Description
Course Name: Diploma in Data Analytics & Visualization
Course Id: DDAV/Q1001
Eligibility: 10+2(higher Secondary) or equivalent.
Objective: The Diploma in Data Analytics & Visualization aims to develop professionals who can collect, process, analyze, and present data in a meaningful way to support strategic and operational decision-making. The course provides a strong foundation in statistics, data handling, and analytical thinking, enabling learners to work with structured and unstructured data from various sources.
Duration: Six Months.
How to Enroll and Get Certified in Your Chosen Course:
Step 1: Choose the course you wish to get certified in.
Step 2: Click on the “Enroll Now” button.
Step 3: Proceed with the enrollment process.
Step 4: Enter your billing details and continue to course fee payment.
Step 5: You will be redirected to the payment gateway. Pay the course and exam fee using one of the following methods:
Debit/Credit Card, Wallet, Paytm, Net Banking, UPI, or Google Pay.
Step 6: After successful payment, you will receive your study material login ID and password via email within 48 hours of fee payment.
Step 7: Once you complete the course, take the online examination.
Step 8: Upon passing the examination, you will receive:
• A soft copy (scanned) of your certificate via email within 7 days of examination.
• A hard copy (original with official seal and signature) sent to your address within 45 day of declaration of result.
Step 9: After certification, you will be offered job opportunities aligned with your area of interest.
Online Examination Detail:
Duration- 120 minutes.
No. of Questions- 60. (Multiple Choice Questions).
10 Questions from each module, each carry 10 marks.
Maximum Marks- 600, Passing Marks- 40%.
There is no negative marking in this module.
| How Students will be Graded: | ||
| S.No. | Marks | Grade |
| 1 | 91-100 | O (Outstanding) |
| 2 | 81-90 | A+ (Excellent) |
| 3 | 71-80 | A (Very Good) |
| 4 | 61-70 | B (Good) |
| 5 | 51-60 | C (Average) |
| 6 | 41-50 | P (Pass) |
| 7 | 0-40 | F (Fail) |
Key Benefits of Certification- Earning a professional certification not only validates your skills but also enhances your employability. Here are the major benefits you gain:
Practical, Job-Ready Skills – Our certifications are designed to equip you with real-world, hands-on skills that match current industry demands — helping you become employment-ready from day one.
Lifetime Validity – Your certification is valid for a lifetime — no renewals or expirations. It serves as a permanent proof of your skills and training.
Lifetime Certificate Verification – Employers and institutions can verify your certification anytime through a secure and reliable verification system — adding credibility to your qualifications.
Industry-Aligned Certification –All certifications are developed in consultation with industry experts to ensure that what you learn is current, relevant, and aligned with market needs.
Preferred by Employers – Candidates from ISO-certified institutes are often prioritized by recruiters due to their exposure to standardized, high-quality training.
Free Job Assistance Based on Your Career Interests – Receive personalized job assistance and career guidance in your preferred domain, helping you land the right role faster.
Syllabus
Module 1: Foundations of Data Analytics and Statistical Modeling: Introduction to the Data Analytics Lifecycle, Descriptive Statistics and Data Distributions, Inferential Statistics and Hypothesis Testing, Probability Theory and Business Application, Correlation and Regression Analysis, Sampling Techniques and Data Bias Mitigation, Exploratory Data Analysis (EDA) Frameworks, Understanding Structured vs Unstructured Data, Introduction to Predictive Analytics, Ethics and Data Privacy in Analytics.
Module 2: Data Extraction, Cleaning, and Structured Query Language (SQL): Relational Database Management Systems (RDBMS) Fundamentals, Writing Advanced SQL Queries and Joins, Data Filtering, Sorting, and Aggregation, Using Subqueries, CTEs, and Window Functions, Data Cleaning and Transformation via SQL, Handling Missing Values and Outliers, Database Normalization and Schema Design, Extract, Transform, Load (ETL) Pipeline Architecture, Connecting Databases to Analytical Tools, Optimizing Query Performance for Large Datasets.
Module 3: Programming for Analytics with Python and R: Introduction to Python and R for Data Science, Data Manipulation Using NumPy and Pandas, String and Date-Time Data Handling, Conditional Logic, Loops, and Custom Functions, Web Scraping and Automated Data Collection, Importing and Exporting Multi-Format Datasets, Introduction to R’s Tidyverse Ecosystem, Data Wrangling and Missing Value Imputation, Version Control Fundamentals Using Git and GitHub, Notebook Environments for Reproducible Analysis.
Module 4: Data Visualization Theory, Design, and Storytelling: Gestalt Principles of Visual Perception, Choosing the Right Chart Type for Data, Color Theory and Psychological Impact in Design, Avoiding Chart Junk and Maximizing Data-Ink Ratio, Structuring a Compelling Data Narrative, Designing Visualizations for Diverse Audiences, Dashboard Layout Optimization and Grid Systems, Interactive Elements and User Experience (UX) Design, Accessibility Standards (Color Blindness Considerations), Evaluating and Critiquing Data Visualizations.
Module 5: Enterprise Business Intelligence (BI) with Tableau and Power BI: Introduction to Tableau and Power BI Interfaces, Data Connection, Blending, and Relationship Modeling, Creating Calculated Fields and DAX Formulas, Designing Interactive Dashboards and Storyboards, Implementing Filters, Slicers, and Parameters, Advanced Mapping and Geospatial Visualizations, Time Series Analysis and Trend Forecasting, Row-Level Security and Data Governance, Publishing and Sharing Enterprise BI Reports, Automating Scheduled Data Refreshes.
Module 6: Advanced Analytics, Big Data, and Capstone Project: Introduction to Machine Learning Algorithms for PMs, Cluster Analysis and Customer Segmentation, Text Analytics and Sentiment Mining, Big Data Frameworks (Hadoop and Spark Ecosystems), Cloud Analytics Platforms (AWS, Google Cloud, Azure), Key Performance Indicators (KPIs) and Metric Design, A/B Testing and Experimental Design Frameworks, Automated Reporting and Email Alert Pipelines, Deploying Analytical Models to Production, Final Capstone Project and Executive Portfolio Presentation.
Job Opportunities after Diploma in Data Analytics & Visualization
Graduates of this program gain expertise in data collection, data cleaning, statistical analysis, dashboard creation, data visualization tools, and business intelligence reporting, preparing them for analytical roles across IT companies, finance, marketing, e-commerce, and consulting industries.
Key Career Options: Data Analyst, Junior Data Analyst, Business Analyst, Data Visualization Specialist, MIS Executive, Reporting Analyst, Business Intelligence (BI) Analyst, Excel/Advanced Excel Analyst, SQL Data Analyst, Tableau/Power BI Developer, Marketing Data Analyst, Operations Analyst, Research Analyst, Freelance Data Analyst.
Salary Range (India):
Entry-level: ₹3–5 LPA
Mid-level: ₹5–10 LPA
Senior-level: ₹10–20+ LPA
Industries Hiring Graduates: IT & Software Companies, E-commerce Platforms, Banking & Finance Sector, Consulting Firms, Healthcare Analytics, Marketing & Advertising Agencies, Logistics & Supply Chain Companies, Startups, Government & Policy Research Organizations.
Skills Developed: Data analysis using Excel, SQL database querying, data visualization (Power BI/Tableau), statistical analysis, data cleaning & preprocessing, dashboard development, business reporting, problem-solving, data interpretation, and basic programming concepts (Python for data analysis).
Graduates can grow into senior data analyst roles, business intelligence developers, data scientists (with advanced skills), or analytics managers, and can also pursue freelance analytics consulting or specialized roles in finance, marketing, or product analytics.
