Data Science Programming All in One For Dummies 1st edition by John Paul Mueller, Luca Massaron ISBN 1119626145 9781119626145

Book 1: Defining Data Science

Chapter 1: Considering the History and Uses of Data Science

Considering the Elements of Data Science

Defining the Role of Data in the World

Creating the Data Science Pipeline

Comparing Different Languages Used for Data Science

Learning to Perform Data Science Tasks Fast

Chapter 2: Placing Data Science within the Realm of AI

Seeing the Data to Data Science Relationship

Defining the Levels of AI

Creating a Pipeline from Data to AI

Chapter 3: Creating a Data Science Lab of Your Own

Considering the Analysis Platform Options

Choosing a Development Language

Obtaining and Using Python

Obtaining and Using R

Presenting Frameworks

Accessing the Downloadable Code

Chapter 4: Considering Additional Packages and Libraries You Might Want

Considering the Uses for Third-Party Code

Obtaining Useful Python Packages

Locating Useful R Libraries

Chapter 5: Leveraging a Deep Learning Framework

Understanding Deep Learning Framework Usage

Working with Low-End Frameworks

Understanding TensorFlow

Book 2: Interacting with Data Storage

Chapter 1: Manipulating Raw Data

Defining the Data Sources

Considering the Data Forms

Understanding the Need for Data Reliability

Chapter 2: Using Functional Programming Techniques

Defining Functional Programming

Understanding Pure and Impure Languages

Comparing the Functional Paradigm

Using Python for Functional Programming Needs

Understanding How Functional Data Works

Working with Lists and Strings

Employing Pattern Matching

Working with Recursion

Performing Functional Data Manipulation

Chapter 3: Working with Scalars, Vectors, and Matrices

Considering the Data Forms

Defining Data Type through Scalars

Creating Organized Data with Vectors

Creating and Using Matrices

Extending Analysis to Tensors

Using Vectorization Effectively

Selecting and Shaping Data

Working with Trees

Representing Relations in a Graph

Chapter 4: Accessing Data in Files

Understanding Flat File Data Sources

Working with Positional Data Files

Accessing Data in CSV Files

Moving On to XML Files

Considering Other Flat-File Data Sources

Working with Nontext Data

Downloading Online Datasets

Chapter 5: Working with a Relational DBMS

Considering RDBMS Issues

Accessing the RDBMS Data

Creating a Dataset

Mixing RDBMS Products

Chapter 6: Working with a NoSQL DMBS

Considering the Ramifications of Hierarchical Data

Accessing the Data

Interacting with Data from NoSQL Databases

Working with Dictionaries

Developing Datasets from Hierarchical Data

Processing Hierarchical Data into Other Forms

Book 3: Manipulating Data Using Basic Algorithms

Chapter 1: Working with Linear Regression

Considering the History of Linear Regression

Combining Variables

Manipulating Categorical Variables

Using Linear Regression to Guess Numbers

Learning One Example at a Time

Chapter 2: Moving Forward with Logistic Regression

Considering the History of Logistic Regression

Differentiating between Linear and Logistic Regression

Using Logistic Regression to Guess Classes

Switching to Probabilities

Working through Multiclass Regression

Chapter 3: Predicting Outcomes Using Bayes

Understanding Bayes’ Theorem

Using Naïve Bayes for Predictions

Working with Networked Bayes

Considering the Use of Bayesian Linear Regression

Considering the Use of Bayesian Logistic Regression

Chapter 4: Learning with K-Nearest Neighbors

Considering the History of K-Nearest Neighbors

Learning Lazily with K-Nearest Neighbors

Leveraging the Correct k Parameter

Implementing KNN Regression

Implementing KNN Classification

Book 4: Performing Advanced Data Manipulation

Chapter 1: Leveraging Ensembles of Learners

Leveraging Decision Trees

Working with Almost Random Guesses

Meeting Again with Gradient Descent

Averaging Different Predictors

Chapter 2: Building Deep Learning Models

Discovering the Incredible Perceptron

Hitting Complexity with Neural Networks

Understanding More about Neural Networks

Looking Under the Hood of Neural Networks

Explaining Deep Learning Differences with Other Forms of AI

Chapter 3: Recognizing Images with CNNs

Beginning with Simple Image Recognition

Understanding CNN Image Basics

Moving to CNNs with Character Recognition

Explaining How Convolutions Work

Detecting Edges and Shapes from Images

Chapter 4: Processing Text and Other Sequences

Introducing Natural Language Processing

Understanding How Machines Read

Understanding Semantics Using Word Embeddings

Using Scoring and Classification

Book 5: Performing Data-Related Tasks

Chapter 1: Making Recommendations

Realizing the Recommendation Revolution

Downloading Rating Data

Leveraging SVD

Chapter 2: Performing Complex Classifications

Using Image Classification Challenges

Distinguishing Traffic Signs

Chapter 3: Identifying Objects

Distinguishing Classification Tasks

Perceiving Objects in Their Surroundings

Overcoming Adversarial Attacks on Deep Learning Applications

Chapter 4: Analyzing Music and Video

Learning to Imitate Art and Life

Mimicking an Artist

Moving toward GANs

Chapter 5: Considering Other Task Types

Processing Language in Texts

Processing Time Series

Chapter 6: Developing Impressive Charts and Plots

Starting a Graph, Chart, or Plot

Setting the Axis, Ticks, and Grids

Defining the Line Appearance

Using Labels, Annotations, and Legends

Creating Scatterplots

Plotting Time Series

Plotting Geographical Data

Visualizing Graphs

Book 6: Diagnosing and Fixing Errors

Chapter 1: Locating Errors in Your Data

Considering the Types of Data Errors

Obtaining the Required Data

Validating Your Data

Manicuring the Data

Dealing with Dates in Your Data

Chapter 2: Considering Outrageous Outcomes

Deciding What Outrageous Means

Considering the Five Mistruths in Data

Considering Detection of Outliers

Examining a Simple Univariate Method

Developing a Multivariate Approach

Chapter 3: Dealing with Model Overfitting and Underfitting

Understanding the Causes

Determining the Sources of Overfitting and Underfitting

Guessing the Right Features

Chapter 4: Obtaining the Correct Output Presentation

Considering the Meaning of Correct

Determining a Presentation Type

Choosing the Right Graph

Working with External Data

Chapter 5: Developing Consistent Strategies

Standardizing Data Collection Techniques

Using Reliable Sources

Verifying Dynamic Data Sources

Looking for New Data Collection Trends

Weeding Old Data

Considering the Need for Randomness

Index

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