Analytics In A Big Data World By B. Baesens Free Download Pdf

5 Free Books to Learn Statistics for Data Science

Learn all the statistics y'all need for data science for costless

Statistics is a key skill that data scientists use every day. It is the branch of mathematics that allows us to collect, describe, translate, visualise, and make inferences nigh data. Data scientists will apply it for information analysis, experiment pattern, and statistical modelling.

Statistics is too essential for auto learning. We wil fifty use statistics to understand the data prior to training a model. When we have samples of data for grooming and testing our models we need to employ statistical techniques to ensure fairness. When evaluating the functioning of a model we need statistics to assess the variability of the predictions and assess accuracy.

"If statistics are boring, y'all've got the wrong numbers.", Edward Tufte

These are just some of the ways in which statistics are employed by data scientists. If y'all are studying data scientific discipline it is therefore essential to develop a practiced agreement of these statistical techniques.

This is ane area where books can be a especially useful study tool every bit detailed explanations of statistical concepts is essential to your understanding.

Hither are my top 5 costless books for learning statistics for data science.

Practical Statistics for Data Scientists

by Peter Bruce and Andrew Bruce

Read for gratis here .

Primary topics covered:

Data structures.
Descriptive statistics.
Probability.
Auto learning.

Suitable for: Complete beginners.

Statistics is a very broad field, and only part of it is relevant to information scientific discipline. This book is extremely good at only covering the areas related to information science. And then if y'all are looking for a volume that will quickly requite you just enough agreement to be able to practice data science then this book is definitely the one to choose.

Information technology is filled with a lot of applied coded examples (written in R), gives very articulate explanations for any statistical terms used and likewise links out to other resources for further reading.

This is overall an excellent book to cover off the nuts and is suitable for an absolute beginner to the field.

Think Stats

by Allen B. Downey

Read for complimentary here.

Principal topics covered:

Statistical thinking.
Distributions.
Hypothesis testing.
Correlation.

Suitable for: Beginners with basic Python.

The introduction for this book states that "this book is well-nigh turning cognition into data" and it does a very good job of introducing statistical concepts through practical examples of data analysis.

"this book is about turning knowledge into data"

It is another volume that covers only the concepts directly related to data science and also contains lots of code examples, this time written in Python. It is aimed heavily at programmers and relies on using that skill to sympathise the key statistical concepts introduced. This book is therefore ideally suited to those who already have at least a basic grasp of Python.

Bayesian Methods for Hackers

by Cameron Davidson-Pilon

Read for gratis hither.

Chief topics covered:

Bayesian inference.
Loss functions.
Bayesian machine learning.
Priors.

Suitable for: Non-statisticians with a working knowledge of Python.

Bayesian inference is a branch of statistics that deals with understanding uncertainty. As a data scientist dubiety is something you volition need to model on a very regular basis. If you are building a auto learning model, for example, you lot will demand to be able to sympathise the uncertainty effectually the predictions that your model is delivering.

Bayesian methods can exist quite abstruse and difficult to understand. This book aimed firmly at programmers (so some Python is a prerequisite), is the only material I have plant that explains these concepts in a unproblematic plenty manner for a not-statistician to understand. At that place are coded examples throughout and the Github repository, where the chapters are hosted, contains a large pick of notebooks. It is, therefore, an excellent hands-on introduction to this subject.

Statistics in Plain English

past Timothy C. Urdan

Read for free hither.

Main topics covered:

Regression.
Distributions.
Factor analysis.
Probability.

Suitable for: Non-statisticians with any level of programming experience.

This book covers general statistical techniques rather than just those aimed at data scientists or programmers. It is however written in a very straight forward style and covers a wide range and depth of statistical concepts in a very simple to understand style.

The book was originally written for students studying a non-mathematics based form where an agreement of statistics is required, such as the social sciences. It, therefore, covers enough theory to sympathise the techniques merely doesn't assume an existing mathematical background. It is, therefore, an ideal volume to read if y'all are coming into information science without a math-based degree.

Computer Age Statistical Inference

by Bradley Efron and Trevor Hastie

Read for free here.

Chief topics covered:

Bayesian and frequentist inference.
Large calibration hypothesis testing.
Machine learning.
Deep learning.

Suitable for: Someone with a basic agreement of statistics and statistical note. No programming required.

This volume covers the theory behind most of the pop auto learning algorithms used past data scientists today. It also gives a thorough introduction to both Bayesian and Frequentist statistical inference methodologies.

The second half of the volume, which covers automobile learning algorithms, is some of the best material I have seen on this bailiwick. Each explanation is in-depth and uses practical examples such as the nomenclature of spam information which makes quite circuitous ideas easier to digest. The volume is nigh suited to those who have already covered the basics of statistics for data analysis and are familiar with some statistical annotation.

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