Description
Course Name: Certificate in Python with Data Science and Machine Learning
Course Id: CPDS&ML/Q1001.
Eligibility: Completion of 10+2 (higher Secondary) or equivalent.
Objective: The Certificate in Python with Data Science and Machine Learning is designed to provide learners with a comprehensive introduction to Python programming along with essential data science and machine learning concepts. The course covers data manipulation, statistical analysis, data visualization, supervised and unsupervised learning algorithms, and model evaluation.
Duration: Three 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- 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.
Syllabus
Introduction to Python for Data Science: Overview of Python and its applications in Data Science, Setting up the Python environment (Anaconda, Jupyter Notebook, Google Colab), Python syntax and basic operations, Data types and structures (lists, tuples, dictionaries, sets), Conditional statements and loops, Functions and lambda expressions, File handling in Python, Introduction to Python libraries for Data Science (NumPy, Pandas, Matplotlib, Scikit-learn).
Data Manipulation with NumPy and Pandas: Introduction to NumPy and its importance in numerical computing, Creating and manipulating NumPy arrays, Vectorized operations and broadcasting in NumPy, Introduction to Pandas for data analysis, DataFrames and Series: creation and manipulation, Handling missing data and duplicate values, Data filtering, sorting, and grouping in Pandas, Merging, joining, and concatenating datasets.
Data Visualization with Matplotlib and Seaborn: Importance of data visualization in Data Science, Introduction to Matplotlib and its key components, Creating line plots, bar charts, histograms, and scatter plots, Customizing plots (titles, labels, legends, colors), Introduction to Seaborn for statistical visualization, Creating heatmaps, boxplots, violin plots, and pair plots, Advanced visualization techniques for large datasets, Best practices for effective data visualization.
Exploratory Data Analysis (EDA): Understanding the importance of EDA in Data Science, Statistical summary of datasets using Pandas, Identifying data distributions and outliers, Handling categorical and numerical data, Correlation and covariance analysis, Feature engineering techniques, Data scaling and normalization, Case study: Performing EDA on real-world datasets.
Introduction to Machine Learning with Scikit-Learn: Understanding Machine Learning and its types (Supervised, Unsupervised, Reinforcement Learning), Overview of Scikit-learn library, Splitting data into training and testing sets, Evaluating model performance using metrics (accuracy, precision, recall, F1-score), Introduction to regression and classification problems, Implementing simple linear regression, Decision trees and random forests, Case study: Building a basic ML model.
Supervised Learning Techniques: Understanding supervised learning and its applications, Implementing linear and multiple regression models, Logistic regression for binary classification, Decision trees and random forests in-depth, Support Vector Machines (SVM) for classification, Hyperparameter tuning using GridSearchCV, Model evaluation and cross-validation techniques, Case study: Predicting real-world outcomes using supervised learning.
Job Opportunities after Certificate in Python with Data Science and Machine Learning
Graduates can build careers in data science, machine learning, AI, analytics, and business intelligence.
Top Roles: Data Scientist, Machine Learning Engineer, Data Analyst, AI Engineer, Business Intelligence Analyst, Deep Learning Engineer, Data Engineer, Quantitative Analyst (Quant), Computer Vision Engineer, NLP Engineer, Freelancer/Consultant, Research Scientist (Data Science/AI).
Key Skills: Python programming, data analysis, machine learning, deep learning, AI, data engineering, computer vision, NLP, predictive modeling, business intelligence.
Salary Range (India):
- Entry-level: ₹4–6 LPA
- Mid-level: ₹6–12 LPA
- Senior-level: ₹12–20 LPA+
Scope: With the rapid growth of AI, machine learning, and data-driven decision-making across industries like technology, finance, healthcare, retail, and robotics, graduates are in high demand. Career opportunities include data science, AI development, analytics consulting, and research roles with excellent growth potential.
