Description
Certification Name: Certificate in Data Science Professional
Course Id: CDSP/Q0001.
Eligibility: Graduation or Equivalent.
Objective: The Certified Data Science Professional course aims to provide participants with comprehensive knowledge and practical skills in data science methodologies, tools, and applications. The course equips learners to collect, clean, process, analyze, and interpret complex data sets using statistical methods, machine learning algorithms, and data visualization techniques.
Duration: Three Month.
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- 60 minutes.
No. of Questions- 30. (Multiple Choice Questions).
Maximum Marks- 100, Passing Marks- 40%.
There is no negative marking in this module.
| Marking System: | ||||||
| S.No. | No. of Questions | Marks Each Question | Total Marks | |||
| 1 | 10 | 5 | 50 | |||
| 2 | 5 | 4 | 20 | |||
| 3 | 5 | 3 | 15 | |||
| 4 | 5 | 2 | 10 | |||
| 5 | 5 | 1 | 5 | |||
| 30 | 100 | |||||
| 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 | 40-50 | 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.
Assessment Modules:
Module 1: Introduction to Data Science: Data Science Overview, Role of a Data Scientist, Data Science Lifecycle, Tools and Technologies in Data Science, Applications of Data Science, Ethics and Privacy in Data Science.
Module 2: Python for Data Science: Python Basics and Data Types, Control Structures and Functions, NumPy for Numerical Computing, Pandas for Data Manipulation, Data Visualization with Matplotlib and Seaborn, Working with Jupyter Notebooks.
Module 3: Statistics and Probability: Descriptive Statistics, Probability Theory and Distributions, Inferential Statistics, Hypothesis Testing, Correlation and Regression Analysis, Sampling Techniques.
Module 4: Data Preparation and Cleaning: Data Collection Techniques, Handling Missing Values, Data Transformation and Scaling, Feature Engineering and Selection, Data Encoding Techniques, Outlier Detection and Treatment.
Module 5: Machine Learning Foundations: Supervised vs Unsupervised Learning, Linear and Logistic Regression, Decision Trees and Random Forest, Clustering Techniques (K-Means, Hierarchical), Model Evaluation Metrics, Cross-Validation and Hyperparameter Tuning.
Module 6: Capstone Project and Advanced Topics: Real-world Data Science Project, Model Deployment Techniques, Introduction to Deep Learning, Natural Language Processing Basics, Working with Big Data in Data Science, Building Data Science Portfolio for Career Opportunities.
