Courses

Gain a solid foundation in the principles of relational databases.

Learn how to create and manage databases.

Master DDL commands with practical examples to create and modify database structures.

Master the different types of joins (Left Outer, Right Outer, Inner, Full Outer, and Self Joins) to combine data from multiple tables.

Understand DCL for managing access to the database.

Explore how to manage transactions within a database.

Learn to retrieve data using various SQL queries.

Learn how to use DML commands to manipulate data within the database.

Learn to create summaries and perform calculations using SQL aggregate functions.

Understand and apply advanced SQL window functions.

Apply your knowledge through a small project involving a real-world case study.

Understand the core concepts and functionalities of Power BI.

Learn how to connect Power BI to various data sources, including databases, cloud services, and files.

Master the use of Power Query for cleaning, transforming, and preparing data for analysis.

Develop skills in modelling data, including creating calculated columns, measures, and tables.

Gain insights into managing and understanding relationships between different data sets within Power BI.

Learn to create compelling visualizations and reports that effectively communicate data insights.

Discover how to publish reports to the Power BI Service for sharing and collaboration.

Apply your knowledge in a real-world scenario through a small project that involves a use case, reinforcing your learning experience.

Master measures of central tendency, dispersion, skewness, and kurtosis to effectively describe and summarize data. Utilize probability and probability distributions to make informed decisions.

Learn to use techniques such as Principal Component Analysis, Hierarchical and Non-Hierarchical Clustering, Logistic Regression, K-Nearest Neighbors, Support Vector Machines, Naïve Bayes, Random Forests, Decision Trees, Gradient Boosting, and XGBoost.

Gain insights into Markov Chains, Extended Markov Chains, and Bellman’s Equation to understand reinforcement learning concepts.

Analyze relationships between variables and predict outcomes using various regression techniques.

Develop and apply Artificial Neural Networks, Recurrent Neural Networks, and Convolutional Neural Networks for tasks like image detection and analysis.

Employ techniques such as Bag of Words, TF-IDF, Word Embedding, and Sentiment Analysis to analyze and interpret text data.

Learn to build and deploy machine learning applications using frameworks such as Django, Flask, BentoML, and FastAPI.

Design and implement recommendation systems using Next Best Offer Product Recommendations, Collaborative Filtering, and Content-Based Filtering.