
Premium
Title Page
1/7/2025
Copyright Page
1/7/2025
Dedication Page
1/7/2025
About the Author
1/7/2025
About the Reviewer
1/7/2025
Acknowledgement
1/7/2025
Preface
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Table of Contents
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1. Introduction to Data Science
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Introduction
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Structure
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Objectives
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Data science objectives
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Evolution of data science
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Role of data science in various domains
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Stages of a data science project
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Data security issues
1/7/2025
Data science vs. data analytics vs. machine learning vs. artificial intelligence
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Career in data science
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Steps to install Anaconda and Python
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Conclusion
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Multiple choice questions
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Answers
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Questions
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2. NumPy
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Introduction to NumPy
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Importance of NumPy in data science
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NumPy basics
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Creating NumPy arrays
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From Python lists and tuples
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Creating arrays with specific values
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Creating arrays with a range of values
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Creating random arrays
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Creating empty and uninitialized arrays
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Creating arrays with patterns
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NumPy array attributes
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NumPy array operations
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Arithmetic operations
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Mathematical functions
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Aggregation functions
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Array manipulation functions
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NaN values
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Logical operations
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Sorting and searching
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Linear algebra operations
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Universal functions
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Key features of ufuncs
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Advantages of using ufuncs
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Vectorized operations
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Broadcasting
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Rules of broadcasting
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Indexing, slicing, and iterating NumPy arrays
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Indexing
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Slicing
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Iterating
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Boolean indexing and conditional filtering in NumPy
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Boolean indexing
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Conditional filtering
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Example with 2D arrays
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Fancy indexing in NumPy
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Using integer arrays for indexing
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Advanced indexing techniques
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Reshaping arrays in NumPy
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Changing dimensions
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Adding and removing dimensions
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Combining and splitting NumPy arrays
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Random numbers and simulations in NumPy
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Generating random numbers
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Input and output in NumPy
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Binary data handling
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Performance and optimization in NumPy
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Programming exercises
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3. Pandas
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Key features of Pandas
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Benefits of Pandas
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Pandas basics
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Series
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Key features of a series
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DataFrame
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Creating a DataFrame
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Common operations on DataFrames
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Combining multiple DataFrames
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Reshaping DataFrames
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4. Data Collection and Data Preprocessing
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Types of data
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Structured vs. unstructured data
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Structured data
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Unstructured data
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Key differences between structured and unstructured data
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Data collection
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Datasets
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Based on source and availability
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Based on data type
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Based on domain
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Based on machine learning task
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Real-world examples by domain
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Data formats
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Benefits of data format types
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Data parsing
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Types of data parsers
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Types of data parsing
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Data parser use cases
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Data transformation
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Steps in data transformation
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Example of data transformation
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Real-time issues in data transformation
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5. Data Cleaning
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Data consistency
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Causes of data inconsistency
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Importance of data consistency
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Methods to ensure data consistency
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Data consistency issues
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Heterogeneous data
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Challenges of heterogeneous data
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How to handle heterogeneous data
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Missing data
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Types of missing data
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Causes of missing data
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Handling missing data
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Types of data transformation
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Data segmentation
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Types of data segmentation
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Data transformation vs. data segmentation
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6. Exploratory Data Analysis
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Descriptive statistics
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Measures of central tendency
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Measures of dispersion
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Statistical tools
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Use of descriptive statistics
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Comparative statistics
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Role of t-test
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Use of comparative statistics
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Descriptive statistics vs. comparative statistics
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Clustering
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K-means clustering
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Example
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Hierarchical clustering
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Density-based clustering
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Uses of clustering
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Association
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Apriori algorithm
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Frequent Pattern Growth
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FP Growth algorithm steps
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Uses of association rule mining
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Clustering vs. association
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Hypothesis generation
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Steps in hypothesis generation
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Examples of hypotheses
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7. Data Visualization
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Principles of data visualization
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Types of data visualization
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Basic charts and graphs
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Advanced visualizations
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Interactive dashboards
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Tools for data visualization
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Python libraries for visualizations
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Business intelligence tools
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Geospatial tools
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Web-based visualization frameworks
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Feature selection
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Time series analysis
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Key characteristics of time series data
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Components of time series
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Steps in time series analysis
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Applications of time series analysis
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Geolocated data analysis
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Key concepts in geolocated analysis
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Tools for geolocated analysis
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Steps in performing geolocated analysis
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Applications of geolocated analysis
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Correlations and connections
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Importance of correlation in data analysis
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Visualization of correlations
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Connections
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Networks and hierarchies
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Key features of networks
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Tools for network analysis
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Hierarchies
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Key features of hierarchies
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Interactivity
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Key aspects of interactivity
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Tools for interactive data science
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Benefits of interactivity
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8. Projects
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Movie recommender system
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MovieLens dataset
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Customer support chatbot
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Customer segmentation system
1/7/2025