Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Principles of Data Science
  • Table Of Contents Toc
Principles of Data Science

Principles of Data Science - Second Edition

By : Sinan Ozdemir, Kakade, Tibaldeschi
Buy this Book
close
close
Principles of Data Science

Principles of Data Science

By: Sinan Ozdemir, Kakade, Tibaldeschi
Buy this Book

Overview of this book

Need to turn programming skills into effective data science skills? This book helps you connect mathematics, programming, and business analysis. You’ll feel confident asking—and answering—complex, sophisticated questions of your data, making abstract and raw statistics into actionable ideas. Going through the data science pipeline, you'll clean and prepare data and learn effective data mining strategies and techniques to gain a comprehensive view of how the data science puzzle fits together. You’ll learn fundamentals of computational mathematics and statistics and pseudo-code used by data scientists and analysts. You’ll learn machine learning, discovering statistical models that help control and navigate even the densest datasets, and learn powerful visualizations that communicate what your data means.
Table of Contents (17 chapters)
close
close
close
Preface
Icon
Preface
Icon
Who this book is for
Icon
What this book covers
Icon
To get the most out of this book
Icon
Get in touch
Lock Free Chapter
1
1. How to Sound Like a Data Scientist
Icon
1. How to Sound Like a Data Scientist
Icon
What is data science?
Icon
The data science Venn diagram
Icon
Why Python?
Icon
Some more terminology
Icon
Data science case studies
Icon
Summary
2
2. Types of Data
Icon
2. Types of Data
Icon
Flavors of data
Icon
Why look at these distinctions?
Icon
Structured versus unstructured data
Icon
Quantitative versus qualitative data
Icon
The road thus far
Icon
The four levels of data
Icon
Data is in the eye of the beholder
Icon
Summary
3
3. The Five Steps of Data Science
Icon
3. The Five Steps of Data Science
Icon
Introduction to data science
Icon
Overview of the five steps
Icon
Exploring the data
Icon
Summary
4
4. Basic Mathematics
Icon
4. Basic Mathematics
Icon
Mathematics as a discipline
Icon
Basic symbols and terminology
Icon
Linear algebra
Icon
Summary
5
5. Impossible or Improbable - A Gentle Introduction to Probability
chevron up
Icon
5. Impossible or Improbable - A Gentle Introduction to Probability
Icon
Basic definitions
Icon
Probability
Icon
Bayesian versus Frequentist
Icon
Compound events
Icon
Conditional probability
Icon
The rules of probability
Icon
A bit deeper
Icon
Summary
6
6. Advanced Probability
Icon
6. Advanced Probability
Icon
Collectively exhaustive events
Icon
Bayesian ideas revisited
Icon
Random variables
Icon
Summary
7
7. Basic Statistics
Icon
7. Basic Statistics
Icon
What are statistics?
Icon
How do we obtain and sample data?
8
8. Advanced Statistics
Icon
8. Advanced Statistics
Icon
Point estimates
Icon
Sampling distributions
Icon
Confidence intervals
Icon
Hypothesis tests
Icon
Summary
9
9. Communicating Data
Icon
9. Communicating Data
Icon
Why does communication matter?
Icon
Identifying effective and ineffective visualizations
Icon
When graphs and statistics lie
Icon
Verbal communication
Icon
The why/how/what strategy of presenting
Icon
Summary
10
10. How to Tell If Your Toaster Is Learning – Machine Learning Essentials
Icon
10. How to Tell If Your Toaster Is Learning – Machine Learning Essentials
Icon
What is machine learning?
Icon
Machine learning isn't perfect
Icon
How does machine learning work?
Icon
Types of machine learning
Icon
How does statistical modeling fit into all of this?
Icon
Linear regression
Icon
Logistic regression
Icon
Probability, odds, and log odds
Icon
Dummy variables
Icon
Summary
11
11. Predictions Don't Grow on Trees - or Do They?
Icon
11. Predictions Don't Grow on Trees - or Do They?
Icon
Naive Bayes classification
Icon
Decision trees
Icon
Unsupervised learning
Icon
k-means clustering
Icon
Choosing an optimal number for K and cluster validation
Icon
Summary
12
12. Beyond the Essentials
Icon
12. Beyond the Essentials
Icon
The bias/variance trade-off
Icon
K folds cross-validation
Icon
Grid searching
Icon
Ensembling techniques
Icon
Neural networks
Icon
Summary
13
13. Case Studies
Icon
13. Case Studies
Icon
Case study 1 – Predicting stock prices based on social media
Icon
Case study 2 – Why do some people cheat on their spouses?
Icon
Case study 3 – Using TensorFlow
Icon
Summary
14
14. Building Machine Learning Models with Azure Databricks and Azure Machine Learning service
Icon
14. Building Machine Learning Models with Azure Databricks and Azure Machine Learning service
Icon
Technical requirements
Icon
Technologies for machine learning projects
Icon
Configuring Azure Databricks
Icon
Training a text classifier with Azure Databricks
Icon
Azure Machine Learning
Icon
Summary
15
Other Books You May Enjoy
Icon
Other Books You May Enjoy
Icon
Leave a review – let other readers know what you think
16
Index
Icon
Index

Conditional probability

Let's pick an arbitrary person from this study of 100 people. Let's also assume that you are told that their test result was positive. What is the probability of them actually having cancer? So, we are told that event B has already taken place, and that their test came back positive. The question now is: what is the probability that they have cancer, that is, P(A)? This is called a conditional probability of A given B or P(A|B). Effectively, it is asking you to calculate the probability of an event given that another event has already happened.

You can think of conditional probability as changing the relevant universe. P(A|B) (called the probability of A given B) is a way of saying, given that my entire universe is now B, what is the probability of A? This is also known as transforming the sample space.

Zooming in on our previous diagram, our universe is now B, and we are concerned with AB (A and B) inside B.

The formula can be given as follows:

P(A|B) = P...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Principles of Data Science
End of Section 5
Next Section
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options
font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon