Advanced Diploma in Data Science

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.