Part 1 -  Data Science Fundamentals

Topics Covered: Python Scripting, Object-Oriented Programming, Introduction to Data Science, Data Extraction, Wrangling, & Visualization

Learning Objectives:​

• Learn the fundamentals of Python programming language.

• Learn how Data Science helps to analyze large and unstructured data with different tools.

• Understand the Life cycle of Data Science

• Learn about Tools used for Data Science

• Learn different sources available to extract data, arrange the data in structured form, analyze the data, and represent the data in a graphical format.

Part 2- Machine Learning with Python

Topics Covered: Machine Learning with Python Fundamentals, Supervised Learning, Dimensionality Reduction, Unsupervised Learning

Learning Objectives:

• Learn the concept of Machine Learning with Python and it’s types.

• Learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.

• Learn about impact of dimensions within data. 

• Learn how to perform factor analysis using PCA and compress dimensions.\learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.

Part 3- Advanced Machine Learning

Topics Covered: Association Rules Mining and Recommendation Systems, Reinforcement Learning, Time Series Analysis, Model Selection, and Boosting

Learning Objectives:

• Learn Association rules and their extension towards recommendation engines with Apriori algorithm.

• Learn about developing a smart learning algorithm such that the learning becomes more and more accurate as time passes by.

• Learn how to define an optimal solution for an agent-based on-agent environment interaction.

• Learn Markov’s Decision Process

• Learn about Time Series Analysis to forecast dependent variables based on time. 

• Learn about Model Selection and Boosting