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 a specialized program designed to equip students with the skills and knowledge to analyze, interpret, and manage large datasets. It combines the fundamentals of data science with the practical tools and techniques needed to work with big data, enabling professionals to make data-driven decisions.

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.

Below is a detailed breakdown of career options and salary ranges for graduates in India:

1. Data Scientist

Role: Data Scientists design and implement advanced analytics models, apply machine learning algorithms, and use statistical methods to interpret complex data. They work to uncover patterns and trends to inform business decisions.
Salary Range: ₹6 – ₹12 LPA (entry-level); ₹12 – ₹20 LPA (mid-level); ₹20 LPA and above (senior roles).

2. Data Analyst

Role: Data Analysts are responsible for collecting, processing, and analyzing large datasets to derive actionable insights. They use statistical tools and visualization techniques to present data in a comprehensible way for decision-makers.
Salary Range: ₹3.5 – ₹6 LPA (entry-level); ₹6 – ₹12 LPA (mid-level); ₹12 LPA and above (senior roles).

3. Big Data Engineer

Role: Big Data Engineers are responsible for designing and managing large-scale data architectures, ensuring the storage, processing, and retrieval of vast amounts of data efficiently. They work with big data technologies like Hadoop, Spark, and NoSQL databases.
Salary Range: ₹6 – ₹12 LPA (entry-level); ₹12 – ₹20 LPA (mid-level); ₹20 LPA and above (senior roles).

4. Machine Learning Engineer

Role: Machine Learning Engineers build and deploy machine learning models and algorithms that enable systems to learn from data and improve over time. They often work closely with data scientists to bring models into production.
Salary Range: ₹7 – ₹12 LPA (entry-level); ₹12 – ₹20 LPA (mid-level); ₹20 LPA and above (senior roles).

5. Business Intelligence (BI) Analyst

Role: BI Analysts use data visualization tools and reporting software to create insights that help organizations make informed business decisions. They also evaluate business processes, market trends, and competitor data.
Salary Range: ₹4 – ₹8 LPA (entry-level); ₹8 – ₹12 LPA (mid-level); ₹12 LPA and above (senior roles).

6. Data Engineer

Role: Data Engineers design and build systems for collecting, storing, and processing data efficiently. They create pipelines for data flow, ensuring that data is clean, accessible, and usable for analysis.
Salary Range: ₹6 – ₹12 LPA (entry-level); ₹12 – ₹20 LPA (mid-level); ₹20 LPA and above (senior roles).

7. Data Architect

Role: Data Architects design and implement data systems, databases, and warehouses. They work on the structure of data storage and processing systems to ensure that data is accessible, accurate, and secure.
Salary Range: ₹8 – ₹15 LPA (entry-level); ₹15 – ₹25 LPA (mid-level); ₹25 LPA and above (senior roles).

8. AI Specialist

Role: AI Specialists apply artificial intelligence techniques, including natural language processing (NLP), machine learning, and deep learning, to solve complex problems across various industries.
Salary Range: ₹7 – ₹12 LPA (entry-level); ₹12 – ₹20 LPA (mid-level); ₹20 LPA and above (senior roles).

9. Data Consultant

Role: Data Consultants help organizations optimize their data strategies, improve data infrastructure, and design analytical models. They provide expertise in data management, analytics, and the implementation of data-driven solutions.
Salary Range: ₹5 – ₹10 LPA (entry-level); ₹10 – ₹18 LPA (mid-level); ₹18 LPA and above (senior roles).

10. Quantitative Analyst (Quant)

Role: Quantitative Analysts (also known as quants) use mathematical models and large datasets to analyze financial markets, pricing models, and investment strategies. They are commonly employed in finance and banking.
Salary Range: ₹8 – ₹15 LPA (entry-level); ₹15 – ₹30 LPA (mid-level); ₹30 LPA and above (senior roles).

11. Data Visualization Specialist

Role: Data Visualization Specialists focus on creating charts, graphs, and other visual representations of data to help stakeholders understand complex datasets. They often use tools like Tableau, Power BI, and D3.js.
Salary Range: ₹4 – ₹8 LPA (entry-level); ₹8 – ₹12 LPA (mid-level); ₹12 LPA and above (senior roles).

12. Data Operations Manager

Role: Data Operations Managers oversee the data management process, ensuring the integrity, accuracy, and security of data throughout its lifecycle. They often coordinate with various teams, including IT, data analysts, and business leaders.
Salary Range: ₹7 – ₹12 LPA (entry-level); ₹12 – ₹18 LPA (mid-level); ₹18 LPA and above (senior roles).

13. Cloud Data Engineer

Role: Cloud Data Engineers manage and optimize data storage and processing solutions in cloud environments such as AWS, Google Cloud, and Microsoft Azure. They ensure scalability, efficiency, and security of cloud-based data systems.
Salary Range: ₹7 – ₹12 LPA (entry-level); ₹12 – ₹18 LPA (mid-level); ₹18 LPA and above (senior roles).

14. Data Privacy Officer

Role: Data Privacy Officers ensure that an organization’s data handling and processing practices comply with data protection laws and regulations. They work to protect personal and sensitive data and mitigate data breaches.
Salary Range: ₹6 – ₹12 LPA (entry-level); ₹12 – ₹18 LPA (mid-level); ₹18 LPA and above (senior roles).

15. Big Data Consultant

Role: Big Data Consultants advise organizations on how to handle, store, and analyze large datasets using big data technologies. They design and implement solutions that help businesses derive value from big data.
Salary Range: ₹8 – ₹15 LPA (entry-level); ₹15 – ₹25 LPA (mid-level); ₹25 LPA and above (senior roles).

Key Factors Influencing Salaries:

Experience: More experienced professionals, especially those with expertise in areas like machine learning, AI, and big data technologies, can expect higher salaries.
Industry: Salaries can vary depending on the industry. For example, roles in finance, consulting, and technology companies often offer higher compensation than those in other sectors.
Location: Tech hubs like Bangalore, Hyderabad, Mumbai, Delhi, and Pune typically offer higher salaries due to the concentration of data science and analytics jobs.
Certifications and Skills: Certifications in tools like Hadoop, Spark, Tableau, Power BI, AWS, and advanced machine learning can help increase salary potential.

Emerging Trends:

Artificial Intelligence and Deep Learning: With the increasing application of AI and deep learning across industries, professionals with expertise in these areas are in high demand and earn premium salaries.
Cloud Analytics: The adoption of cloud platforms for data storage and analytics is growing, making cloud data engineers and consultants highly sought after.
Automation and AI Integration in Data Science: The use of automation in data analytics, such as AutoML (Automated Machine Learning), is reshaping the industry, creating new opportunities for data scientists with the right skills.
Data Privacy and Governance: As data privacy laws become more stringent globally (e.g., GDPR), there is a growing need for professionals who specialize in data security, privacy, and compliance.

Graduates with a Diploma in Data Science and Big Data Analytics have abundant career opportunities across various industries and sectors. As they advance in their careers, they can take on specialized roles that offer higher salaries and greater job satisfaction. Given the current demand for data-driven decision-making, the outlook for data science professionals remains highly positive in India.

Additional information

Data entry Operator Course