Data Science

Introduction

Welcome to Introduction to Data Science. This book will give you an overview of the exciting and growing field of data science. You will explore the fundamentals of data science, learn how data science is used in various industries, and gain hands-on experience using Python programming language. Through lectures, case studies, and exercises, you will develop the skills necessary to understand data and create applications that can help answer important questions. At the end of this book, you will have a foundational understanding of data science and its impact on modern business operations.

Objectives

1. Develop the students’ understanding of the basic concepts surrounding data science.
2. Provide hands-on experience with important software tools such as Python and SQL for manipulating, analysing and visualizing data.
3. Equip students with tools for communicating data science topics effectively to stakeholders.
4. Facilitate development in particular topics such as machine learning, predictive analytics, and natural language processing for practical applications in data science projects.
5. Foster an understanding of the ethical implications of conducting data analysis on large datasets from diverse populations.

Table of Contents

Module 1 – Introduction to Data Science

• Overview of data science
• Role of data scientist
• Applications of data science in the industry
• Exploring the different datasets available for analysis.

Module 2 – Getting Started with Data Science

• Introduction to Statistics
• Descriptive and Inferential Statistics
• Mathematical Foundations of Data Science

Module 3 – Working with Data Sources and Formats

• Preparing data for analysis
• Importing, manipulating, and exporting large datasets into various formats like JSON, CSV, XML etc.

Module 4 – Exploratory Data Analysis (EDA) 

• Exploring the relationship between variables using univariate and bivariate analysis techniques
• Dimensionality Reduction Techniques like Principal Component Analysis (PCA) Module

5- Machine Learning Algorithms   

• Types of Machine Learning algorithms like Supervised, Unsupervised & Reinforcement Algorithms
• Various machine learning techniques such as Naïve Bayes Classifier, Support Vector Machines (SVM), Decision Trees, Random Forest etc.

Module 6 – Building Predictive Models

• Building predictive models through Model Selection & Evaluation techniques such as Hyperparameter Tuning, Cross Validation and Regularization Methods.

 Module 7 – Big Data Technologies & Tools   

• Understanding Hadoop File System and its components like HDFS & MapReduce
• Working with Apache Spark programming model & its libraries like MLlib & GraphX

Module 8 – Visualizing Results using Tableau/R/Python      

• Creating visualizations using tools such as Tableau/R/Python for effective exploration and communication of results.

Module 9- Exams