Data Science and AI Certification

Why should you take Data Science & AI Certification course?

• Students will master in-demand skills in Data Science, AI, and Machine Learning, making them job-ready for high-paying careers.
• They will gain hands-on experience through real-world projects, learning to analyze data, build predictive models, and deploy AI solutions.
• With expert mentorship, they will receive personalized career support, including resume building and mock interviews.
• The flexible learning approach allows them to study at their own pace with live and recorded sessions.
• They will also learn MLOps, cloud deployment, and AI integration—key skills for today’s industry.
• By earning a globally recognized certification, students will boost their credibility and unlock top career opportunities in data science.

Who should take Data Science & AI Certification course?

• This course is ideal for aspiring data scientists, working professionals, and career changers looking to master Data Science, data analytics, AI and Machine Learning.
• It’s perfect for students, graduates, and entrepreneurs who want to leverage data science for career growth or business success.

Curriculum for the Data Science & AI Certification Program

Data Science & AI is a rapidly growing field that involves using statistical, analytical, and technological techniques to derive insights from data and build intelligent systems. This certification program is designed to prepare you comprehensively, combining core data science skills with the latest advancements in Generative AI and MLOps, along with interview preparation. To make you master Data Science & AI thoroughly, we have divided the program into 3 levels, each designed with a structured learning path:

In the first level, you focus on Core Data Science Foundations, where you will learn:

• Python Programming: Variables, data types, loops, functions, object-oriented programming
• Data Handling & Analysis: Pandas, NumPy, API integration, SQL, web scraping
• Mathematics for Data Science: Probability, matrix operations, linear algebra, derivatives
• Statistics & Machine Learning: Regression, classification, hypothesis testing, clustering, decision trees, Random Forest, SVM, PCA, XGBoost, and model evaluation metrics
• Data Visualization: Using Matplotlib, Seaborn, Power BI, and Tableau for creating insightful dashboards and reports

In the second level, you dive into Advanced AI Techniques and Generative AI, covering:

• Deep Learning: Neural Networks, CNNs, RNNs, LSTM for time series forecasting, activation functions, backpropagation
• Natural Language Processing (NLP): Text cleaning, TFIDF, Word2Vec, POS tagging, Named Entity Recognition (NER), stemming & lemmatization
• Explainable AI: Techniques like LIME and SHAP to make models interpretable
• Generative AI & AI Agents: BERT, ChatGPT API usage, RAG (Retrieval-Augmented Generation), AI Agents development using LangChain & Phidata
• AI Project Management: Planning AI projects, defining KPIs, stakeholder management, deploying AI solutions effectively

The third level is focused on Real-world Applications, MLOps, and Interview Preparation, including:

• MLOps: Model deployment using Flask API, Docker, AWS, batch processing, model monitoring, Git best practices
• Capstone Projects: Hands-on projects across domains like Finance (fraud detection), Healthcare (disease prediction), E-commerce (recommendation systems), Manufacturing, Automotive, Telecom, and Cybersecurity, including applications using Generative AI & AI Agents
• Interview Preparation & Career Support: Mock interviews, resume review, key Q&A documents, 300+ frequently asked questions, job application guidance, and placement assistance

Tools you will learn in Data Science & AI Certification course

Skills :

• Python Programming
• Data Cleaning & Preprocessing
• Statistical Analysis
• Machine Learning Algorithms (Regression, Classification, Clustering)
• Deep Learning & Neural Networks
• Natural Language Processing (NLP)
• Time Series Forecasting
• Explainable AI Techniques
• AI Project Management
• MLOps & Model Deployment
• Generative AI Development (ChatGPT, BERT, AI Agents)
• Data Visualization and Dashboarding
• SQL for Data Manipulation
• Resume Building & Interview Cracking Skills

Tools & Technologies:

• Python (NumPy, Pandas, Matplotlib, Seaborn)
• SQL
• Power BI
• Tableau
• Scikit-learn
• TensorFlow
• Flask
• Docker
• AWS (Amazon Web Services)
• LangChain
• Phidata
• Git & GitHub
• ChatGPT API
• SHAP & LIME (Explainable AI)