Principles of Data Science : A Beginner's Guide to Essential Math and Coding Skills for Data Fluency and Machine Learning / Sinan Ozdemir.

Format:

Book

Author/Creator:

Ozdemir, Sinan, author.

Language:

English

Subjects (All):

Data mining.

Quantitative research.

Physical Description:

1 online resource (326 pages)

Edition:

Third edition.

Place of Publication:

Birmingham, England : Packt Publishing, [2024]

Biography/History:

Ozdemir Sinan: Sinan is an active lecturer focusing on large language models and a former lecturer of data science at the Johns Hopkins University. He is the author of multiple textbooks on data science and machine learning including "Quick Start Guide to LLMs". Sinan is currently the founder of LoopGenius which uses AI to help people and businesses boost their sales and was previously the founder of the acquired Kylie. ai, an enterprise-grade conversational AI platform with RPA capabilities. He holds a Master's Degree in Pure Mathematics from Johns Hopkins University and is based in San Francisco.

Summary:

Transform your data into insights with must-know techniques and mathematical concepts to unravel the secrets hidden within your data Key Features Learn practical data science combined with data theory to gain maximum insights from data Discover methods for deploying actionable machine learning pipelines while mitigating biases in data and models Explore actionable case studies to put your new skills to use immediately Purchase of the print or Kindle book includes a free PDF eBook Book Description Principles of Data Science bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines. This book will equip you with the tools to transform abstract concepts and raw statistics into actionable insights. Starting with cleaning and preparation, you'll explore effective data mining strategies and techniques before moving on to building a holistic picture of how every piece of the data science puzzle fits together. Throughout the book, you'll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data. With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You'll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift. Finally, you'll explore medium-level data governance, including data provenance, privacy, and deletion request handling. By the end of this data science book, you'll have learned the fundamentals of computational mathematics and statistics, all while navigating the intricacies of modern ML and large pre-trained models like GPT and BERT. What you will learn Master the fundamentals steps of data science through practical examples Bridge the gap between math and programming using advanced statistics and ML Harness probability, calculus, and models for effective data control Explore transformative modern ML with large language models Evaluate ML success with impactful metrics and MLOps Create compelling visuals that convey actionable insights Quantify and mitigate biases in data and ML models Who this book is for If you are an aspiring novice data scientist eager to expand your knowledge, this book is for you. Whether you have basic math skills and want to apply them in the field of data science, or you excel in programming but lack the necessary mathematical foundations, you'll find this book useful. Familiarity with Python programming will further enhance your learning experience.

Contents:

Intro

Title Page

Copyright and Credits

Dedication

Contributor

Table of Contents

Preface

Chapter 1: Data Science Terminology

What is data science?

Understanding basic data science terminology

Why data science?

Example - predicting COVID-19 with machine learning

The data science Venn diagram

The math

Computer programming

Example - parsing a single tweet

Domain knowledge

Some more terminology

Data science case studies

Case study - automating government paper pushing

Case study - what's in a job description?

Summary

Chapter 2: Types of Data

Structured versus unstructured data

Quantitative versus qualitative data

Digging deeper

The four levels of data

The nominal level

Measures of center

The ordinal level

The interval level

The ratio level

Data is in the eye of the beholder

Questions and answers

Chapter 3: The Five Steps of Data Science

Introduction to data science

Overview of the five steps

Exploring the data

Guiding questions for data exploration

DataFrames

Series

Exploration tips for qualitative data

Chapter 4: Basic Mathematics

Basic symbols and terminology

Vectors and matrices

Arithmetic symbols

Summation

Logarithms/exponents

Set theory

Linear algebra

Matrix multiplication

How to multiply matrices together

Chapter 5: Impossible or Improbable - A Gentle Introduction to Probability

Basic definitions

What do we mean by "probability"?

Bayesian versus frequentist

Frequentist approach

The law of large numbers

Compound events

Conditional probability

How to utilize the rules of probability

The addition rule

Mutual exclusivity

The multiplication rule

Independence

Complementary events

Introduction to binary classifiers

Summary.

Chapter 6: Advanced Probability

Bayesian ideas revisited

Bayes' theorem

More applications of Bayes' theorem

Random variables

Discrete random variables

Continuous random variables

Chapter 7: What Are the Chances? An Introduction to Statistics

What are statistics?

How do we obtain and sample data?

Obtaining data

Observational

Experimental

Sampling data

How do we measure statistics?

Measures of variation

The coefficient of variation

Measures of relative standing

The insightful part - correlations in data

The empirical rule

Example - exam scores

Chapter 8: Advanced Statistics

Understanding point estimates

Sampling distributions

Confidence intervals

Hypothesis tests

Conducting a hypothesis test

One-sample t-tests

Type I and Type II errors

Hypothesis testing for categorical variables

Chi-square goodness of fit test

Chi-square test for association/independence

Chapter 9: Communicating Data

Why does communication matter?

Identifying effective visualizations

Scatter plots

Line graphs

Bar charts

Histograms

Box plots

When graphs and statistics lie

Correlation versus causation

Simpson's paradox

If correlation doesn't imply causation, then what does?

Verbal communication

It's about telling a story

On the more formal side of things

The why/how/what strategy for presenting

Chapter 10: How to Tell if Your Toaster is Learning - Machine Learning Essentials

Introducing ML

Example - facial recognition

ML isn't perfect

How does ML work?

Types of ML

SL

UL

RL

Overview of the types of ML

ML paradigms - pros and cons

Predicting continuous variables with linear regression

Causation.

Adding more predictors

Regression metrics

Chapter 11: Predictions Don't Grow on Trees, or Do They?

Performing naïve Bayes classification

Classification metrics

Understanding decision trees

Measuring purity

Exploring the Titanic dataset

Dummy variables

Diving deep into UL

When to use UL

k-means clustering

The Silhouette Coefficient

Feature extraction and PCA

Chapter 12: Introduction to Transfer Learning and Pre-Trained Models

Understanding pre-trained models

Benefits of using pre-trained models

Commonly used pre-trained models

Decoding BERT's pre-training

TL

Different types of TL

Inductive TL

Transductive TL

Unsupervised TL - feature extraction

TL with BERT and GPT

Examples of TL

Example - Fine-tuning a pre-trained model for text classification

Chapter 13: Mitigating Algorithmic Bias and Tackling Model and Data Drift

Understanding algorithmic bias

Types of bias

Sources of algorithmic bias

Measuring bias

Consequences of unaddressed bias and the importance of fairness

Mitigating algorithmic bias

Mitigation during data preprocessing

Mitigation during model in-processing

Mitigation during model postprocessing

Bias in LLMs

Uncovering bias in GPT-2

Emerging techniques in bias and fairness in ML

Understanding model drift and decay

Model drift

Data drift

Mitigating drift

Understanding the context

Continuous monitoring

Regular model retraining

Implementing feedback systems

Model adaptation techniques

Chapter 14: AI Governance

Mastering data governance

Current hurdles in data governance

Data management: crafting the bedrock

Data ingestion - the gateway to information

Data integration - from collection to delivery

Data warehouses and entity resolution.

The quest for data quality

Documentation and cataloging - the unsung heroes of governance

Understanding the path of data

Regulatory compliance and audit preparedness

Change management and impact analysis

Upholding data quality

Troubleshooting and analysis

Navigating the intricacy and the anatomy of ML governance

ML governance pillars

Model interpretability

The many facets of ML development

Beyond training - model deployment and monitoring

A guide to architectural governance

The five pillars of architectural governance

Transformative architectural principles

Zooming in on architectural dimensions

Chapter 15: Navigating Real-World Data Science Case Studies in Action

Introduction to the COMPAS dataset case study

Understanding the task/outlining success

Preliminary data exploration

Preparing the data for modeling

Final thoughts

Text embeddings using pretrainedmodels and OpenAI

Setting up and importing necessary libraries

Data collection - fetching the textbook data

Converting text to embeddings

Querying - searching for relevant information

Concluding thoughts - the power of modern pre-trained models

Index

About Packt

Other Books You May Enjoy.

Notes:

Includes index.

Description based on print version record.

ISBN:

9781837636006

1837636001

OCLC:

1420910296