Search icon Close icon
Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Checkout

Change country

Modal Close icon
Country selected Country selected
country flag United States
Country selected
country flag Great Britain
Country selected
country flag India
Country selected
country flag Germany
Country selected
country flag France
Country selected
country flag Canada
Country selected
country flag Russia
Country selected
country flag Spain
Country selected
country flag Brazil
Country selected
country flag Australia
Country selected
country flag Singapore
Country selected
country flag Canary Islands
Country selected
country flag Hungary
Country selected
country flag Ukraine
Country selected
country flag Luxembourg
Country selected
country flag Estonia
Country selected
country flag Lithuania
Country selected
country flag South Korea
Country selected
country flag Turkey
Country selected
country flag Switzerland
Country selected
country flag Colombia
Country selected
country flag Taiwan
Country selected
country flag Chile
Country selected
country flag Norway
Country selected
country flag Ecuador
Country selected
country flag Indonesia
Country selected
country flag New Zealand
Country selected
country flag Cyprus
Country selected
country flag Denmark
Country selected
country flag Finland
Country selected
country flag Poland
Country selected
country flag Malta
Country selected
country flag Czechia
Country selected
country flag Austria
Country selected
country flag Sweden
Country selected
country flag Italy
Country selected
country flag Egypt
Country selected
country flag Belgium
Country selected
country flag Portugal
Country selected
country flag Slovenia
Country selected
country flag Ireland
Country selected
country flag Romania
Country selected
country flag Greece
Country selected
country flag Argentina
Country selected
country flag Netherlands
Country selected
country flag Bulgaria
Country selected
country flag Latvia
Country selected
country flag South Africa
Country selected
country flag Malaysia
Country selected
country flag Japan
Country selected
country flag Slovakia
Country selected
country flag Philippines
Country selected
country flag Mexico
Country selected
country flag Thailand
Country selected
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
Home > Data > Data Science > Mastering Python for Data Science
Mastering Python for Data Science
Mastering Python for Data Science

Mastering Python for Data Science: Explore the world of data science through Python and learn how to make sense of data

Arrow left icon
Profile Icon Samir Madhavan
Arrow right icon
$65.99
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.6 (10 Ratings)
Paperback Aug 2015 294 pages 1st Edition
eBook
$9.99 $51.99
Paperback
$65.99
Subscription
Free Trial
Renews at $19.99p/m
Arrow left icon
Profile Icon Samir Madhavan
Arrow right icon
$65.99
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.6 (10 Ratings)
Paperback Aug 2015 294 pages 1st Edition
eBook
$9.99 $51.99
Paperback
$65.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$9.99 $51.99
Paperback
$65.99
Subscription
Free Trial
Renews at $19.99p/m

What do you get with Print?

Product feature icon Instant access to your digital copy whilst your Print order is Shipped
Product feature icon Paperback book shipped to your preferred address
Product feature icon Redeem a companion digital copy on all Print orders
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want

Contact Details

Modal Close icon
Payment Processing...
tick Completed

Shipping Address

Billing Address

Shipping Methods
Table of content icon View table of contents Preview book icon Preview Book

Mastering Python for Data Science

Chapter 2. Inferential Statistics

Before getting understanding the inferential statistics, let's look at what descriptive statistics is about.

Descriptive statistics is a term given to data analysis that summarizes data in a meaningful way such that patterns emerge from it. It is a simple way to describe data, but it does not help us to reach a conclusion on the hypothesis that we have made. Let's say you have collected the height of 1,000 people living in Hong Kong. The mean of their height would be descriptive statistics, but their mean height does not indicate that it's the average height of whole of Hong Kong. Here, inferential statistics will help us in determining what the average height of whole of Hong Kong would be, which is described in depth in this chapter.

Inferential statistics is all about describing the larger picture of the analysis with a limited set of data and deriving conclusions from it.

In this chapter, we will cover the following topics:

  • The...

Various forms of distribution

There are various kinds of probability distributions, and each distribution shows the probability of different outcomes for a random experiment. In this section, we'll explore the various kinds of probability distributions.

A normal distribution

A normal distribution is the most common and widely used distribution in statistics. It is also called a "bell curve" and "Gaussian curve" after the mathematician Karl Friedrich Gauss. A normal distribution occurs commonly in nature. Let's take the height example we saw previously. If you have data for the height of all the people of a particular gender in Hong Kong city, and you plot a bar chart where each bar represents the number of people at this particular height, then the curve that is obtained will look very similar to the following graph. The numbers in the plot are the standard deviation numbers from the mean, which is zero. The concept will become clearer as we proceed through...

A z-score

A z-score, in simple terms, is a score that expresses the value of a distribution in standard deviation with respect to the mean. Let's take a look at the following formula that calculates the z-score:

A z-score

Here, X is the value in the distribution, µ is the mean of the distribution, and σ is the standard deviation of the distribution

Let's try to understand this concept from the perspective of a school classroom.

A classroom has 60 students in it and they have just got their mathematics examination score. We simulate the score of these 60 students with a normal distribution using the following command:

>>> classscore
>>> classscore = np.random.normal(50, 10, 60).round()

[ 56.  52.  60.  65.  39.  49.  41.  51.  48.  52.  47.  41.  60.  54.  41.
  46.  37.  50.  50.  55.  47.  53.  38.  42.  42.  57.  40.  45.  35.  39.
  67.  56.  35.  45.  47.  52.  48.  53.  53.  50.  61.  60.  57.  53.  56.
  68.  43.  35.  45.  42.  33.  43.  49.  54.  45.  54...

A p-value

A p-value is the probability of rejecting a null-hypothesis when the hypothesis is proven true. The null hypothesis is a statement that says that there is no difference between two measures. If the hypothesis is that people who clock in 4 hours of study everyday score more that 90 marks out of 100. The null hypothesis here would be that there is no relation between the number of hours clocked in and the marks scored.

If the p-value is equal to or less than the significance level (α), then the null hypothesis is inconsistent and it needs to be rejected.

A p-value

Let's understand this concept with an example where the null hypothesis is that it is common for students to score 68 marks in mathematics.

Let's define the significance level at 5%. If the p-value is less than 5%, then the null hypothesis is rejected and it is not common to score 68 marks in mathematics.

Let's get the z-score of 68 marks:

>>> zscore = ( 68 - classscore.mean() ) / classscore.std()
>&gt...

One-tailed and two-tailed tests

The example in the previous section was an instance of a one-tailed test where the null hypothesis is rejected or accepted based on one direction of the normal distribution.

In a two-tailed test, both the tails of the null hypothesis are used to test the hypothesis.

One-tailed and two-tailed tests

In a two-tailed test, when a significance level of 5% is used, then it is distributed equally in the both directions, that is, 2.5% of it in one direction and 2.5% in the other direction.

Let's understand this with an example. The mean score of the mathematics exam at a national level is 60 marks and the standard deviation is 3 marks.

The mean marks of a class are 53. The null hypothesis is that the mean marks of the class are similar to the national average. Let's test this hypothesis by first getting the z-score 60:

>>> zscore = ( 53 - 60 ) / 3.0
>>> zscore
-2.3333333333333335

The p-value would be:

>>> prob = stats.norm.cdf(zscore)
>>> prob

0.0098153286286453336...

Type 1 and Type 2 errors

Type 1 error is a type of error that occurs when there is a rejection of the null hypothesis when it is actually true. This kind of error is also called an error of the first kind and is equivalent to false positives.

Type 1 and Type 2 errors

Let's understand this concept using an example. There is a new drug that is being developed and it needs to be tested on whether it is effective in combating diseases. The null hypothesis is that it is not effective in combating diseases.

The significance level is kept at 5% so that the null hypothesis can be accepted confidently 95% of the time. However, 5% of the time, we'll accept the rejecttion of the hypothesis although it had to be accepted, which means that even though the drug is ineffective, it is assumed to be effective.

The Type 1 error is controlled by controlling the significance level, which is alpha. Alpha is the highest probability to have a Type 1 error. The lower the alpha, the lower will be the Type 1 error.

The Type 2 error...

Various forms of distribution

There are various kinds of probability distributions, and each distribution shows the probability of different outcomes for a random experiment. In this section, we'll explore the various kinds of probability distributions.

A normal distribution

A normal distribution is the most common and widely used distribution in statistics. It is also called a "bell curve" and "Gaussian curve" after the mathematician Karl Friedrich Gauss. A normal distribution occurs commonly in nature. Let's take the height example we saw previously. If you have data for the height of all the people of a particular gender in Hong Kong city, and you plot a bar chart where each bar represents the number of people at this particular height, then the curve that is obtained will look very similar to the following graph. The numbers in the plot are the standard deviation numbers from the mean, which is zero. The concept will become clearer as we proceed through the chapter.

Also, if you take an...

A z-score

A z-score, in simple terms, is a score that expresses the value of a distribution in standard deviation with respect to the mean. Let's take a look at the following formula that calculates the z-score:

Here, X is the value in the distribution, µ is the mean of the distribution, and σ is the standard deviation of the distribution

Let's try to understand this concept from the perspective of a school classroom.

A classroom has 60 students in it and they have just got their mathematics examination score. We simulate the score of these 60 students with a normal distribution using the following command:

>>> classscore
>>> classscore = np.random.normal(50, 10, 60).round()

[ 56.  52.  60.  65.  39.  49.  41.  51.  48.  52.  47.  41.  60.  54.  41.
  46.  37.  50.  50.  55.  47.  53.  38.  42.  42.  57.  40.  45.  35.  39.
  67.  56.  35.  45.  47.  52.  48.  53.  53.  50.  61.  60.  57.  53.  56.
  68.  43.  35.  45.  42.  33.  43.  49.  54.  45.  54.  48.  55.  56.  30...

A p-value

A p-value is the probability of rejecting a null-hypothesis when the hypothesis is proven true. The null hypothesis is a statement that says that there is no difference between two measures. If the hypothesis is that people who clock in 4 hours of study everyday score more that 90 marks out of 100. The null hypothesis here would be that there is no relation between the number of hours clocked in and the marks scored.

If the p-value is equal to or less than the significance level (α), then the null hypothesis is inconsistent and it needs to be rejected.

Let's understand this concept with an example where the null hypothesis is that it is common for students to score 68 marks in mathematics.

Let's define the significance level at 5%. If the p-value is less than 5%, then the null hypothesis is rejected and it is not common to score 68 marks in mathematics.

Let's get the z-score of 68 marks:

>>> zscore = ( 68 - classscore.mean() ) / classscore.std()
>>> zscore
2.283

Now...

One-tailed and two-tailed tests

The example in the previous section was an instance of a one-tailed test where the null hypothesis is rejected or accepted based on one direction of the normal distribution.

In a two-tailed test, both the tails of the null hypothesis are used to test the hypothesis.

In a two-tailed test, when a significance level of 5% is used, then it is distributed equally in the both directions, that is, 2.5% of it in one direction and 2.5% in the other direction.

Let's understand this with an example. The mean score of the mathematics exam at a national level is 60 marks and the standard deviation is 3 marks.

The mean marks of a class are 53. The null hypothesis is that the mean marks of the class are similar to the national average. Let's test this hypothesis by first getting the z-score 60:

>>> zscore = ( 53 - 60 ) / 3.0
>>> zscore
-2.3333333333333335

The p-value would be:

>>> prob = stats.norm.cdf(zscore)
>>> prob

0.0098153286286453336...

Type 1 and Type 2 errors

Type 1 error is a type of error that occurs when there is a rejection of the null hypothesis when it is actually true. This kind of error is also called an error of the first kind and is equivalent to false positives.

Let's understand this concept using an example. There is a new drug that is being developed and it needs to be tested on whether it is effective in combating diseases. The null hypothesis is that it is not effective in combating diseases.

The significance level is kept at 5% so that the null hypothesis can be accepted confidently 95% of the time. However, 5% of the time, we'll accept the rejecttion of the hypothesis although it had to be accepted, which means that even though the drug is ineffective, it is assumed to be effective.

The Type 1 error is controlled by controlling the significance level, which is alpha. Alpha is the highest probability to have a Type 1 error. The lower the alpha, the lower will be the Type 1 error.

The Type 2 error is the kind...

A confidence interval

A confidence interval is a type of interval statistics for a population parameter. The confidence interval helps in determining the interval at which the population mean can be defined.

Let's try to understand this concept by using an example. Let's take the height of every man in Kenya and determine with 95% confidence interval the average of height of Kenyan men at a national level.

Let's take 50 men and their height in centimeters:

>>> height_data = np.array([ 186.0, 180.0, 195.0, 189.0, 191.0, 177.0, 161.0, 177.0, 192.0, 182.0, 185.0, 192.0,
  173.0, 172.0, 191.0, 184.0, 193.0, 182.0, 190.0, 185.0, 181.0, 188.0, 179.0, 188.0,
  170.0, 179.0, 180.0, 189.0, 188.0, 185.0, 170.0, 197.0, 187.0, 182.0, 173.0, 179.0,
  184.0, 177.0, 190.0, 174.0, 203.0, 206.0, 173.0, 169.0, 178.0, 201.0, 198.0, 166.0,
  171.0, 180.0])

Plotting the distribution, it has a normal distribution:

>>> plt.hist(height_data, 30, normed=True)
>>> plt.show()

The mean of the...

Correlation

In statistics, correlation defines the similarity between two random variables. The most commonly used correlation is the Pearson correlation and it is defined by the following:

The preceding formula defines the Pearson correlation as the covariance between X and Y, which is divided by the standard deviation of X and Y, or it can also be defined as the expected mean of the sum of multiplied difference of random variables with respect to the mean divided by the standard deviation of X and Y. Let's understand this with an example. Let's take the mileage and horsepower of various cars and see if there is a relation between the two. This can be achieved using the pearsonr function in the SciPy package:

>>> mpg = [21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 32.4, 30.4,
       33.9, 21.5, 15.5, 15.2, 13.3, 19.2, 27.3, 26.0, 30.4, 15.8, 19.7, 15.0, 21.4]
>>> hp = [110, 110, 93, 110, 175, 105, 245, 62, 95, 123...

Z-test vs T-test

We have already done a few Z-tests before where we validated our null hypothesis.

A T-distribution is similar to a Z-distribution—it is centered at zero and has a basic bell shape, but its shorter and flatter around the center than the Z-distribution.

The T-distributions' standard deviation is usually proportionally larger than the Z, because of which you see the fatter tails on each side.

The t distribution is usually used to analyze the population when the sample is small.

The Z-test is used to compare the population mean against a sample or compare the population mean of two distributions with a sample size greater than 30. An example of a Z-test would be comparing the heights of men from different ethnicity groups.

The T-test is used to compare the population mean against a sample, or compare the population mean of two distributions with a sample size less than 30, and when you don't know the population's standard deviation.

Let's do a T-test on two classes that are given...

Left arrow icon

Page 1 of 14

Right arrow icon

Description

Data science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it. The Python programming language, beyond having conquered the scientific community in the last decade, is now an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving. This comprehensive guide helps you move beyond the hype and transcend the theory by providing you with a hands-on, advanced study of data science. Beginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python. You will move on to deriving inferences from the analysis by performing inferential statistics, and mining data to reveal hidden patterns and trends. You will use the matplot library to create high-end visualizations in Python and uncover the fundamentals of machine learning. Next, you will apply the linear regression technique and also learn to apply the logistic regression technique to your applications, before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods. Finally, you will perform K-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics.

Who is this book for?

If you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed.

What you will learn

  • Manage data and perform linear algebra in Python
  • Derive inferences from the analysis by performing inferential statistics
  • Solve data science problems in Python
  • Create highend visualizations using Python
  • Evaluate and apply the linear regression technique to estimate the relationships among variables.
  • Build recommendation engines with the various collaborative filtering algorithms
  • Apply the ensemble methods to improve your predictions
  • Work with big data technologies to handle data at scale
Estimated delivery fee Deliver to United States

Economy delivery 10 - 13 business days

Free $6.95

Premium delivery 6 - 9 business days

$21.95
(Includes tracking information)

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Aug 31, 2015
Length: 294 pages
Edition : 1st
Language : English
ISBN-13 : 9781784390150
Category :
Data
Languages :
Python
Concepts :
Data Science
Tools :
Pandas

What do you get with Print?

Product feature icon Instant access to your digital copy whilst your Print order is Shipped
Product feature icon Paperback book shipped to your preferred address
Product feature icon Redeem a companion digital copy on all Print orders
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want

Contact Details

Modal Close icon
Payment Processing...
tick Completed

Shipping Address

Billing Address

Shipping Methods
Estimated delivery fee Deliver to United States

Economy delivery 10 - 13 business days

Free $6.95

Premium delivery 6 - 9 business days

$21.95
(Includes tracking information)

Product Details

Publication date : Aug 31, 2015
Length: 294 pages
Edition : 1st
Language : English
ISBN-13 : 9781784390150
Category :
Data
Languages :
Python
Concepts :
Data Science
Tools :
Pandas

Packt Subscriptions

See our plans and pricing
Modal Close icon
$19.99 billed monthly
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Simple pricing, no contract
START FREE TRIAL
BUY NOW
$199.99 billed annually
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just $5 each
Feature tick icon Exclusive print discounts
START FREE TRIAL
BUY NOW
$279.99 billed in 18 months
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just $5 each
Feature tick icon Exclusive print discounts
START FREE TRIAL
BUY NOW

Frequently bought together

Mastering Python for Data Science
Mastering Python for Data Science
Read more
Aug 2015 294 pages
Full star icon 3.6 (10)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $65.99
$9.99 $51.99
$65.99
Python Machine Learning
Python Machine Learning
Read more
Sep 2015 454 pages
Full star icon 4.3 (100)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $48.99
$9.99 $39.99
$48.99
Learning Data Mining with Python
Learning Data Mining with Python
Read more
Jul 2015 344 pages
Full star icon 3.7 (7)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $48.99
$9.99 $39.99
$48.99
Stars icon
Total $ 163.97
Mastering Python for Data Science
$65.99
Python Machine Learning
$48.99
Learning Data Mining with Python
$48.99
Total $ 163.97 Stars icon

Table of Contents

13 Chapters
1. Getting Started with Raw Data Chevron down icon Chevron up icon
1. Getting Started with Raw Data
The world of arrays with NumPy
Empowering data analysis with pandas
Data cleansing
Data operations
Summary
2. Inferential Statistics Chevron down icon Chevron up icon
2. Inferential Statistics
Various forms of distribution
A z-score
A p-value
One-tailed and two-tailed tests
Type 1 and Type 2 errors
A confidence interval
Correlation
Z-test vs T-test
The F distribution
The chi-square distribution
The chi-square test of independence
ANOVA
Summary
3. Finding a Needle in a Haystack Chevron down icon Chevron up icon
3. Finding a Needle in a Haystack
What is data mining?
Presenting an analysis
Studying the Titanic
Summary
4. Making Sense of Data through Advanced Visualization Chevron down icon Chevron up icon
4. Making Sense of Data through Advanced Visualization
Controlling the line properties of a chart
Creating multiple plots
Playing with text
Styling your plots
Box plots
Heatmaps
Scatter plots with histograms
A scatter plot matrix
Area plots
Bubble charts
Hexagon bin plots
Trellis plots
A 3D plot of a surface
Summary
5. Uncovering Machine Learning Chevron down icon Chevron up icon
5. Uncovering Machine Learning
Different types of machine learning
Decision trees
Linear regression
Logistic regression
The naive Bayes classifier
The k-means clustering
Hierarchical clustering
Summary
6. Performing Predictions with a Linear Regression Chevron down icon Chevron up icon
6. Performing Predictions with a Linear Regression
Simple linear regression
Multiple regression
Training and testing a model
Summary
7. Estimating the Likelihood of Events Chevron down icon Chevron up icon
7. Estimating the Likelihood of Events
Logistic regression
Summary
8. Generating Recommendations with Collaborative Filtering Chevron down icon Chevron up icon
8. Generating Recommendations with Collaborative Filtering
Recommendation data
User-based collaborative filtering
Item-based collaborative filtering
Summary
9. Pushing Boundaries with Ensemble Models Chevron down icon Chevron up icon
9. Pushing Boundaries with Ensemble Models
The census income dataset
Decision trees
Random forests
Summary
10. Applying Segmentation with k-means Clustering Chevron down icon Chevron up icon
10. Applying Segmentation with k-means Clustering
The k-means algorithm and its working
The k-means clustering with countries
Clustering the countries
Summary
11. Analyzing Unstructured Data with Text Mining Chevron down icon Chevron up icon
11. Analyzing Unstructured Data with Text Mining
Preprocessing data
Creating a wordcloud
Word and sentence tokenization
Parts of speech tagging
Stemming and lemmatization
The Stanford Named Entity Recognizer
Performing sentiment analysis on world leaders using Twitter
Summary
12. Leveraging Python in the World of Big Data Chevron down icon Chevron up icon
12. Leveraging Python in the World of Big Data
What is Hadoop?
Python MapReduce
File handling with Hadoopy
Pig
Python with Apache Spark
Summary
Index Chevron down icon Chevron up icon
Index

Recommendations for you

Left arrow icon
LLM Engineer's Handbook
LLM Engineer's Handbook
Read more
Oct 2024 522 pages
Full star icon 4.9 (27)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $59.99
$9.99 $47.99
$59.99
Getting Started with Tableau 2018.x
Getting Started with Tableau 2018.x
Read more
Sep 2018 396 pages
Full star icon 4 (3)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $54.99
$9.99 $43.99
$54.99
Semantic Kernel SDK for Intelligent Applications
Semantic Kernel SDK for Intelligent Applications
Read more
May 2025 3hrs 35mins
Video
Video
  • Video Video $9.99
$9.99 $120.99
AI Ecosystem for the Absolute Beginners - Hands-On
AI Ecosystem for the Absolute Beginners - Hands-On
Read more
Jul 2025 5hrs 7mins
Full star icon 2 (1)
Video
Video
  • Video Video $9.99
$9.99 $119.99
Python for Algorithmic Trading Cookbook
Python for Algorithmic Trading Cookbook
Read more
Aug 2024 406 pages
Full star icon 4.3 (20)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $59.99
$9.99 $47.99
$59.99
RAG-Driven Generative AI
RAG-Driven Generative AI
Read more
Sep 2024 338 pages
Full star icon 4.3 (16)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $43.99
$9.99 $35.99
$43.99
Machine Learning with PyTorch and Scikit-Learn
Machine Learning with PyTorch and Scikit-Learn
Read more
Feb 2022 774 pages
Full star icon 4.4 (87)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $54.99
  • Hardcover Hardcover $79.99
$9.99 $43.99
$54.99
$79.99
Building LLM Powered Applications
Building LLM Powered Applications
Read more
May 2024 342 pages
Full star icon 4.2 (21)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $49.99
$9.99 $39.99
$49.99
Python Machine Learning By Example
Python Machine Learning By Example
Read more
Jul 2024 526 pages
Full star icon 4.3 (25)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $45.99
$9.99 $36.99
$45.99
AI Product Manager's Handbook
AI Product Manager's Handbook
Read more
Nov 2024 488 pages
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $39.99
$9.99 $31.99
$39.99
Right arrow icon

Customer reviews

Top Reviews
  • Top Reviews
  • Most Recent
Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.6
(10 Ratings)
5 star 40%
4 star 30%
3 star 0%
2 star 10%
1 star 20%
Filter icon Filter
Top Reviews
  • Top Reviews
  • Most Recent

Filter reviews by

ruben Oct 13, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Hello I would like to recommend this book I like this book because its content is about Python with appliations in Science and has very interesting programs that we can develop using this language. It explains since the beginning to the most interesting projects. in order to apply them.Beginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python.
Amazon Verified review Amazon
Arunkumar S Jan 25, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Good book with right content
Amazon Verified review Amazon
Natester Oct 12, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Madhavan's book has proven useful for some of the projects I'm working on.The first chapter includes a brief primer on Numpy and Pandas--useful for someone that is new to the Python ecosystem, but assuming you are already familiar with those packages, it should be okay to skip to the second chapter. The second chapter includes some Python statistical examples that I have not seen in other texts, but are important when looking at different types of distributions. These distribution examples and explanations are a must-have in my collection of Python recipes. There are also data visualization tweaks that I've not seen in other Data Science + Python texts.The book also provides an intro to some of the canonical machine learning algorithms (Chapter 5). These examples are great for becoming familiarized with some of the ML algorithms out there without being overwhelmed by all the other algorithms out there.If you are looking for a good primer on Data Science with Python, this is a good book. I'm using the book as a reference more than a primer and the book is also useful.
Amazon Verified review Amazon
jamie May 28, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Good
Amazon Verified review Amazon
Jonathan Brett Crawley Oct 09, 2015
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
The pace of the book is quite quick, so you will be up to speed in no time. The book gives a nice introduction to the algorithms used in data science, explained well and backed up with source code examples of how to implement them in the Python language. My only criticism would be that there are a number of grammatical errors in the text but they do not obstruct the reader from understanding the material. Overall a good beginners book for getting to know the world of data science
Amazon Verified review Amazon

People who bought this also bought

Left arrow icon
Causal Inference and Discovery in Python
Causal Inference and Discovery in Python
Read more
May 2023 466 pages
Full star icon 4.5 (47)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $53.99
$9.99 $43.99
$53.99
Generative AI with LangChain
Generative AI with LangChain
Read more
Dec 2023 376 pages
Full star icon 4.1 (33)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $79.99
$9.99 $63.99
$79.99
Modern Generative AI with ChatGPT and OpenAI Models
Modern Generative AI with ChatGPT and OpenAI Models
Read more
May 2023 286 pages
Full star icon 4.1 (30)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $49.99
$9.99 $39.99
$49.99
Deep Learning with TensorFlow and Keras – 3rd edition
Deep Learning with TensorFlow and Keras – 3rd edition
Read more
Oct 2022 698 pages
Full star icon 4.5 (44)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $49.99
$9.99 $39.99
$49.99
Machine Learning Engineering with Python
Machine Learning Engineering with Python
Read more
Aug 2023 462 pages
Full star icon 4.6 (37)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $49.99
$9.99 $39.99
$49.99
Right arrow icon

About the author

Profile icon Samir Madhavan
Samir Madhavan
Samir Madhavan has been working in the field of data science since 2010. He is an industry expert on machine learning and big data. He has also reviewed R Machine Learning Essentials by Packt Publishing. He was part of the ubiquitous Aadhar project of the Unique Identification Authority of India, which is in the process of helping every Indian get a unique number that is similar to a social security number in the United States. He was also the first employee of Flutura Decision Sciences and Analytics and is a part of the core team that has helped scale the number of employees in the company to 50. His company is now recognized as one of the most promising Internet of Things—Decision Sciences companies in the world.
Read more
See other products by Samir Madhavan
Get free access to Packt library with over 7500+ books and video courses for 7 days!
Start Free Trial

FAQs

What is the digital copy I get with my Print order? Chevron down icon Chevron up icon

When you buy any Print edition of our Books, you can redeem (for free) the eBook edition of the Print Book you’ve purchased. This gives you instant access to your book when you make an order via PDF, EPUB or our online Reader experience.

What is the delivery time and cost of print book? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela
What is custom duty/charge? Chevron down icon Chevron up icon

Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order? Chevron down icon Chevron up icon

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela
Modal Close icon
Modal Close icon