Description
Course Name: Advanced Diploma in Data Science
Course Id: ADDS/Q1001.
Eligibility: 10+2 (Higher Secondary) or Equivalent.
Objective: To equip learners with advanced tools, techniques, and methodologies for working with big data and complex datasets. To prepare students for careers in data analytics, data engineering, machine learning, and artificial intelligence.
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 Analytics: Overview of Data Science, Importance and Applications of Data Science, Data Science Lifecycle, Data Science vs. Business Intelligence vs. Big Data, Role of a Data Scientist, Structured vs. Unstructured Data, Basics of Data Cleaning and Preprocessing, Introduction to Data Visualization, Fundamentals of Exploratory Data Analysis (EDA), Ethical Considerations in Data Science.
Programming for Data Science (Python & R): Introduction to Python for Data Science, Introduction to R for Data Science, Data Types and Structures, File Handling and Data Importing, Data Manipulation using Pandas and dplyr, Loops, Functions, and Conditional Statements, NumPy and Matplotlib for Scientific Computing, Introduction to Jupyter Notebook and RStudio, Debugging and Error Handling, Best Practices in Coding for Data Science.
Data Wrangling and Preprocessing: Handling Missing Data, Data Cleaning Techniques, Feature Engineering and Feature Selection, Data Transformation (Scaling and Normalization), Handling Categorical Data (One-Hot Encoding, Label Encoding), Outlier Detection and Treatment, Data Imbalance Handling (SMOTE, Undersampling), Data Integration and Merging, Working with Large Datasets, Introduction to SQL for Data Science.
Exploratory Data Analysis (EDA) and Data Visualization: Introduction to EDA, Summary Statistics and Data Distribution, Data Visualization using Matplotlib & Seaborn, Interactive Visualization with Plotly, Correlation and Relationship Analysis, Dimensionality Reduction Techniques (PCA, t-SNE), Heatmaps and Clustered Data Visualization, Time Series Data Visualization, Customizing Graphs for Business Insights, Storytelling with Data.
Statistical Analysis and Probability: Basics of Descriptive and Inferential Statistics, Measures of Central Tendency and Variability, Probability Distributions (Normal, Binomial, Poisson), Hypothesis Testing (T-test, Chi-square test, ANOVA), Confidence Intervals and Significance Levels, Bayesian Statistics and Its Applications, Correlation vs. Causation, Statistical Power and Sample Size, A/B Testing and Experimentation, Monte Carlo Simulations.
Machine Learning Fundamentals: Supervised vs. Unsupervised Learning, Linear Regression and Logistic Regression, Decision Trees and Random Forest, Support Vector Machines (SVM), Naïve Bayes Classifier, K-Nearest Neighbors (KNN), Clustering Techniques (K-Means, Hierarchical), Evaluation Metrics for Model Performance (Accuracy, Precision, Recall, F1-Score), Cross-Validation Techniques, Bias-Variance Tradeoff.
Job Opportunities after completion of Advanced Diploma in Data Science course:
Graduates of the Advanced Diploma in Data Science program have a wide array of career opportunities across multiple industries, such as technology, finance, healthcare, retail, and marketing. The data science field is growing rapidly in India due to the increasing importance of data-driven decision-making and advancements in technology like machine learning, artificial intelligence, and big data.
Career Options and Salary Range
1. Data Scientist
- Role: Data scientists analyze and interpret complex data to help organizations make informed decisions. They use programming, machine learning, statistical models, and data visualization tools to extract insights from large datasets.
- Salary:
- Entry-level: ₹6–₹10 LPA
- Mid-level: ₹10–₹20 LPA
- Senior-level: ₹20–₹40 LPA (can go higher in specialized roles or with extensive experience)
2. Data Analyst
- Role: Data analysts focus on collecting, processing, and analyzing data to help organizations understand patterns, trends, and insights. They use statistical tools and software to prepare reports and visualizations.
- Salary:
- Entry-level: ₹4–₹6 LPA
- Mid-level: ₹6–₹12 LPA
- Senior-level: ₹12–₹20 LPA
3. Data Engineer
- Role: Data engineers design and maintain the infrastructure for data generation, collection, and storage. They work with databases, data warehouses, and big data technologies like Hadoop and Spark to ensure the proper flow and management of data.
- Salary:
- Entry-level: ₹6–₹10 LPA
- Mid-level: ₹10–₹18 LPA
- Senior-level: ₹18–₹30 LPA
4. Machine Learning Engineer
- Role: Machine learning engineers build and deploy machine learning models and algorithms that help organizations make predictions, automate tasks, and improve processes. They often work with large datasets and artificial intelligence technologies.
- Salary:
- Entry-level: ₹7–₹12 LPA
- Mid-level: ₹12–₹20 LPA
- Senior-level: ₹20–₹35 LPA
5. Artificial Intelligence (AI) Engineer
- Role: AI engineers create intelligent systems and algorithms that can mimic human behavior and decision-making processes. They apply principles of AI and deep learning to solve complex problems in various sectors.
- Salary:
- Entry-level: ₹8–₹15 LPA
- Mid-level: ₹15–₹30 LPA
- Senior-level: ₹30–₹50 LPA
6. Business Intelligence (BI) Analyst
- Role: BI analysts use data to help organizations make strategic business decisions. They analyze business trends and performance data to create reports and dashboards that inform management decisions.
- Salary:
- Entry-level: ₹4–₹6 LPA
- Mid-level: ₹6–₹12 LPA
- Senior-level: ₹12–₹18 LPA
7. Quantitative Analyst (Quant)
- Role: Quants use advanced mathematical models and statistical techniques to analyze financial markets and make investment decisions. They often work in finance and banking, helping organizations optimize their portfolios.
- Salary:
- Entry-level: ₹8–₹12 LPA
- Mid-level: ₹12–₹25 LPA
- Senior-level: ₹25–₹50 LPA
8. Big Data Engineer
- Role: Big data engineers work with large-scale data processing systems, designing and implementing solutions that allow organizations to handle massive amounts of data. They work with tools like Hadoop, Spark, and NoSQL databases.
- Salary:
- Entry-level: ₹7–₹12 LPA
- Mid-level: ₹12–₹20 LPA
- Senior-level: ₹20–₹35 LPA
9. Data Visualization Specialist
- Role: Data visualization specialists focus on transforming data into graphical representations such as charts, graphs, and dashboards to help organizations understand the story behind the data. They use tools like Tableau, Power BI, and Python libraries (e.g., Matplotlib).
- Salary:
- Entry-level: ₹4–₹7 LPA
- Mid-level: ₹7–₹12 LPA
- Senior-level: ₹12–₹20 LPA
10. Data Consultant
- Role: Data consultants provide expert advice to organizations on how to best utilize their data for business strategy and performance improvements. They may also recommend tools, technologies, and best practices for data management and analysis.
- Salary:
- Entry-level: ₹6–₹10 LPA
- Mid-level: ₹10–₹20 LPA
- Senior-level: ₹20–₹40 LPA
11. Data Governance Manager
- Role: Data governance managers ensure that the organization’s data is accurate, consistent, secure, and compliant with relevant regulations. They develop policies and procedures for managing data and overseeing its quality.
- Salary:
- Entry-level: ₹8–₹12 LPA
- Mid-level: ₹12–₹20 LPA
- Senior-level: ₹20–₹40 LPA
12. Data Architect
- Role: Data architects are responsible for designing the structure and organization of databases, data warehouses, and other data storage systems. They ensure that data is stored in a way that supports effective analysis and retrieval.
- Salary:
- Entry-level: ₹8–₹15 LPA
- Mid-level: ₹15–₹25 LPA
- Senior-level: ₹25–₹45 LPA
Salary Growth Overview
- Entry-level: ₹4–₹10 LPA (for roles like data analyst, BI analyst, junior data engineer, etc.)
- Mid-level: ₹10–₹20 LPA (for roles like data scientist, machine learning engineer, or data engineer)
- Senior-level: ₹20–₹50 LPA (for experienced roles like senior data scientist, AI engineer, data architect, etc.)
Factors Influencing Salary
- Experience: More experienced professionals in data science roles command higher salaries due to their deeper expertise in handling complex data problems and tools.
- Industry: Data science roles in industries like finance, healthcare, and technology often come with higher pay due to the complexity and demand for these skill sets.
- Skillset: Knowledge of advanced tools and technologies (like machine learning, big data technologies, AI) and proficiency in languages such as Python, R, and SQL can significantly increase earning potential.
- Location: Cities like Bangalore, Delhi, Mumbai, and Hyderabad generally offer higher salaries due to the concentration of tech companies and startups.
Industry Growth and Job Outlook
The demand for data science professionals in India is expected to continue growing as companies increasingly rely on data to drive business decisions, improve operations, and develop new products. With the rise of artificial intelligence, machine learning, and big data, the job market for skilled data scientists is expanding rapidly. This growth is also fueled by the adoption of data analytics in various sectors, including healthcare, e-commerce, finance, and government.
According to industry reports, India’s data science and analytics market is projected to grow at a compound annual growth rate (CAGR) of around 30% in the coming years, creating even more opportunities for graduates.
Conclusion
Graduates of the Advanced Diploma in Data Science program are well-positioned for lucrative and diverse career paths across industries. The combination of analytical skills, programming knowledge, and business insight allows them to pursue roles that offer excellent salary growth and job stability. With data science being a key driver of innovation and decision-making, professionals in this field can expect long-term demand and career advancement opportunities.
