Data science for Business

Introduction

Welcome to the Data Science for Business book. This course has been designed to help business professionals better understand and apply data science concepts in their working environments. Through our sessions, we will focus on topics such as data acquisition, analysis, visualization, and communication. You will gain skills in identifying unmet customer needs and opportunities for improvement, recognizing areas of potential risk or aberrant behavior, exploring data-driven strategies for improving decision-making processes and optimizing operations. Additionally, you will learn how to apply these methods to make informed decisions with real world applications.

This book is intended for individuals who are curious about data science for business purposes or those who are seeking an understanding of the fundamental tools that can be used when working with large datasets. By the end of this book, you will have a strong foundation of knowledge that can help catapult your career path forward!

Objectives

1. Understand how data science can be applied to improve business operations and decision making.
2. Learn how to collect, analyze, and visualize data in meaningful ways.
3. Develop the ability to recognize opportunities for using data-driven insights to optimize performance and profitability.
4. Explore common tools used in data analysis and predictive analytics.
5. Master techniques for creating effective and actionable models from collected datasets.
6. Examine strategies for advancing projects through the entire lifecycle, including implementation and evaluation stages.

Table of Contents

Module 1: Introduction to Data Science

-Overview of Data Science Concepts

-What is data science?

-Common terminology used in the data science world

-Types of data

-Data collection methods

-Analyze, and visualize data in meaningful ways

-How data science can be applied to improve business operations and decision making.

-Ability to recognize opportunities for using data-driven insights to optimize performance and profitability

Module 2: Exploratory Data Analysis (EDA) and Preprocessing:

-Understanding the dataset & gathering insights from descriptive statistics & visualizations

– Common tools used in data analysis and predictive analytics

-Data preprocessing techniques such as missing value imputation, normalization, one hot encoding etc.

-Feature engineering & selection processes to improve predictive models & reduce noise.

-Master techniques for creating effective and actionable models from collected datasets.

-Strategies for advancing projects through the entire lifecycle, including implementation and evaluation stages.

Module 3:  Basic Statistical Modeling Techniques:

-Various types of regression models such as linear regression, logistic regression etc.

-How to evaluate these models’ using metrics like R2 scores and AUC scores

-Different techniques in dealing with imbalanced datasets

Module 4: Advanced Machine Learning Algorithms:     

-Supervised/Unsupervised learning algorithms: kmeans clustering, support vector machines, decision trees, random forests etc.

-Model tuning using hyperparameter optimization

-Using Neural Networks for classification tasks and Deep Learning algorithms for unstructured data such as images and audio files.

Module 5:  Other Important Topics in Data Science        

-Use cases of Natural Language Processing (NLP) and their application in businesses

-Using flask/ Django REST API frameworks to build interactive web applications on top of trained models

-Deployment of ML Models on cloud platforms such as AWS/GCP/Azure

Module 6: Exams