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Statistics and Probability Refresher, and Python Practice

In this chapter, we are going to go through a few concepts of statistics and probability, which might be a refresher for some of you. These concepts are important to go through if you want to be a data scientist. We will see examples to understand these concepts better. We will also look at how to implement those examples using actual Python code.

We'll be covering the following topics in this chapter:

Types of data you may encounter and how to treat them accordingly
Statistical concepts of mean, median, mode, standard deviation, and variance
Probability density functions and probability mass functions
Types of data distributions and how to plot them
Understanding percentiles and moments

Key benefits

• Take your first steps in the world of data science by understanding the tools and techniques of data analysis

• Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods

• Learn how to use Apache Spark for processing Big Data efficiently

Description

Join Frank Kane, who worked on Amazon and IMDb’s machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank’s successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis.

Who is this book for?

If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don't need to be an expert Python coder or mathematician to get the most from this book.

What you will learn

• Learn how to clean your data and ready it for analysis

• Implement the popular clustering and regression methods in Python

• Train efficient machine learning models using decision trees and random forests

• Visualize the results of your analysis using Python's Matplotlib library

• Use Apache Spark's MLlib package to perform machine learning on large datasets

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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National Hunter Jul 13, 2020

Excellent, but complicated book in data science

Amazon Verified review

RSG Aug 08, 2017

Very well written and easy to follow. The jupyter notebook code files available on the publisher's site make it very easy to work alongside the author as he presents the material. Arguably, one of the best introductory books on the subject if you want to dive right in with only minimal programming experience.

SwedishMike Jan 01, 2018

If you are interested in getting started with Data Science and Machine Learning you could do much worse than picking up a copy of this book.It takes you through the basics, through basic Python, Statistics and such, and shows you the ropes. The tone in the book is nice and chatty without losing focus of the task at hand.You won't walk away with every single piece of maths and algorithm etched into your brain but for some of us that's good. Getting a good grounding before going into all the gory details is my preferred way of learning and I would assume that there are more people like me out there too.Another good point is that most of the examples are 'real world' examples - there are still some parts which are made up of randomized numbers but in those cases it doesn't matter much. I've read some books on the subjects here were almost every example is made up of random numbers but I do personally learn much better if the numbers and examples are real - it also makes it easier to see the outcome of changes in the code/approach as you play around.All in all - a good introduction to get you started in these fields.

Steve Kaplan Aug 06, 2021

One of the worst books on programming I've read. Not to even mention it reads like it was written by a high school student and has never been proof-read, there are numerous typos and blatant discrepancies in describing the output of code. I had to get this textbook for a college class, but if you can I highly recommend finding another book.

Hands-On Data Science and Python Machine Learning: Perform data mining and machine learning efficiently using Python and Spark

What do you get with Print?

Hands-On Data Science and Python Machine Learning

Statistics and Probability Refresher, and Python Practice

Types of data

Mean, median, and mode

Mean

Using mean, median, and mode in Python

Calculating mean using the NumPy package

Standard deviation and variance

Variance

Probability density function and probability mass function

The probability density function and probability mass functions

Types of data distributions

Uniform distribution

Percentiles and moments

Percentiles

Summary

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