Learning Objectives – Get an introduction to Data Science in this module and see how Data Science helps to analyze large and unstructured data with different tools.

Learning Objectives – In this module, you will learn about different statistical techniques and terminologies used in data analysis.

Learning Objectives – Discuss the different sources available to extract data, arrange the data in structured form, analyze the data, and represent the data in a graphical format.

Learning Objectives – Get an introduction to Machine Learning as part of this module. You will discuss the various categories of Machine Learning and implement Supervised Learning Algorithms.

Learning Objectives – In this module, you should learn the Supervised Learning Techniques and the implementation of various techniques, such as Decision Trees, Random Forest Classifier, etc. 

Learning Objectives – Learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data. 

Learning Objectives – In this module, you should learn about association rules and different types of Recommender Engines.

Learning Objectives – Discuss Unsupervised Machine Learning Techniques and the implementation of different algorithms, for example, TF-IDF and Cosine Similarity in this Module.

Learning Objectives – In this module, you should learn about Time Series data, different component of Time Series data, Time Series modeling – Exponential Smoothing models and ARIMA model for Time Series Forecasting.

Learning Objectives – Get introduced to the concepts of Reinforcement learning and Deep learning in this module. These concepts are explained with the help of Use cases. You will get to discuss Artificial Neural Network, the building blocks for Artificial Neural Networks, and few Artificial Neural Network terminologies.