Name: Data Science for Business Professionals
Price: 19.95 USD
ISBN: 9789389423280

BPB Online LLP

Probyto Data Science and Consulting Pvt Ltd.

Data Science for Business Professionals

US$ 19.95

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Description

Contents

Reviews

Primer into the multidisciplinary world of Data Science

Key Features
Explore and use the key concepts of Statistics required to solve data science problems
Use Docker, Jenkins, and Git for Continuous Development and Continuous Integration of your web app
Learn how to build Data Science solutions with GCP and AWS

Description
The book will initially explain the What-Why of Data Science and the process of solving a Data Science problem. The fundamental concepts of Data Science, such as Statistics, Machine Learning, Business Intelligence, Data pipeline, and Cloud Computing, will also be discussed. All the topics will be explained with an example problem and will show how the industry approaches to solve such a problem. The book will pose questions to the learners to solve the problems and build the problem-solving aptitude and effectively learn. The book uses Mathematics wherever necessary and will show you how it is implemented using Python with the help of an example dataset.

What you will learn
Understand the multi-disciplinary nature of Data Science
Get familiar with the key concepts in Mathematics and Statistics
Explore a few key ML algorithms and their use cases
Learn how to implement the basics of Data Pipelines
Get an overview of Cloud Computing & DevOps
Learn how to create visualizations using Tableau

Who this book is for
This book is ideal for Data Science enthusiasts who want to explore various aspects of Data Science. Useful for Academicians, Business owners, and Researchers for a quick reference on industrial practices in Data Science.

Table of Contents
1. Data Science in Practice
2. Mathematics Essentials
3. Statistics Essentials
4. Exploratory Data Analysis
5. Data preprocessing
6. Feature Engineering
7. Machine learning algorithms
8. Productionizing ML models
9. Data Flows in Enterprises
10. Introduction to Databases
11. Introduction to Big Data
12. DevOps for Data Science
13. Introduction to Cloud Computing
14. Deploy Model to Cloud
15. Introduction to Business Intelligence
16. Data Visualization Tools
17. Industry Use Case 1 – FormAssist
18. Industry Use Case 2 – PeopleReporter
19. Data Science Learning Resources
20. Do It Your Self Challenges
21. MCQs for Assessments

About the Author
The book has been written by collective experience of many of Probyto past client projects, academic collaborations and team members for last 5 years. The collective work is represented by different experts in data driven decision making and portion they deal with in creating value for the clients at Probyto. The team has experienced professionals and freshers who have gained from the approach as mentioned in the book as well. Two key contributions for the book goes to Parvej Reja Saleh (Manager) and Namachivayam Dharmalingam (Senior Analyst).

Blog links: https://probyto/resources/blogs

LinkedIn Profile: https://www.linkedin.com/company/probyto

Language

English

ISBN

9789389423280

big data

business intelligence

data science questions

Cover Page

Title Page

Dedication

About the Author

Acknowledgement

Preface

Errata

Table of Contents

1. Data Science Overview

Structure

Objectives

Evolution of data analytics

Define data science

Domain knowledge

Mathematical and scientific techniques

Tools and technology

Data science analysis types

Data science job roles

ML model development process

Data visualizations

Result communication

Responsible and ethical AI

Career in data science

Conclusion

2. Mathematics Essentials

Structure

Objectives

Introduction to linear algebra

Scalar, vectors, matrices, and tensors

Scalar

Vectors

Matrices

Tensors

The determinant

Eigenvalues and Eigenvectors

Eigenvalue decomposition and Singular Value Decomposition (SVD)

Singular value decomposition

Principal component analysis

Multivariate calculus

Differential Calculus

Sum rule

Power rule

Special cases

Trigonometric functions

Product rule

Chain rule

Quotient rule

Multiple variables

Partial differentiation

Total derivative

Integral calculus

Slices

Definite vs.indefinite integrals

The Gradient

The Jacobian

The Hessian

The Lagrange multipliers

Laplace interpolation

Optimization

The Gradient Descent algorithm

Conclusion

3. Statistics Essentials

Structure

Objectives

Introduction to probability and statistics

Descriptive statistics

The measure of central tendency

Mean

Median

Mode

Measures of variability

Range

Variance

Covariance

Standard Deviation

Measure of asymmetry

Modality

Skewness

Populations and samples

Central Limit Theorem

Sampling distribution

Conditional probability

Random variables

Inferential statistics

Probability distributions

What is a probability distribution?

Normal distribution

Binomial distribution

Poisson distribution

Geometric distribution

Exponential distribution

Conclusion

4. Exploratory Data Analysis

Structure

Objectives

What is EDA?

Need for the EDA

Understanding data

Categorical variables

Numeric variables

Binning (numeric to categorical)

Encoding

Methods of EDA

Key concepts of EDA

Conclusion

5. Data Preprocessing

Structure

Objectives

Introduction to data preprocessing

Methods in data preprocessing

Transformation into vectors

Normalization

Dealing with the missing values

Conclusion

6. Feature Engineering

Structure

Objectives

Introduction to feature engineering

Importance of feature variable

Feature engineering in machine learning

Feature engineering techniques

Imputation

Handling outliers

Binning

Log Transform

One-hot encoding

Grouping operations

Categorical column grouping

Numerical column grouping

Feature split

Scaling

Extracting date

Applying feature engineering

Conclusion

7. Machine Learning Algorithms

Structure

Objectives

Introduction to machine learning

Brief history of machine learning

Classification of machine learning algorithms

Top 10 algorithms of machine learning explained

Building a machine learning model

Conclusion

8. Productionizing Machine Learning Models

Structure

Objectives

Types of ML production system

Batch prediction

Batch learning

REST APIs

Online learning

Introduction to REST APIs

Application Programming Interface (APIs)

Hyper Text Transfer Protocol (HTTP)

Client-server architecture

Resource

Flask framework

Simple flask application

Salary prediction model

ML model user interface

HTML template

Conclusion

9. Data Flows in Enterprises

Structure

Objectives

Introducing data pipeline

Designing data pipeline

ETL vs. ELT

Scheduling jobs

Messaging queue

Passing arguments to data pipeline

Conclusion

10. Introduction to Databases

Structure

Objectives

Modern databases and terminology

Relational database or SQL database

Install PostgreSQL and pgAdmin

Set-up a database and table

Connect Python to Postgres

Modify data pipeline to store in Postgres

Document-oriented database or No-SQL

Install MongoDB and compass client

Create a database and collection

Connect Python to MongoDB

Modify data pipeline to store in MongoDB

Graph databases

Install and start Neo4j

Add nodes and relations

Filesystem as storage

What is Filesystem?

Filesystem as data store

Hierarchy to store CSV

Conclusion

11. Introduction to Big Data

Structure

Objectives

Introducing Big Data

Definition of Big Data

Introducing Hadoop

Hadoop Distributed File System (HDFS)

MapReduce

YARN

Hadoop common

Setting-up a Hadoop Cluster

Installing a Hadoop Cluster

Starting Hadoop cluster in Docker

Word-count MapReduce Program

Map program

Reducer program

MapReduce JAR

Running Word Count in HDFS Cluster

Conclusion

12. DevOps for Data Science

Structure

Objectives

Introduction to DevOps

Agile methodology, CI/CD, and DevOps

DevOps for data science

Source Code Management

Quality Assurance

Model objects andsecurity

Production deployment

Communication and collaboration

Conclusion

13. Introduction to Cloud Computing

Structure

Objectives

Introducing cloud computing

Operating system model

What is virtualization?

What is cloud computing?

Types of cloud services

Infrastructure as a Service (IaaS)

Platform as a Service (PaaS)

Software as a Service (SaaS)

Types of cloud infrastructure

Public cloud

Private cloud

Hybrid cloud

Data science and cloud computing

Data

Compute

Integration

Deployment

Market growth of cloud

Conclusion

14. Deploy Model to Cloud

Structure

Objectives

GCP console

Create VM and its properties

Connecting and uploading code to VM

Executing Python model on cloud

Access the model via browser

Scaling the resources in Cloud

Conclusion

15. Introduction to Business Intelligence

Structure

Objectives

What is business intelligence?

Business intelligence analysis

Business intelligence process

Step 1: Data awareness

Data types

Data sources

Step 2: Store data

Data models

Data storage

Step 3: Business needs

Key Performance Indicators (KPI)

Data Visuals

Step 4: a Visualization tool

Time to insight

Ease of use

Step 5: Enable platform

Data access

Business users

Business intelligence trends

Gartner 2019 Magic Quadrant

Conclusion

16. Data Visulazation Tools

Structure

Objectives

Introduction to data visualization

Data visualization types

Data visualization tools

Visualization tool features

Introduction to Microsoft Power BI

Use case Microsoft Power BI

Microsoft Power BI console

Load the data

Create data visuals

Publish the visuals

Conclusion

17. Industry Use Case 1 - Form Assist

Structure

Objective

Abstract

Introduction

Related Work

Proposed work

Work architecture

NIST dataset

Activation function – ReLU

Dropout

Data augmentation

Optimization

Feature extraction

Image thresholding

Classifier

Results

Conclusion

Acknowledgment

References

18. Industry Use Case 2 - People Reporter

Structure

Objective

Abstract

Introduction

Event detection

Work architecture

Results

Nipah virus outbreak in Kerala

CSK enters the final of IPL 2018:

OnePlus 6 launched in India

Conclusion

Acknowledgment

References

19. Data Science Learning Resources

Structure

Objective

Books

Online courses

Competitions

Blogs and magazines

University courses

Conferences and events

Meet-ups and interest groups

YouTube channels and Podcasts

Analytic reports and white paper

Talk to people

Conclusion

20. Do It Your Self Challenges

Structure

Objectives

DIY challenge 1 – Analyzing the pathological slide for blood analysis

Challenge overview

Challenge statement

Target users

Resources

IP source

DIY challenge 2 – IoT based weather monitoring system

Challenge overview

Challenge statement

Target Users

Resources

IP source

DIY challenge 3 – Facial image-based BMI calculator

Challenge overview

Challenge statement

Target users

Resources

IP source

DIY challenge 4 – Chatbot assistant for Tourism in North East

Challenge overview

Challenge statement

Target users

Resources

IP source

DIY challenge 5 – Assaying and grading of fruits for e-procurement

Challenge overview

Challenge statement

Target users

Resources

IP source

Conclusion

21. Qs for DS Assessment

Structure

Objectives

Data Science Overview

Mathematics Essentials

Statistics Essentials

Exploratory Data Analysis

Data Preprocessing

Feature Engineering

Machine Learning Algorithms

Productionizing Machine Learning Models

Data Flows in Enterprises

Introduction to Databases

Introduction to Big Data

DevOps for Data Science

Introduction to Cloud Computing

Deploy Model to Cloud

Introduction to Business Intelligence

Data Visualization Tools

Conclusion

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