Data Analytics using Python, 2ed
ISBN: 9789357469449
828 pages
For more information write to us at: acadmktg@wiley.com
Description
In this competitive world, there is a need to continue supporting core strengths in higher education: build on a reputation for excellence and diversity in learning and teaching, world-leading research and an enviable record of knowledge exchange. The goal of this book is to open the doors of opportunity related to different analytical techniques from a broader array of datasets. It is an attempt to provide a reservoir of updated knowledge on varied tools for academicians, consultants, research scholars, practitioner and students. Data Analytics using Python will enable readers gain sufficient knowledge and experience to perform analysis using different tools and techniques available in numerous libraries according to different requirement of the user for different types of data. In order to provide a more meaningful and easier learning experience, this book has been written with more interesting and relevant real-life examples. The examples taken from a variety of sectors are solved with proper explanation of code and comments are used for better clarity. This easy-to-understand approach would enable readers to develop the required skills and apply techniques to solve all types of problems in Python in an effective manner.
The second edition of this book includes new chapters related to web scraping with Beautiful Soup and Selenium, new visualization technologies like dashboard with streamlit and graphical user interface with tkinter, integration of NoSQL databases like Neo4j, MongoDB and more SQL databases, advanced mathematical and statistical techniques like optimization and conjoint analysis, object oriented programming in Python, machine learning techniques like recommendation system, apriori algorithm and association rules and many more.
SECTION 1 Programming in Python
Chapter 1 Introduction to Python
1.1 Features of Python
1.2 Installation of Python
1.3 Getting Started
1.4 Variables in Python
1.5 Output in Python
1.6 Input in Python
1.7 Operators
1.8 Core Modules in Python
1.9 Core Libraries in Python
Summary
Multiple-Choice Questions
Review Questions
Chapter 2 Control Flow Statements
2.1 Decision-Making Structures
2.2 Loops
2.3 Nesting of Conditional Statements and Loops
2.4 Abnormal Loop Termination
2.5 Errors and Exception Handling
2.6 User-Defined Functions
2.7 Multithreading
Summary
Multiple-Choice Questions
Review Questions
Chapter 3 Object Oriented Programming
3.1 Encapsulation
3.2 Inheritance
3.3 Polymorphism
Summary
Multiple-Choice Questions
Review Questions
Chapter 4 Modules
4.1 In-Built Modules in Python
4.2 User-Defined Module
Summary
Multiple-Choice Questions
Review Questions
SECTION 2 Data Structures, Databases and Dataframes
Chapter 5 Data Structures
5.1 Lists
5.2 Tuples
5.3 Dictionary
Summary
Multiple-Choice Questions
Review Questions
Chapter 6 NumPy Library for Arrays
6.1 One-Dimensional Array
6.2 Multidimensional Arrays
Summary
Multiple-Choice Questions
Review Questions
Chapter 7 Pandas Library for Data Processing
7.1 Basics of Dataframe
7.2 Import of Data
7.3 Functions of Dataframe
7.4 Data Extraction
7.5 Group by Functionality
7.6 Creating Charts for Dataframe
7.7 Data Tables
7.8 Missing Values
7.9 Data Transformation
7.10 Data Type Transformation
Summary
Multiple-Choice Questions
Review Questions
Chapter 8 SQL Databases – SQLAlchemy, MS-Access, MySQL and SQL SERVER
8.1 Basic SQL
8.2 Advanced SQL for Multiple Tables
8.3 SQL with MS-Access Database
8.4 SQL with MySQL Database
8.5 SQL with SQL Server Database
Summary
Multiple-Choice Questions
Review Questions
Chapter 9 NoSQL Databases - MongoDB and Neo4J
9.1 MongoDB Database
9.2 Neo4J
Summary
Multiple-Choice Questions
Review Questions
Chapter 10 Web Scraping
10.1 Web Scraping with API of Website
10.2 Web Scraping with Beautiful Soup
10.3 Web Scraping with Selenium
Summary
Multiple-Choice Questions
Review Questions
SECTION 3 Data Visualization
Chapter 11 Matplotlib Library for Visualization
11.1 Charts Using plot() Function
11.2 Pie Chart
11.3 Violin Plot
11.4 Scatter Plot
11.5 Histogram
11.6 Bar Chart
11.7 Area Plot
11.8 Quiver Plot
11.9 Mesh Grid
11.10 Contour Plot
11.11 Animation
Summary
Multiple-Choice Questions
Review Questions
Chapter 12 Seaborn for Advanced Visualization
12.1 Visualization for Categorical Variable
12.2 Visualization for Continuous Variable
Summary
Multiple-Choice Questions
Review Questions
Chapter 13 TKinter for Graphical User Interface
13.1 Layout Manager
13.2 Widgets
13.3 Integrated Application
Summary
Multiple-Choice Questions
Review Questions
Chapter 14 Streamlit for Interactive Dashboards
14.1 Select Box
14.2 Radio Button
14.3 Check Box
14.4 Slider
14.5 Number Select
14.6 Multiselect
Summary
Multiple-Choice Questions
Review Questions
SECTION 4 Basic Mathematical and Statistical Techniques
Chapter 15 Pulp Library for Optimisation Techniques
15.1 Maximization Problems
15.2 Minimization Problems
Summary
Multiple-Choice Questions
Review Questions
Chapter 16 SciPy Library for Comparing Means
16.1 Basic Statistics
16.2 Parametric Techniques for Comparing Means
16.3 Non-Parametric Techniques for Comparing Means
Summary
Multiple-Choice Questions
Review Questions
Chapter 17 Time Series Analysis
17.1 Time Series Object
17.2 Determining Stationarity
17.3 Making Time Series Stationary
17.4 ARIMA Modeling
Summary
Multiple-Choice Questions
Review Questions
Chapter 18 Conjoint Analysis
18.1 Measuring Part Worth Utilities of Level
18.2 Measuring Part Worth Utilities of Each Attribute
18.3 Comparison of Different Plans
Summary
Multiple-Choice Questions
Review Questions
SECTION 5 Unsupervised Machine Learning
Chapter 19 Association Rules and Apriori Algorithm
19.1 Apriori Algorithm
19.2 Association Rules
Summary
Multiple-Choice Questions
Review Questions
Chapter 20 Dimension Reduction Algorithms
20.1 Factor Analysis
20.2 Principal Component Analysis
Summary
Multiple-Choice Questions
Review Questions
Chapter 21 Recommendation System
21.1 Cosine Similarity
21.2 Cosine Distance
21.3 Euclidean Distance
21.4 Manhattan Distance
Summary
Multiple-Choice Questions
Review Questions
Chapter 22 Cluster Analysis
22.1 K-Means Clustering
22.2 Agglomerative Hierarchical Clustering
Summary
Multiple-Choice Questions
Review Questions
SECTION 6 Supervised Machine Learning
Chapter 23 Supervised Machine Learning Problems
23.1 Basic Steps of Machine Learning
23.2 Regression
23.3 Classification
Summary
Multiple-Choice Questions
Review Questions
Chapter 24 Supervised Machine Learning Algorithms
24.1 Naive Bayes Algorithm
24.2 k-Nearest Neighbor’s Algorithm
24.3 Support Vector Machines
24.4 Decision Tree
Summary
Multiple-Choice Questions
Review Questions
Chapter 25 Supervised Machine Learning Ensemble Techniques
25.1 Bagging
25.2 Random Forest
25.3 Extra Trees
25.4 Ada Boosting
25.5 Gradient Boosting
Summary
Multiple-Choice Questions
Review Questions
SECTION 7 Machine Learning for Text and Image Data
Chapter 26 Machine Learning for Text Data
26.1 Text Mining
26.2 Sentiment Analysis Using Lexicon-Based Approach
26.3 Similarity Techniques for Text Data
26.4 Cluster Analysis for Text Data
26.5 Supervised Machine Learning for Text Data
Summary
Multiple-Choice Questions
Review Questions
Chapter 27 Machine Learning for Image Data
27.1 Image Acquisition and Preprocessing
27.2 Similarity Techniques for Images
27.3 Cluster Analysis for Images
27.4 Supervised Machine Learning for Images
Summary
Multiple-Choice Questions
Review Questions
SECTION 8 Deep Learning and Transfer Learning
Chapter 28 Basic Neural Network Models
28.1 Steps for Building a Neural Network Model
28.2 Multilayer Perceptrons Model (2-D Tensor)
28.3 Recurrent Neural Network Model (3-D Tensor)
28.4 CNN Model (4-D tensor)
28.4.8 Regularization
28.4.9 Autoencoder as Classifier
28.4.10 Data Augmentation
Summary
Multiple-Choice Questions
Review Questions
Chapter 29 Transfer Learning for Text Data
29.1 Similarity Techniques for Text Data
29.2 Cluster Analysis for Text Data
29.3 Supervised Machine Learning for Text Data
Summary
Multiple-Choice Questions
Review Questions
Chapter 30 Transfer Learning for Image Data
30.1 Similarity Techniques for Images
30.2 Cluster Analysis for Images
30.3 Supervised Machine Learning for Images
References
Summary
Multiple-Choice Questions
Review Questions
Chapter 31 Chatbots
31.1 Understanding Rasa Environment and Executing Default Chatbot
31.2 Creating Basic Chatbot
31.3 Creating Chatbot with Entities and Actions
31.4 Creating Effective Chatbot
Summary
Multiple-Choice Questions
Review Questions
Chapter 32 The Road Ahead
32.1 Reinforcement Learning
32.2 Federated Learning
32.3 Graph Neural Networks (GNNs)
32.4 Generative Adversarial Network (GAN)
Summary
Multiple-Choice Questions
Review Questions
Index