Best data science books 2022
This is the best data science books 2021. At first glance, you might dismiss data science as being far too hard to learn, but it’s like anything else – break it down into smaller parts and you’ll find it much easier to grasp. That’s what I’ve done here – broken the subject down into each of its realms, to give you a better idea of what it is all about.
In this book, you will learn:•What data science is•How data science relates to and differs from artificial intelligence and machine learning•Math and statistics•Descriptive and inferential analysis•What data engineering is•What data visualization is•The different types of visualization•An introduction to Seaborn•What machine learning is and how it relates to data science•Different types of machine learning•Different ML algorithms•The steps required for successful machine learning along the way you will find plenty of practical examples and we finish off with a series of questions you might be asked at a data science interview, along with detailed answers, and a glossary of terms.
In this phenomenal bundle, Jason Callaway has condensed everything you need to know in a simple and clear way, with practical examples, detailed explanations, tips and tricks from his experience. His revolutionary approach will speed up your learning, allowing you to master the Python language and its powerful applications in an extremely short time, even if you are a complete beginner.
Here is just a tiny fraction of what you will learn:
✓ The basics of Python programming
✓ Variables, data types, basic and advanced operations
✓ Essential Python libraries such as NumPy, Pandas, Matplotlib
✓ The most up-to-date computational methods for data analysis
✓ Data visualization tools and techniques
✓ Real-world Python programming applications: machine learning and the future of artificial intelligence
✓ How to build neural networks with Python
✓ Step-by-step exercises, practical examples, tips and tricks
You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager.
Here you will learn:
PART 1 - GETTING STARTED WITH DATA SCIENCE
PART 2 - FINDING YOUR DATA SCIENCE JOB
PART 3 - SETTLING INTO DATA SCIENCE
PART 4 - GROWING IN YOUR DATA SCIENCE ROLE
A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence
"Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality -- the study of cause and effect -- on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence.
An accessible fast-paced introduction to all aspects of Power BI for new or aspiring BI professionals, data analysts, and data visualizers
- Updated with the latest features in Power BI including Dataflow, AI insights, visuals and row-level security
- Get faster and more intuitive data insights using Microsoft Power BI and its business intelligence capabilities
- Build accurate analytical models, reports, and dashboards
DataStory teaches you the most effective ways to turn your data into narratives that blend the power of language, numbers, and graphics. This book is not about visualizing data, there are plenty of books covering that. Instead, you’ll learn how to transform numbers into narratives to drive action.
- It will help you communicate data in a way that creates outcomes both inside and outside your own organization.
- It will help you earn a reputation as a trusted advisor, which will advance your career.
- it will help your organization make faster decisions and inspire others to act on them!
A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges.
The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges.
In Data Science for Business, renowned data scientists Foster Provost and Tom Fawcett introduce the fundamental concepts of data science. And walk you through the data-analytic thinking necessary for extracting useful knowledge and business value from your collected data. This guide also makes you understand the different data-mining techniques are being used today.
What you'll learn:
- How data science fits in your organization
- Ways to use it for competitive advantage
- Why data is treated as a business asset that requires careful investment if you’re to gain real value
- How to approach business problems data-analytically
- Using the data-mining process to gather valuable data in the most appropriate way
- Essential concepts for extracting knowledge from data
- How to Apply data science principles when interviewing data science job candidates
Data science Experts use many tools such as libraries, frameworks, modules, and toolkits for doing data science. You can also use them to dive into the discipline without actually understanding data science. Data Science from Scratch teaches you how many of the most basic data science tools and algorithms operate by implementing them from scratch.
What you'll learn:
- A basic course in Python
- Fundamentals of linear algebra, statistics, and probability-- and how and when they're used in data science
- How to explore, collect, clean, and manipulate data to extract meaningful value
- Essential basics of machine learning
- How to Implement these models-- k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering
- Recommender systems and natural language processing technologies
- Network analysis, MapReduce, and databases
R is a very effective language to turn raw data into insight, knowledge, and understanding. R for Data Science introduces you to the collection of R packages including R, RStudio, and the tiny verse designed to work together to make data science fast, fluent, and fun. No previous programming experience is required, this book is devised to get you doing data science as quickly as possible.
What You’ll learn:
- How to transform your datasets into a form convenient for analysis
- Implementing powerful R tools for solving data problems with greater clarity and ease
- analyze your data, generate hypotheses, and quickly test them
- How to implement a low-dimensional summary that captures true "signals" in your dataset
- R Markdown for integrating prose, code, and results
For those who think Statistics is something boring and useless, this book is a surprise. With the wit, accessibility, and absolute fun, Charles Wheelan challenges the odds yet again by bringing another crucial, formerly unglamorous discipline to life. Wheelan removes the arcane and technical details and focuses on the underlying intuition that drives statistical analysis.
What you'll learn:
- Key concepts such as inference, correlation, and regression analysis
- How biased or careless parties can manipulate or misrepresent data
- The way how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions
- Catching schools that cheat on standardized tests
- How does Netflix know which movies you’ll like?
- What is causing the rising incidence of autism?
How can you exactly do data science for your business? Do you need to hire one of the experts of this dark art, the "data scientist," to extract the gold from your data? Absolutely not.
Data science is little more than using simple steps to process raw data into actionable insight. And in Data Smart, you will learn how that's done within the familiar environment of a spreadsheet.
What you'll learn:
- Everything you ever need to know about spreadsheets
- Using K models to segment your customer base
- How to optimize modeling
- Network graphs and community detection
- How to ensemble models
- Outlier detection
- Moving from Spreadsheets into R
Statistics will be the next sexy job--Hal Varian, Googles' chief economists
- We've all heard it but what does this statement mean?
- Why do we suddenly care about statistics and about data?
This report explores the many sides of data science -- the technologies, the companies, and the unique skill sets everything you need to understand the hot topic of this decade.
- What is the importance of context and audience is
- Determining the type of graph that fits best for your situation
- How to identify and reduce the clutter clouding your information
- Most effective ways to direct your audience's attention to the most important parts of your data
- Acting as a designer and utilizing concepts of design in data visualization
- How the power of storytelling makes your message resonate with your audience
Numerous jobs in data science out there, but few people have skills needed to fill these increasingly important roles. Data Science For Dummies is the ideal starting point for students and IT professionals who want a quick guideline covering all areas of the widespread data science space. Focusing on business cases, the book teaches you topics in big data, data science, data engineering and how you can combine these three areas to generate tremendous value.
What you'll learn:
- Fundamentals of big data and data engineering
- Implementation of big data frameworks and applications like Spark, Hadoop, MPP platforms, MapReduce, and NoSQL.
- Machine learning and many of its algorithms
- Artificial intelligence and the evolution of the Internet of Things.
- Detailed data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate.
Due to a bunch of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who have a little knowledge about this technology can use simple, effective tools to write programs capable of learning from data. This practical book shows you how to do these miracles.
What you'll learn:
- Understanding the machine learning landscape, especially neural nets
- Using scikit-learning, an example machine-learning project end-to-end
- Various training models, including decision trees, random forests, support vector machines, and ensemble methods
- How to use the TensorFlow library to develop and train neural nets
- Different neural net architectures, including recurrent nets, convolutional nets, and deep reinforcement learning
- Effective techniques for training and scaling deep neural nets
- How to write practical code examples without acquiring excessive theory or algorithm details on machine learning