Leading Data Analytics Course in Rohini with 100% Placement Support
DSSD offers a leading Data Analytics course in Rohini, designed to help learners build strong, job-ready skills with 100% placement assistance. With over 8 years of expertise in training, DSSD provides a perfect blend of advanced learning and hands-on experience. This program covers essential areas like data visualization, statistical analysis, machine learning, and big data tools, ensuring you gain practical knowledge aligned with industry needs. Guided by experienced mentors, students develop confidence to work on real-world projects. Take the first step toward a successful future in the growing analytics field by joining DSSD and building a career with excellent opportunities.Build a rewarding future with DSSD’s Data Analytics course in Rohini. Learn key skills in data visualization, statistical analysis, machine learning, and big data tools to become job-ready for the booming analytics industry. Start your journey to success today
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AI-Powered Data Analytics Institute in Rohini | 100% Placement Assistance
Top Data Analytics Training Institute in Rohini | Learn Excel, SQL, Python & Power BI
- Industry-Recognized Data Analytics Certification
- Career-Focused Training with Placement Assistance
- Hands-On Training in Excel, SQL, Power BI & Python
- Real-World Data Analytics Projects & Case Studies
- Expert-Led Training with Practical Learning Approach
- Internship Opportunities & Job Placement Support
- Python Programming for Data Analysis
- Internship with Practical Exposure
- Course Completion Certification
Professional Data Analytics Training in Rohini | Live Projects & Internship
AI-Powered Data Analytics Course in Rohini – Learn Excel, SQL, Python, Power BI & Data Visualization
Microsoft Fabric AI
Power BI Copilot
Microsoft Copilot
Tableau AI
Transform Your Career with the Best Data Analytics Course in Rohini
- Introduction to Data Analytics
- Advanced Excel
- SQL (Structured Query Language)
- Python for Data Analysis
- Data Cleaning
- Statistics Basics
- Data Visualization
- Power BI / Tableau
- Business Analytics
- Live Projects
- Resume & Interview Prep
Introduction to Data Analytics
The Introduction to Data Analytics module helps learners understand how organizations use information to solve problems, evaluate performance, and make better decisions. Students discover how everyday business activities generate data and how that information can be converted into useful insights. The module builds a foundation for understanding the purpose of analytics before moving into individual tools and techniques.
Learners explore the Data Analytics Lifecycle, including defining a business question, collecting relevant information, preparing datasets, performing analysis, and communicating results. They are introduced to Descriptive, Diagnostic, Predictive, and Prescriptive Analytics, understanding the different questions each approach addresses. Students also learn about structured and unstructured data, common data sources, and the importance of accuracy, privacy, and responsible data handling.
Through examples from retail, finance, healthcare, marketing, and other industries, students understand how analysts connect their findings with practical business needs. The module also introduces the responsibilities of a data analyst, the skills required for analytical work, and the career paths learners can explore as they develop their expertise.
Topics Covered
Understanding Data Analytics and Its Applications
Stages of the Data Analytics Lifecycle
Descriptive, Diagnostic, Predictive, and Prescriptive Analytics
Structured and Unstructured Data
Common Data Sources and Collection Methods
Data Quality, Privacy, and Responsible Use
Role and Responsibilities of a Data Analyst
Career Opportunities in Data Analytics
Advanced Excel
The Advanced Excel module develops the spreadsheet skills needed to organize, examine, and present business information efficiently. Students learn to work with structured tables, manage larger datasets, and use formulas to answer practical questions. The focus is on building accurate, understandable spreadsheets that support everyday reporting and analytical tasks.
Learners work with logical functions, lookup formulas, text functions, date calculations, and conditional aggregations. They explore how Pivot Tables summarize information, how Pivot Charts reveal patterns, and how conditional formatting highlights important changes. Data validation, error handling, and consistent spreadsheet structure help students improve the reliability of their work. The module also introduces Power Query for importing and preparing data through repeatable steps.
Using examples involving sales, expenses, attendance, inventory, and customer records, students practice turning raw worksheets into useful reports. They learn to select relevant metrics, compare results across periods, and present findings through clear dashboards. These skills provide a practical foundation for MIS reporting, business analysis, and further learning in SQL and business intelligence tools.
Topics Covered
Excel Tables and Structured References
IF, IFS, SUMIFS, and COUNTIFS
VLOOKUP, XLOOKUP, and INDEX-MATCH
Text, Date, and Error-Handling Functions
Sorting, Filtering, and Data Validation
Pivot Tables and Pivot Charts
Power Query for Data Preparation
Dashboard Creation and Business Reporting
SQL (Structured Query Language)
The SQL module introduces learners to the databases that store much of an organization’s operational information. Students understand how records are arranged into tables, how relationships connect different datasets, and how SQL helps retrieve the information required for analysis. The module develops a practical approach to answering business questions using structured data.
Learners begin with SELECT statements, filtering, sorting, and calculated expressions, then progress to aggregate functions and grouped summaries. They practice joining tables, writing subqueries, and using Common Table Expressions to organize more complex queries. Primary keys, foreign keys, null values, and table relationships are explained so students can understand why a query may return missing or duplicated results.
Through exercises involving customers, orders, products, and transactions, students learn to investigate trends and prepare reporting datasets. Attention is given to checking query outputs, choosing appropriate joins, and maintaining the correct level of detail. By the end of the module, learners can extract and combine relevant information while explaining how their queries answer a specific analytical question.
Topics Covered
Relational Database Fundamentals
Tables, Data Types, Primary Keys, and Foreign Keys
SELECT, WHERE, ORDER BY, and DISTINCT
Aggregate Functions, GROUP BY, and HAVING
INNER JOIN and OUTER JOIN
Subqueries and Common Table Expressions
CASE Expressions and Handling NULL Values
Query Validation and Reporting Datasets
Python for Data Analysis
The Python for Data Analysis module introduces programming as a practical way to process information, explore datasets, and reduce repetitive manual work. Students begin with essential Python concepts and gradually apply them to analytical tasks. The emphasis is on writing clear, understandable code that produces results learners can check and explain.
Students explore variables, data types, conditions, loops, functions, and basic data structures before working with analytical libraries. Using Pandas and NumPy, they learn to import files, inspect datasets, select records, calculate new columns, combine tables, and summarize information. They also practice handling missing values, converting data types, and investigating patterns through exploratory data analysis.
Practical exercises demonstrate how Python can support sales analysis, customer reporting, and recurring data preparation. Learners use notebooks to combine code, observations, and visual outputs in one place. The module encourages careful documentation, basic debugging, and validation of results, helping students build reusable analysis workflows and explain their approach during project discussions and technical interviews.
Topics Covered
Python Syntax and Programming Fundamentals
Lists, Tuples, Dictionaries, and Sets
Conditions, Loops, and Functions
Working with Jupyter Notebooks
NumPy Arrays and Numerical Operations
Pandas DataFrames and File Import
Data Transformation, Grouping, and Merging
Exploratory Data Analysis and Task Automation
Data Cleaning
The Data Cleaning module focuses on preparing information that is accurate, consistent, and suitable for analysis. Students learn why raw datasets often contain incomplete records, duplicate entries, incorrect values, and inconsistent formatting. They understand how these issues can distort reports and why cleaning decisions must be connected to the purpose of the analysis.
Learners practice identifying missing values, repeated records, invalid entries, and conflicting data types. They explore methods for standardizing dates, correcting text fields, separating combined columns, and reconciling inconsistent categories. The module also explains how to investigate unusual values, distinguishing genuine observations from errors before deciding whether any correction is appropriate.
Using practical datasets, students develop a step-by-step cleaning workflow that preserves the original information and records important changes. They compare results before and after preparation, check whether transformations introduce new problems, and document assumptions that could affect interpretation. These habits help learners create trustworthy datasets for spreadsheets, SQL queries, Python analysis, and business intelligence dashboards.
Topics Covered
Identifying Common Data Quality Problems
Handling Missing and Incomplete Records
Finding and Removing Duplicate Entries
Standardizing Text, Dates, and Categories
Correcting Data Types and Invalid Values
Investigating Outliers and Inconsistencies
Validating Cleaned Datasets
Documenting Cleaning Steps and Assumptions
Statistics Basics
The Statistics Basics module helps students understand what numbers reveal about a dataset and where their limitations lie. Learners develop the ability to summarize information, compare groups, and interpret variation. Rather than treating statistics as a collection of formulas, the module connects each concept with the questions analysts encounter in business reporting.
Students explore mean, median, mode, range, variance, standard deviation, and percentiles. They learn how distributions describe the shape of data and why a single average may hide important differences. Probability, sampling, and correlation are introduced through accessible examples, alongside the distinction between an observed relationship and evidence of cause and effect.
Practical exercises help learners interpret sales figures, customer behavior, delivery times, and other business measurements. Students consider how sample size, unusual values, and selection methods can influence conclusions. By developing these foundations, learners become better equipped to explain findings, recognize misleading summaries, and approach more advanced analytical methods with appropriate care and confidence.
Topics Covered
Descriptive Statistics and Data Summaries
Mean, Median, and Mode
Range, Variance, and Standard Deviation
Percentiles and Frequency Distributions
Probability Fundamentals
Populations, Samples, and Sampling Bias
Correlation and Its Limitations
Interpreting Statistical Results in Business
Data Visualization
The Data Visualization module teaches students to communicate analytical findings through clear and purposeful visual representations. Learners understand how charts can make comparisons easier, reveal changes over time, and highlight relationships that may be difficult to identify in raw tables. The focus is on helping an audience understand the evidence and its business relevance.
Students explore how to choose suitable bar charts, line charts, scatter plots, histograms, and other visual formats. They learn to use titles, labels, scales, and color consistently while avoiding clutter and misleading presentation. The module also introduces accessibility considerations, including readable text, sufficient contrast, and alternatives to relying only on color.
Through practical reporting exercises, learners organize individual visuals into a coherent explanation. They practice identifying the main message, providing relevant context, and distinguishing observations from interpretations. Students also learn when a simple table communicates exact values more effectively than a chart. These skills support clearer presentations, more useful dashboards, and stronger communication with managers, clients, and other stakeholders.
Topics Covered
Principles of Effective Data Visualization
Selecting Charts for Different Questions
Visualizing Trends, Comparisons, and Relationships
Chart Titles, Labels, Scales, and Annotations
Color, Readability, and Accessibility
Avoiding Misleading Visualizations
Dashboard Layout and Information Hierarchy
Data Storytelling and Presenting Insights
Power BI / Tableau
The Power BI / Tableau module introduces business intelligence tools used to explore data and build interactive reports. Students learn how dashboards bring related information together, allowing users to examine performance, compare results, and investigate changes. The module connects dashboard development with the needs of the people who will use the finished report.
Learners explore data connections, preparation, relationships, calculations, and interactive visualizations. In Power BI, this includes an introduction to Power Query and DAX measures. In Tableau, students explore calculated fields, parameters, and dashboard actions. Across both tools, the emphasis is on selecting appropriate visuals and ensuring that filters and calculations behave as intended.
Practical dashboard exercises may involve sales, finance, marketing, or operational datasets. Students learn to check totals against source information, organize report pages, and provide clear labels and context. Publishing and sharing concepts are also introduced, including the role of permissions and available platform features. These skills help learners present analytical work through useful, understandable business reports.
Topics Covered
Introduction to Power BI and Tableau
Connecting and Preparing Data Sources
Data Relationships and Modeling Concepts
Power Query and DAX Fundamentals
Tableau Calculated Fields and Parameters
Filters, Slicers, and Dashboard Interactions
KPI Reporting and Dashboard Validation
Publishing, Sharing, and Access Concepts
Business Analytics
The Business Analytics module helps learners connect technical analysis with practical organizational goals. Students understand how to move from a broad concern, such as declining sales or rising costs, to a focused question that can be investigated using data. The module emphasizes the business context behind an analysis and the decisions it should support.
Learners explore performance indicators, revenue trends, customer behavior, profitability, and operational efficiency. They practice defining metrics clearly, choosing suitable comparison periods, and breaking results down by relevant categories. Business examples show why the same number can have different meanings depending on targets, seasonality, customer segments, and operating conditions.
Through case studies, students learn to assess possible explanations, identify evidence gaps, and develop recommendations that reflect the limits of the available information. They also practice presenting findings to audiences who may not have a technical background. The module builds analytical judgment by encouraging learners to explain what changed, why it may matter, and what further investigation or action would be useful.
Topics Covered
Defining Business Problems and Analytical Questions
Selecting KPIs and Performance Measures
Sales, Revenue, and Profitability Analysis
Customer Segmentation and Behavior Analysis
Operational Performance and Efficiency
Trend Analysis and Business Comparisons
Evaluating Findings and Recommending Actions
Communicating Insights to Stakeholders
Live Projects
The Live Projects module gives students an opportunity to combine their learning in complete analytical assignments. Working with real-world or simulated business datasets, learners move beyond isolated exercises and experience how different stages of an analytics project connect. The emphasis is on producing work that answers a clear question and can be explained to others.
Students begin by understanding the project brief, identifying relevant data, and setting achievable objectives. They then carry out data preparation, exploration, analysis, visualization, and reporting, choosing appropriate tools from Excel, SQL, Python, Power BI, or Tableau. Throughout the process, they check results, document assumptions, and consider whether the evidence supports their conclusions.
The module also develops project communication and presentation skills. Students organize their work into case studies that explain the original problem, their approach, the main findings, and any limitations. Feedback and revision help them improve both technical accuracy and clarity. Completed assignments provide material for a professional portfolio and useful examples to discuss during internship applications and interviews.
Topics Covered
Understanding Project Briefs and Business Objectives
Selecting Relevant Datasets and Tools
Data Cleaning and Exploratory Analysis
Applying Excel, SQL, Python, and BI Skills
Dashboard and Analytical Report Development
Validating Results and Documenting Assumptions
Presenting Findings and Recommendations
Project Case Studies and Portfolio Development
Resume & Interview Prep
The Resume & Interview Prep module helps students communicate their analytical skills, learning experience, and project work professionally. Learners understand how to present relevant evidence of their abilities, particularly when applying for entry-level opportunities. The focus is on making their experience clear, accurate, and appropriate for the roles they want to pursue.
Students learn to structure a readable resume with sections for technical skills, education, projects, certifications, and work experience. They practice writing specific project descriptions that explain the problem addressed, the tools used, and the outcome achieved. The module also covers tailoring applications to job requirements, organizing portfolio links, and presenting a consistent professional profile on LinkedIn.
Interview preparation includes technical questions, business scenarios, and discussions about completed projects. Learners practice explaining SQL queries, spreadsheet methods, dashboards, and analytical decisions in simple language. Mock interviews help students develop structured responses, recognize gaps in their preparation, and improve confidence. Guidance on professional communication and thoughtful questions also prepares them to participate more effectively in recruitment conversations.
Topics Covered
Resume Structure and ATS-Friendly Formatting
Writing Clear Skills and Project Descriptions
Tailoring Applications to Job Requirements
LinkedIn Profile and Portfolio Presentation
Excel, SQL, Python, and BI Interview Questions
Business Cases and Analytical Problem-Solving
Mock Interviews and Project Walkthroughs
Professional Communication and Interview Etiquette
Select the Right Certification Course
Choose the right AI-powered digital marketing certification based on your career goals, learning depth, and budget.
ANALYTICS EDGE WITH AI
Build a competitive edge with AI-powered data analytics, hands-on projects, real-world datasets, industry tools, and practical training designed to prepare you for in-demand analytics roles.
AI-Powered Analytics and Insights Mastery
Master data analytics with AI-driven insights, data visualization, statistical analysis, and hands-on training using industry tools to turn complex data into actionable business decisions.
Customized Data Analytics Program
Learn SQL, Advanced Excel, Python, Power BI, and Tableau through practical assignments, live projects, and industry-focused training tailored to your career goals.
COURSES
Select the Right Certification Course
Clear plans help students decide faster.
Master Program
ANALYTICS EDGE WITH AI
- Introduction to Data Analytics
- Advanced Excel for Data Analysis
- SQL Fundamentals & Database Concepts
- Data Visualization Basics
- Business Reporting
- Live projects + assignments
- AI tools + automation
- Internship certificate
- Placement assistance
Advanced Program
AI-Powered Analytics and Insights Mastery
- Advanced Excel
- SQL Queries, Joins & Database Management
- Power BI Dashboard Development
- Live projects + assignments
- AI-Powered Reporting & Automation
- AI Tools (ChatGPT, Copilot & Gemini)
- Internship certificate
- Placement assistance
Customized Program
Customized Data Analytics Program
- Personalized Learning Path
- Excel, SQL, Power BI & Python Training
- Excel, SQL, Power BI & Python Training
AI Tools & Automation - Live projects + assignments
- AI tools + automation
- Internship certificate
- Placement assistance
SYLLABUS
Explore the Complete Data Analytics Training Program
A detailed curriculum gives confidence before enquiry.
MONTH 1
Learn the fundamentals of data analytics, Excel, data collection, and data cleaning. Understand basic statistics, data visualization concepts, and business analytics.
Month 2
Learn SQL to query, filter, and manage databases efficiently. Master data manipulation techniques, joins, aggregate functions, and database concepts.
MONTH 3
Master Power BI to create interactive dashboards and reports. Learn data modeling, Power Query, DAX basics, and data visualization techniques.
MONTH 4
Python for Data Analytics and automate data analysis tasks. Master NumPy, Pandas, data cleaning, data transformation, and exploratory data analysis (EDA).
MONTH 5
MONTH 6
Complete industry-level capstone projects using Excel, SQL, Power BI, Python, and Machine Learning.
TOOLS
30+ Marketing Tools You Will Practice
THE SMARTER CHOICE
WHY LEARNERS CHOOSE DSSD
Industry-focused training. Practical learning. Real career outcomes.
| Feature | Other Institutes | DSSD |
|---|---|---|
| AI Training | ×No | ✔Yes |
| Live Projects | ×Limited | ✔Real-Time Projects |
| Placements | ×Average | ✔100% Assistance |
| Certifications | ×Basic | ✔Industry Recognized |
| Support | ×Limited | ✔Dedicated Mentorship |
| Classes | ×Fixed | ✔Flexible Batches |
| Practical Learning | ×Theory Based | ✔Hands-On Training |
| Career Growth | ×Slow | ✔Job-Ready Skills |
HOW IT WORKS
Your Learning Journey at DSSD
Make the process simple and predictable
PLACEMENT
Career Support That Helps You Get Started
Add real placement images, offer letters and student success stories.
Resume Building
Interview Prep
Mock Interviews
Job Updates
Internship Help
Portfolio Review
Placement Highlight
Build a portfolio before applying for jobs
Students should complete campaigns, reports and website work during training.
Upcoming Batches for Data Analytics Course
Join DSSD’s AI-Powered Data Analytics Course in Rohini and build practical skills with industry-relevant tools, live projects and career-focused training.
| Batch | Training | Next Batch | Duration | FEES | ACTION |
|---|---|---|---|---|---|
|
Weekday Batch |
130 Hrs Training + 10 Hrs Backup Classes |
07th Oct 2026
Tuesday to Friday
03:00pm – 04:30pm |
20 Weeks |
Program Fee: INR 35,000/- EMI available |
|
|
Weekday Batch |
130 Hrs Training + 10 Hrs Backup Classes |
13th Sep 2026
Saturday & Sunday
1:30pm – 03:00pm |
20 Weeks |
Program Fee: INR 35,000/- EMI available |
|
|
Weekday Batch |
130 Hrs Training + 10 Hrs Backup Classes |
8th Sep 2026
Tuesday to Friday
10:00am – 11:30am |
20 Weeks |
Program Fee: INR 35,000/- EMI available |
Limited Seats Available. Enquire now and get complete details about the AI-Powered Data Analytics Course in Rohini
Students’ Happy Faces
A glimpse of learning, achievements and celebrations at DSSD.
Trusted by Students, Proven by Results
TRAINERS
Meet Our Industry Expert Trainers
DSSD Institute — Training Location
1st Floor, H-17/253, opposite Metro Pillar Number 423,
near Rohini West Metro Station, New Delhi 110085
Have Questions? We Have Answers
This 6 months Data Analytics training program includes fundamentals of fundamental of coding, basics of Data Analytics, advanced techniques, difficult problems, principles, and best practices in Data Analytics.
To know more about the fee structure for this 6 months practical Data Analytics training program, kindly call our admission counsellor on this mobile number 9811128610
Our course focuses on practical learning with expert trainers, live projects, the latest analytics tools, AI integration, flexible class timings, and dedicated placement support. Whether you’re starting from scratch or upgrading your skills, our curriculum is designed to make you job-ready with real-world experience.
Enrollment is simple and quick. Students can visit the DSSD Rohini center, contact the team via phone, or register online. After discussing batch timing and course details, the registration can be completed with a small admission fee. The DSSD team guides you through the entire process and ensures you join the right batch according to your skill level and availability.
DSSD focuses on skill development beyond the classroom. Students get continuous guidance, updated study material, free tool demos, and regular workshops on industry trends. The institute teaches long-term strategies in SEO, advertising, branding, content planning, and analytics, ensuring you stay competitive in the digital world. Lifetime support for queries and career guidance helps students upgrade even after completing the course.