validation workflows

Query and Analyse Business Data

  • ✓Write SQL queries using joins, aggregations, subqueries and CTEs
  • ✓Analyse datasets using Python, Pandas and NumPy
  • ✓Identify trends, outliers and patterns through exploratory analysis

Apply Statistics Correctly

  • ✓Use descriptive statistics to summarise data
  • ✓Interpret confidence intervals, hypothesis tests and p-values
  • ✓Analyse experiments and A/B test results responsibly

Build Decision-Ready Dashboards

  • ✓Create interactive dashboards in Excel, Power BI and Tableau
  • ✓Define KPIs connected to real business questions
  • ✓Present analytical findings as practical recommendations

Present Professional Portfolio Work

  • ✓Document analysis through notebooks, reports and case studies
  • ✓Organise projects through GitHub or a portfolio presentation
  • ✓Explain decisions, results and limitations during interviews
Who Should Join

Who Should Join This Data Analytics Course?

Fresh Graduates and Final-Year Students

Build an employable analytics foundation before applying for analyst roles.

Commerce, BBA, MBA and Non-Technical Learners

Move into analytics through Excel, SQL, dashboards and guided Python.

Working Professionals

Improve reporting and decisions across operations, marketing, finance, HR or product teams.

Career Switchers

Replace random self-learning with a structured roadmap, mentor feedback and portfolio work.

Excel and Reporting Professionals

Progress from manual reporting to SQL, automation, BI dashboards and deeper analysis.

Early-Career Technical Professionals

Add business analytics, visualisation and stakeholder communication to your technical skills.

Entry requirement: No previous coding experience is required. Basic computer knowledge, consistent practice and willingness to solve problems are enough to begin.
Data analytics learner progressing through dashboards, reports, projects and guided practice
Learning Experience

From Foundation to Interview-Ready Portfolio

Move through one connected learning path from analyst foundations and live practice to projects, a capstone and interview preparation.

01

Understand the Workflow

Learn the data lifecycle and the role of each tool.

02

Practise Live

Work through examples and ask questions.

03

Complete Assignments

Create queries, notebooks, reports and dashboards.

04

Build Connected Projects

Use multiple tools inside one workflow.

05

Create Your Capstone

Take a business problem through to recommendation.

06

Prepare for Interviews

Practise SQL, statistics and project explanation.

07

Continue Through LMS

Revisit recordings, notes and learning resources.

Curriculum Overview

Data Analytics Course Curriculum

The curriculum moves from foundations to business-ready analysis. Every module supports a visible output, so you can show what you learned instead of only listing tools on your resume.

Phase 1

Analyst Foundations

Business problems, data lifecycles, programming foundations and relational data.

Phase 2

Data Preparation and Analysis

Python, Pandas, NumPy, SQL, MongoDB and Excel for structured and semi-structured data.

Phase 3

Statistics, Dashboards and BI

Statistical reasoning and decision-ready dashboards through Power BI and Tableau.

Phase 4

Data Models and Capstone Work

ETL, data quality, GitHub, documentation, real-time projects and career preparation.

01Analytics Foundations and Data LifecycleBuild the analyst mindset before tool training begins.

Topics covered

  • Business problem framing and requirement gathering
  • KPI identification and dataset understanding
  • Structured and unstructured data
  • Data quality awareness
  • Analytical documentation

What you will build: A business problem brief and KPI map.

02Python Programming for Data AnalyticsDevelop programming and data-handling ability for practical analysis.

Topics covered

  • Python setup, IDEs, variables, data types, operators and expressions
  • Conditionals, loops, functions, arguments, lambda functions and scope
  • Modules, packages, strings, lists, tuples, dictionaries and sets
  • Comprehensions, enumerate and zip
  • Exception handling and file handling
  • CSV and JSON processing
  • Regular expressions
  • Object-oriented programming foundations for maintainable scripts

What you will build: A Python-based data-preparation and analysis assignment.

03NumPy, Pandas, Visualisation and Exploratory Data AnalysisTurn Python knowledge into an end-to-end analysis workflow.

Topics covered

  • NumPy arrays, indexing, slicing, reshaping and broadcasting
  • Matrix and statistical operations
  • Pandas Series and DataFrames
  • Data import, cleaning, missing values, filtering and sorting
  • GroupBy, merge, join, pivoting and aggregation
  • Matplotlib and Seaborn charts
  • Distributions, box plots, heat maps and correlation views
  • Exploratory data analysis
  • Outlier detection, feature distributions and pattern discovery

What you will build: A documented exploratory data analysis notebook with visual findings.

04SQL for Data AnalyticsQuery relational data and produce reusable business reports.

Topics covered

  • RDBMS concepts, database and table creation
  • SQL categories, data types and constraints
  • SELECT, WHERE, ORDER BY, GROUP BY, HAVING, DISTINCT and CASE
  • Aggregate functions
  • Inner, left, right, full, self and cross joins
  • Set operators and subqueries
  • Normalisation, keys and indexes
  • Views and common table expressions
  • Query optimisation
  • Functions, stored procedures, transactions, sequences and triggers at an applied level
  • Sales, customer, inventory and financial reporting queries

What you will build: A SQL analysis pack for a business dataset.

05MongoDB for AnalyticsWork with document databases and semi-structured data.

Topics covered

  • Collections, documents and BSON
  • MongoDB Compass and database structure
  • CRUD, filters, projection, sorting and limiting
  • Bulk operations
  • Aggregation pipeline, operators and expressions
  • Reporting queries
  • Embedded documents, referencing and relationships
  • Indexing, replication and sharding awareness

What you will build: A MongoDB aggregation report using a semi-structured dataset.

06Excel for Data AnalysisMaster spreadsheet analysis, reporting and automation used by business teams.

Topics covered

  • Analytical mindset, interface, navigation and spreadsheet design
  • Data entry, formatting and formula control
  • Arithmetic formulas and cell references
  • Error handling
  • Aggregation, lookup, text, date, time and conditional functions
  • Sorting, filtering, Excel Tables and data validation
  • PivotTables, PivotCharts and dashboard design
  • Data storytelling
  • Power Query for ETL
  • Merge, append, unpivot and data profiling
  • Database connections
  • Macros and VBA foundations
  • Repetitive-task automation and user-defined functions

What you will build: An interactive Excel dashboard and automated reporting workflow.

07Statistics, Probability and Mathematical FoundationsSupport reliable analysis, experimentation and interpretation.

Topics covered

  • Data types, population, samples and measurement scales
  • Sampling methods
  • Mean, median, mode, variance and standard deviation
  • Quartiles, percentiles and distributions
  • Normal distribution, skewness and kurtosis
  • Outlier analysis and confidence intervals
  • Hypothesis testing and p-values
  • Type I and Type II errors
  • Z-test, T-test, ANOVA and Chi-square
  • Correlation and covariance
  • Experimental design and A/B testing
  • Probability rules and conditional probability
  • Bayes theorem and common distributions
  • Vectors, matrices, distance, similarity and PCA intuition

What you will build: A statistical analysis and A/B test interpretation report.

08Power BI for AnalyticsBuild interactive business reports and publishable dashboards.

Topics covered

  • Power BI Desktop, interface and report workflow
  • Data connectivity
  • Power Query cleaning and transformations
  • Merge, append, data profiling and validation
  • Relationships and data modelling
  • Star and snowflake schemas
  • Fact and dimension tables
  • Model optimisation
  • Measures, calculated columns and KPIs
  • Logical calculations and time intelligence
  • Charts, cards, maps, filters and slicers
  • Drill-through, bookmarks and navigation
  • Power BI Service, publishing and workspaces
  • Sharing, scheduled refresh and gateway foundations
  • SQL connectivity and source-change handling

What you will build: A multi-page Power BI dashboard with an executive summary.

09Tableau for AnalyticsCreate flexible visual analysis and storytelling experiences.

Topics covered

  • Tableau products, interface and data connections
  • Live connections and extracts
  • Fields and data types
  • Dimensions, measures and hierarchies
  • Groups, sets, parameters and calculated fields
  • Joins and blending
  • Filters, table calculations and level-of-detail expressions
  • Core and advanced charts
  • Maps, forecasting, trends and clustering
  • Dashboards, actions and device layouts
  • Story points and performance optimisation
  • Tableau Online publishing, projects, permissions and sharing

What you will build: An interactive Tableau dashboard and data story.

10Business Intelligence, KPI and Decision AnalyticsConnect analytical output to business performance and decisions.

Topics covered

  • Business intelligence, data analytics and business analytics
  • Strategic, tactical and operational decisions
  • KPI frameworks and metric trees
  • Leading and lagging indicators
  • Business health metrics
  • Process mapping and funnel analysis
  • Bottleneck analysis
  • Balanced scorecard and OKRs
  • Trend, variance and root-cause analysis
  • Decision-support and recommendation frameworks
  • Executive reporting
  • Business storytelling and stakeholder presentation

What you will build: A KPI tree, root-cause analysis and management recommendation deck.

11Data Modelling, ETL and Data Quality FoundationsBuild enough data-engineering context to work with reliable analytical models.

Topics covered

  • CRM, ERP, HRMS, POS, APIs, databases and spreadsheets as data sources
  • ETL and ELT
  • Extraction, transformation, loading and validation
  • Duplicate, missing-value, format and standardisation checks
  • OLTP and OLAP
  • Data warehouse foundations
  • Fact and dimension tables
  • Star schema
  • Power Query-based mini ETL workflow
  • Data lineage awareness

What you will build: A simple ETL flow and dimensional model for reporting.

12Complementary Professional ToolsPrepare for team workflows and responsible AI-assisted productivity.

Topics covered

  • Git and GitHub foundations for portfolio versioning
  • Responsible use of Generative AI for analysis support, documentation and ideation
  • Agile concepts
  • Jira awareness for project tracking
  • Communication, documentation and presentation standards

What you will build: An organised GitHub portfolio with project documentation.

13Capstone, Real-Time Projects and Career ReadinessCombine the full analytics stack into portfolio-ready work.

Topics covered

  • Business problem identification
  • Data collection and cleaning
  • SQL and Python analysis
  • Dashboard development
  • Sales, marketing, HR, finance, customer, inventory and experiment-analysis domains
  • Project documentation
  • Case-study presentation
  • Business recommendations
  • Resume and LinkedIn preparation
  • Job-portal profile setup
  • Mock interviews and technical assessments
  • Interview preparation

What you will build: A portfolio containing capstone and real-time business analytics projects.

Tools and Technologies

Tools Covered in the Data Analytics Program

Every tool is taught within a defined module and practical workflow rather than as an isolated software feature.

Microsoft ExcelPower QueryPythonPandasNumPySQLMongoDBPower BIDAXTableauMatplotlibSeabornJupyter NotebookGitGitHub
Projects and Portfolio

Build an Analytics Portfolio Around Real Business Questions

Your project work is designed to show how you think, not only whether you can operate a tool.

Data analyst building a portfolio with dashboards, reports, business charts and analytical project outputs
01

Sales Performance Dashboard

Create KPIs, compare regions and products, and recommend actions for improving revenue.

02

Marketing Campaign and A/B Test Analysis

Evaluate conversion behaviour and experiment results using statistics and clear visual reporting.

03

Customer Retention Analysis

Explore behavioural patterns and build a dashboard that highlights churn or retention signals.

04

Inventory and Operations Reporting

Use SQL and BI tools to identify delays, stock movement and operational bottlenecks.

05

HR or Workforce Analytics

Analyse hiring, attendance, attrition or performance data and communicate findings responsibly.

06

Executive Analytics Capstone

Take a business problem from requirement gathering through data preparation, analysis and recommendation.

What You Can Show Recruiters and Hiring Managers

Python exploratory analysis notebookReusable SQL reporting-query packExcel dashboard and automated workflowPower BI report with KPIs and commentaryTableau dashboard and data storyBusiness case study with recommendationsOrganised GitHub or portfolio presentation
Why NeoNex MindX

Why Learn Data Analytics with NeoNex MindX?

One Connected Curriculum

Learn how Excel, SQL, Python, statistics, Power BI and Tableau work together inside an actual analyst workflow.

Live Guidance, Not Passive Videos

Ask questions, practise during sessions and revisit recorded classes through the LMS.

Business-First Projects

Define the question, validate the data, identify the insight and recommend an action.

Portfolio Evidence

Build queries, notebooks, dashboards, documentation and presentations that demonstrate capability.

Flexible Learning Schedules

Choose weekday morning, weekday evening or weekend learning.

Structured Career Support

Prepare your resume, LinkedIn profile, job profiles, interview answers and project explanations.

Trainer-Led Learning

Meet the Trainers Supporting Your Data Analytics Journey

Learn analytics tools, business problem-solving, projects and professional readiness with trainers covering data, AI, engineering, development, QA, DevOps and communication skills. Trainer allocation may vary by module and batch.

Data analytics learner receiving online mentoring and preparing for interviews and career growth
Internship and Career Support

Build Skills, Portfolio Proof and Interview Readiness

Internship Component

The program includes a 3–6 month internship component with certification, subject to current program requirements and successful completion of the required learning and project milestones.

Career and Placement Assistance

✓Resume and LinkedIn profile development
✓Job-portal profile setup and optimisation
✓Portfolio and project-presentation review
✓Mock interviews and technical-assessment practice
✓Communication and interview-readiness support
✓Relevant opportunity and referral support where applicable
Important: NeoNex MindX provides placement assistance, not a guaranteed job. Hiring outcomes depend on learner capability, portfolio quality, communication, interview performance, employer requirements and market conditions.
Career Paths

Career Paths Supported by Data Analytics Skills

The program builds foundations relevant to roles such as Data Analyst, Reporting Analyst, Business Data Analyst, BI Analyst, Marketing Analyst, Operations Analyst, Product Analyst and MIS or Dashboard Analyst.

Job titles and entry requirements vary by employer, industry, prior experience and project quality.

Course Comparison

Data Analytics vs Data Science: Which Course Should You Choose?

Comparison Point Data Analytics Data Science
Primary Focus Reporting, trends, dashboards and business decisions Predictive models, machine learning and experimentation
Core Tools Excel, SQL, Python, Power BI and Tableau Python, SQL, statistics, scikit-learn and deployment tools
Coding Depth Beginner to intermediate Intermediate, with stronger mathematics and modelling
Best Suited For Analyst, BI, reporting and functional analytics roles Data science, predictive analytics and machine learning roles
Choose This When You want a direct path into business data analysis and dashboard roles You want to build and evaluate predictive models
Batch Schedule

Flexible Live Batches

Weekday Morning

7:00 AM–9:30 AM

Monday to Friday

Weekday Evening

8:00 PM–10:30 PM

Monday to Friday

Weekend Batch

9:00 AM–1:30 PM

Saturday and Sunday

Batch availability and start dates may vary. Confirm the current schedule during counselling.

Check the Next Available Batch
Frequently Asked Questions

Data Analytics Course FAQs

Data analytics is the process of collecting, cleaning, examining and visualising data to understand what happened, why it happened and what action a business can take. Data analysts commonly use Excel, SQL, Python, Power BI, Tableau and statistics.

Yes. The learning path starts with analytics foundations, Excel, Python basics and SQL fundamentals before moving into statistics, dashboards and end-to-end projects. You need basic computer knowledge and consistent practice, not previous coding experience.

Yes. Commerce, management, economics, science and other non-technical learners can enter data analytics. The program introduces coding gradually and connects technical tasks to familiar business questions such as sales, customer, finance and operations performance.

The curriculum covers Excel, Power Query, SQL, Python, Pandas, NumPy, MongoDB, Power BI, DAX, Tableau, Matplotlib, Seaborn, Jupyter Notebook and Git/GitHub foundations.

Yes. Project work is integrated throughout the course. Learners build analysis notebooks, SQL reports, Excel and BI dashboards, KPI frameworks and an end-to-end business analytics capstone suitable for portfolio presentation.

The program includes a 3–6 month internship component with certification, subject to current program requirements and successful completion of the required learning and project milestones.

Yes. Placement assistance includes resume development, LinkedIn and job-profile optimisation, mock interviews, technical preparation, project-presentation support and relevant opportunity sharing or referrals where applicable. Employment is not guaranteed.

Data Analytics focuses more on preparing, querying and visualising data. Business Analytics gives greater emphasis to KPIs, processes, management decisions and stakeholder recommendations. The two overlap, but the preferred career direction is different.

Data Analytics usually focuses on reporting, dashboards, trends and decision support. Data Science goes deeper into probability, machine learning and predictive modelling. Data Analytics is often the more accessible starting point for beginners.

Yes. NeoNex MindX provides LMS access with class recordings, notes and additional learning resources so you can revisit topics and catch up when you miss a session.

Current schedule options include weekday morning, weekday evening and weekend batches. Speak to the course advisor to confirm seat availability and the start date for your preferred schedule.

Submit the enquiry form or call 084318 03839. The course advisor will share the current fee, available payment options, EMI information, batch date and complete syllabus before enrolment.

Start Your Next Step

Stop Watching Analytics Content. Start Building Analyst Proof.

Get the complete Data Analytics syllabus, understand the right batch for your schedule and speak with a course advisor before you decide.

Call 084318 03839WhatsApp Course Counsellingneonexminds@gmail.com

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