Attribute

Detail

Format

Online (e-LMS)

Level

Intermediate

Duration

6 Months

Certification

e-Certification + e-Marksheet

Tools

Python, R, TensorFlow, Keras, scikit-learn, NumPy

About the Python Course

Python for Data Science Course dives deep into Python For Data Science.

Gain comprehensive expertise through our structured curriculum and hands-on approach.

Program Highlights

• Comprehensive coverage of Python for Data Science from fundamentals to advanced applications

• Hands-on projects and real-world case studies in Data Science

• Expert-curated curriculum aligned with current industry standards

• Access to recorded lectures and e-LMS platform for flexible, self-paced learning

• e-Certification and e-Marksheet upon successful completion

• Dedicated mentor support and interactive doubt-clearing sessions

• Practical experience with tools: Python, R, TensorFlow, Keras

• Career-oriented training for academic and professional growth in Data Science

Course Curriculum

Module 1: AI Fundamentals, Mathematics, and Python Foundations

Develop a comprehensive understanding of linear algebra and calculus for data science applications
Analyze the fundamentals of probability and statistics for machine learning model development
Configure Python environments and libraries, including NumPy, pandas, and Matplotlib, for data science tasks

Module 2: Data Engineering, Preprocessing, and Feature Pipelines

Design and implement data pipelines using Apache Beam and Apache Spark for large-scale data processing
Evaluate and preprocess datasets using techniques such as handling missing values, data normalization, and feature scaling
Implement data quality checks and data validation using Python libraries like Great Expectations and Pandas

Module 3: Model Architecture, Algorithm Design, and Python Methods

Develop and train machine learning models using scikit-learn and TensorFlow for classification, regression, and clustering tasks
Analyze and compare the performance of different algorithmic approaches, including decision trees, random forests, and neural networks
Optimize model hyperparameters using techniques such as grid search, random search, and Bayesian optimization

Module 4: Training, Hyperparameter Optimization, and Evaluation

Configure and train deep learning models using Keras and TensorFlow for image classification, natural language processing, and time series forecasting
Evaluate the performance of machine learning models using metrics such as accuracy, precision, recall, F1 score, and mean squared error
Implement cross-validation techniques, including k-fold cross-validation and stratified cross-validation, for model evaluation and selection

Module 5: Deployment, MLOps, and Production Workflows

Design and deploy machine learning models using Docker, Kubernetes, and cloud platforms like AWS and GCP
Develop and implement model serving pipelines using TensorFlow Serving, AWS SageMaker, and Azure Machine Learning
Configure and monitor model performance in production environments using tools like Prometheus, Grafana, and New Relic

Module 6: Ethics, Bias Mitigation, and Responsible AI Practices

Analyze and identify potential biases in machine learning models and datasets using techniques such as data auditing and fairness metrics
Develop and implement strategies for bias mitigation, including data preprocessing, feature engineering, and model regularization
Evaluate the ethical implications of AI systems and develop guidelines for responsible AI development and deployment

Module 7: Industry Integration, Business Applications, and Case Studies

Develop and present business cases for AI adoption in various industries, including healthcare, finance, and retail
Analyze and discuss real-world applications of machine learning, including recommender systems, natural language processing, and computer vision
Design and propose AI-powered solutions for business problems, including customer segmentation, demand forecasting, and supply chain optimization

Tools, Techniques, or Platforms Covered

Python
R
TensorFlow
Keras
scikit-learn
NumPy
pandas
Matplotlib

Real-World Applications

Apply Python for Data Science skills directly to academic research, thesis work, and publications
Build a professional portfolio showcasing practical Data Science competencies
Solve industry-relevant problems using Python for Data Science methodologies and tools
Contribute to open-source projects and collaborative research in Data Science
Prepare for competitive examinations, interviews, and professional certifications in Data Science

Who Should Attend & Prerequisites

Designed for Professionals.
Designed for Students.
Foundational knowledge of artificial intelligence and familiarity with core concepts recommended.
Mentorship by industry experts and NSTC faculty.

Prerequisites:

Frequently Asked Questions

1. What is the format of this Python for Data Science course?

This is an Online (e-LMS) course delivered via our e-LMS platform. You will have access to pre-recorded video lectures, reading materials, assignments, quizzes, and hands-on projects that you can complete at your own pace.

2. Will I receive a certificate after completing this course?

Yes! Upon successful completion of all modules, assignments, and assessments, you will receive an e-Certification along with an e-Marksheet from NanoSchool (NSTC) that you can showcase on your CV and LinkedIn profile.

3. What are the prerequisites for this course?

Learners should have a foundational understanding of Data Science concepts. Familiarity with basic tools and programming is recommended.

4. How long will I have access to the course materials?

You will have access to all course materials for the duration of 6 Months. The self-paced format allows you to learn according to your own schedule through our online learning management system.

5. Is mentor support available during the course?

Yes, dedicated mentor support is available throughout the course. You can reach out for doubt-clearing sessions, project guidance, and career advice related to Data Science. Our mentors are industry experts and experienced professionals.

Enroll in Python for Data Science today and take the next step in your professional journey. With expert-curated content, practical projects, and industry-recognized certification, this course is your gateway to mastering Data Science skills that matter.

Format	Online (e-LMS)

Achieve Excellence & Enter the Hall of Fame!

Elevate your research to the next level! Get your groundbreaking work considered for publication in prestigious Open Access Journal (worth USD 1,000) and Opportunity to join esteemed Centre of Excellence. Network with industry leaders, access ongoing learning opportunities, and potentially earn a place in our coveted

Hall of Fame.

Achieve excellence and solidify your reputation among the elite!