Modules.md

Modules 📁📂

Module 1: Basics of Data Science

Introduction to Data science: Applications of data science - Properties of Data: Exploring various dataset in different repositories - Tool Boxes for Data Scientist.

Module 2: Understanding Data

Working with Data: Import, Select, Filter, Manipulate, sort, group, rearrange, rank and analyze the data for missing data values. Data visualization: Plot various plots for the given dataset.

Module 3: Statistical Inference

Descriptive statistics, Exploratory Data Analysis: Calculate the mean, median, variance, and standard deviation for the given small and large dataset, analyze the correlation between the variables in the dataset, estimation, hypothesis testing: Formulate null and alternative hypothesis for real world use cases.

Module 4: Supervised Learning

Introduction to machine learning, Types of machine learning, Linear, Multiple, Logistic and Polynomial. Regression: Applications in transport, gaming and banking. KNN, Decision Trees: Applications in precision farming and smart building, calculate the performance metrics of regression and classification techniques.

Module 5: Unsupervised Learning

Clustering, Similarity and Distance measure, K means clustering: sentiment analysis. Agglomerative Clustering: gene expression data analysis. Graph based clustering techniques: smart city application.

Module 6: Recommender System

Content Based Filtering, Collaborative Filtering: Developing a retail recommendation system, Hybrid. Recommenders: Hotel recommendation system - Evaluating Recommenders