Certificate in Python with Data Science and Machine Learning

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 course combines:

1. Python Programming: Mastering Python as a versatile language for data analysis and machine learning.
2. Data Science Concepts: Learning how to process, analyze, and visualize data.
3. Machine Learning: Implementing algorithms and models to solve real-world problems using Python.

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.

 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 completion of Certificate in Python with Data Science and Machine Learning course:

After successful completion of the Certificate in Python with Data Science and Machine Learning program, graduates acquire specialized skills in Python programming, data science, and machine learning. This combination equips them to solve complex problems in data analysis, predictive modeling, and automation across a variety of industries. The demand for professionals with expertise in Python and data science continues to grow, as companies increasingly rely on data-driven decision-making and machine learning technologies.

Career Options for Graduates:

1. Data Scientist
   • Design, develop, and implement machine learning models to analyze complex data sets and extract meaningful insights for decision-making.
2. Machine Learning Engineer
   • Build and deploy machine learning algorithms and models, working with large data sets to improve business processes and systems.
3. Data Analyst
   • Analyze large data sets using Python libraries like Pandas, NumPy, and Matplotlib, and generate reports to support business decisions.
4. AI Engineer
   • Develop and implement artificial intelligence algorithms and systems, using Python for machine learning and deep learning applications.
5. Business Intelligence Analyst
   • Use Python and data analysis tools to collect and analyze data from various sources to support business intelligence strategies and improve business outcomes.
6. Deep Learning Engineer
   • Specialize in deep learning technologies (such as neural networks), using Python frameworks like TensorFlow, Keras, and PyTorch to create advanced AI models.
7. Data Engineer
   • Build and maintain infrastructure for data generation, collection, and storage, enabling smooth access to data for data scientists and analysts.
8. Quantitative Analyst (Quant)
   • Apply machine learning and statistical techniques to financial data, building models for risk analysis, portfolio management, and algorithmic trading.
9. Computer Vision Engineer
   • Develop machine learning models focused on image processing and computer vision applications, leveraging Python libraries like OpenCV and TensorFlow.
10. Natural Language Processing (NLP) Engineer
    • Work with text and speech data, using Python-based machine learning algorithms to build language models for applications like chatbots and translation systems.
11. Freelancer/Consultant
    • Work independently as a data science consultant, helping companies implement machine learning solutions, optimize their data strategies, and drive insights from their data.
12. Research Scientist (Data Science/AI)
    • Conduct academic or industry research in the fields of data science, machine learning, and AI, advancing the development of new technologies and algorithms.

Industries for Employment:

• Technology & Software Development
• Data Science & Analytics
• Finance & Banking
• Healthcare & Pharmaceuticals
• E-commerce & Retail
• Automotive & Robotics
• Telecommunications
• Government & Public Sector
• Education & Research
• Consulting Firms

Salary Range:

• Entry-Level Roles: ₹4,00,000 – ₹6,00,000 per annum
• Mid-Level Roles: ₹6,00,000 – ₹12,00,000 per annum
• Senior-Level Roles: ₹12,00,000 – ₹20,00,000+ per annum

Conclusion:

Graduates of the Certificate in Python with Data Science and Machine Learning program are well-prepared for high-demand roles in the rapidly evolving fields of data science and machine learning. With the growing importance of data in modern business, organizations are increasingly seeking professionals who can develop and implement advanced machine learning models, analyze large data sets, and drive data-driven decision-making. This certification opens a variety of career opportunities, including roles in AI, business intelligence, finance, healthcare, and more.