Data Science Course

• Understand the basics of SQL, such as creating tables, updating tables, etc.
• Deep dive into SQL joins and learn about Left join, Right join, etc.
• Know why SQL functions are needed and how to use them, along with using subqueries in SQL.
• Learn SQL Nested Queries and understand User Defined Functions in SQL.
• Learn how to do SQL Optimization and improve the performance of SQL queries.

• Understand how to use Python for Data Science and the importance of Python APIs. Learn how to create APIs with Python.
• Deep-dive into the nitty-gritty of Data Manipulation and Data Handling in Python using NumPy and Pandas.
• Know the importance of Data Preprocessing and Visualization and their Python implementation.

• Introduction to machine learning, why it is essential, and how it works.
• Understand regression, classification, and clustering algorithms. Also, learn when to use which algorithm.

Supervised Learning – Classification and Regression Implementation

• Understand what linear and logistic regression is and the difference between them.
• Learn and implement Decision Tree and Random Forest algorithms, and understand when to use decision tree vs random forest.
• Acquire hands-on skills in Support Vector Machine and Gradient Descent.
• Learn what K-Nearest Neighbors and Time Series Forecasting is.

Unsupervised Learning – K-means clustering and Dimensionality reduction

• Master the fundamentals of K-means clustering along with Dimensionality reduction. Learn linear discriminant analysis and principal component analysis.
• Understand the importance and usage of Performance Metrics and Classification Reports.
• Learn how to evaluate machine learning models using the Confusion matrix.

• Learn the basics of Artificial Intelligence and Deep Learning. Get a deeper understanding of neural networks and their different types.
• Understand the internal workings of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), along with various real-time scenarios.

• Explore the fundamentals of LSTM and its advantages over RNN. Learn to make them suitable for complex time series data analysis and sequence-based tasks.
• Deep-dive into the mechanics of Transformer architecture, learn how they have improved traditional NLP techniques, and why they are better at understanding context.
• Learn what BERT is and how it can understand the deep contextual meaning of sentences. Learn its application in text classification, sentiment analysis, and question answering.
• Understand GPT models and the importance of autoregressive learning. Learn how GPT models are trained on vast datasets and how to fine-tune them for different creative applications.
• Learn the evolution of large language models (LLMs) and their architecture, to perform complex language understanding and generation tasks.

• Use ETL processes to make data usable, along with data manipulation and visualization techniques to gather insights and pre-process data.
• Understand how to do Feature Engineering to enhance the performance of machine learning models. Learn to select the right supervised or unsupervised algorithm for varied use cases and problems.
• Learn how to evaluate and monitor machine learning models.

• Recommendation Engine: Learn how to create recommendation engines using collaboration filtering and content-based methods. Then, render personalized recommendations using datasets from E-Commerce platforms and video streaming services.
• Rating Predictions: Learn how to predict user ratings using machine learning models on various datasets such as movie ratings, product reviews, and customer satisfaction analysis.
• Census: Analyse and interpret Census data using various machine-learning techniques. Learn to find patterns using multiple criteria, such as demographics, age, etc.
• House Price Prediction: Learn to build a machine learning model in this certification course that can predict house prices by being trained on historical house prices.
• Object Detection: Understand the fundamentals of object detection. Learn how to detect objects in images and videos.
• Stock Market Analysis: Learn to analyze stock market data using time series analysis and learn how to build predictive models for stock prices and market trends.
• Banking Problem: Learn to solve real-world banking problems using machine learning models. Work on problems such as fraud detection, credit risk assessment, and improved decision-making.
• AI Chatbot: Work on creating powerful chatbots powered by AI. Learn how to design a conversational AI using Natural Language techniques.

• Learn the basics of PowerBI, Data Modelling, and DAX. Then, implement Time Intelligence Functions and learn the advanced features of DAX.
• Understand the use of Slicers, filters, and Drill-down Reports. Learn features such as Power BI Query, Q&A, and Data Insights.
• Deep dive into Power BI Settings, Administration, and Direct Connectivity
• Learn how to integrate PowerBI features into custom applications using the Embedded Power BI API. Then, explore the features of Power BI Mobile for accessing reports and dashboards.
• Understand the advanced concepts of Power BI functionalities such as complex data modeling, DAX formulas, and report optimization.
• Learn Power BI Premium and the extra features it offers.

• Learn the importance of version control and the different types of tools that can help with version control.
• Get introduced to Git and it’s lifecycle. Learn the standard Git commands.
• Understand the concept of branching in git, why it is used, and how to create and use branches in git.
• Connect your local git repository to GitHub, and learn how to authenticate to GitHub using SSH and HTTP.
• Learn how to merge branches and how to resolve Merge Conflicts.
• Understand how to work with big development teams and cross-team using Git workflows.