Description
Course Name: Diploma in Data Science and Big Data Analytics
Course Id: DDSBDA/Q1001.
Eligibility: Completion of 10+2 (higher Secondary) or equivalent.
Objective: The Diploma in Data Science and Big Data Analytics is designed to provide learners with a solid foundation in data analysis, statistical methods, and big data technologies. The course covers data mining, machine learning, data visualization, and tools for handling large datasets.
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:
Introduction to Data Science and Big Data: Definition and Scope of Data Science, Evolution of Big Data and Its Importance, Applications of Data Science in Various Industries, Big Data vs Traditional Data Processing, Data Science Workflow and Lifecycle, Key Components of Big Data (Volume, Velocity, Variety, Veracity, Value), Data Structures and Types, Overview of Data Engineering, Ethical Considerations in Data Science, Future Trends in Data Science and Big Data.
Programming for Data Science (Python & R): Introduction to Python and R for Data Science, Variables, Data Types, and Operators, Control Structures (Loops, Conditionals, Functions), Data Structures (Lists, Tuples, Dictionaries, Data Frames), File Handling and Data Input/Output, Data Visualization with Matplotlib, Seaborn, and ggplot, Introduction to Jupyter Notebooks and RStudio, Pandas and NumPy for Data Manipulation, Error Handling and Debugging, Writing Efficient Code and Optimization Techniques.
Data Wrangling and Preprocessing: Data Collection Techniques and Sources, Handling Missing and Duplicate Data, Data Cleaning and Transformation Techniques, Feature Engineering and Scaling, Handling Categorical and Numerical Data, Data Normalization and Standardization, Data Integration and Aggregation, Working with Structured and Unstructured Data, Outlier Detection and Treatment, Data Encoding and Dimensionality Reduction.
Exploratory Data Analysis (EDA): Understanding the Importance of EDA, Summary Statistics and Data Distribution, Univariate and Bivariate Analysis, Data Visualization with Python and R, Identifying Patterns and Trends in Data, Correlation and Covariance, Histogram, Box Plot, Scatter Plot, and Heatmaps, Detecting and Handling Anomalies, Hypothesis Testing and Statistical Inference, Using EDA to Inform Model Selection.
Machine Learning Fundamentals: Introduction to Machine Learning and Its Types, Supervised vs Unsupervised Learning, Regression Techniques (Linear, Multiple, Polynomial), Classification Algorithms (Logistic Regression, Decision Trees, Random Forest, SVM), Clustering Methods (K-Means, Hierarchical, DBSCAN), Bias-Variance Tradeoff and Model Selection, Model Evaluation Metrics (Accuracy, Precision, Recall, F1 Score, ROC-AUC), Cross-Validation and Hyperparameter Tuning, Overfitting and Underfitting, Ensemble Learning and Bagging/Boosting Techniques.
Deep Learning and Neural Networks: Introduction to Deep Learning and Neural Networks, Structure of Artificial Neural Networks (ANN), Activation Functions and Backpropagation, Convolutional Neural Networks (CNN) for Image Processing, Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM), Natural Language Processing (NLP) Basics, Implementing Neural Networks with TensorFlow and Keras, Transfer Learning and Pretrained Models, Reinforcement Learning Fundamentals, Applications of Deep Learning in Various Domains.
A Diploma in Data Science and Big Data Analytics program provides graduates with the skills and knowledge needed to analyze and interpret large datasets, implement machine learning algorithms, and manage data-driven decision-making processes. Graduates of this program can pursue a variety of roles in fields such as data analysis, machine learning, business intelligence, and big data management. Given the high demand for professionals with data science and analytics skills across industries such as finance, healthcare, e-commerce, technology, and more, the career opportunities are vast and well-compensated.
Job Opportunities after Diploma in Data Science and Big Data Analytics
Graduates can build careers in data science, analytics, artificial intelligence, and big data technologies across IT, finance, healthcare, and consulting industries.
Top Roles: Data Scientist, Data Analyst, Big Data Engineer, Machine Learning Engineer, Business Intelligence (BI) Analyst, Data Engineer, Data Architect, AI Specialist, Data Consultant, Quantitative Analyst, Data Visualization Specialist, Data Operations Manager, Cloud Data Engineer, Data Privacy Officer, Big Data Consultant.
Key Skills: Data analysis, machine learning, statistics, Python/R programming, big data tools (Hadoop, Spark), data visualization (Power BI, Tableau), SQL, cloud computing, data modeling.
Salary Range (India):
Entry-level: ₹3.5–8 LPA
Mid-level: ₹8–20 LPA
Senior-level: ₹20–30 LPA+
Scope: Extremely high demand due to rapid growth of AI, big data, and digital transformation across industries, with strong career growth in fields like machine learning, cloud analytics, and data-driven decision-making.
