Data Science for Business Professionals: A Practical Guide for Beginners 9789389423280

Table of contents :
Cover Page
Title Page
Copyright 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
Register for GCP free account
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