Read e-book online Mastering Python for Data Science PDF

By Samir Madhavan

ISBN-10: 1784390151

ISBN-13: 9781784390150

Approximately This Book

grasp info technology tools utilizing Python and its libraries
Create facts visualizations and mine for patterns
complex thoughts for the 4 basics of information technology with Python - information mining, information research, facts visualization, and desktop learning

Who This e-book Is For

If you're a Python developer who desires to grasp the area of knowledge technological know-how then this e-book is for you. a few wisdom of knowledge technology is assumed.
What you are going to Learn

deal with info and practice linear algebra in Python
Derive inferences from the research by means of appearing inferential statistics
resolve info technology difficulties in Python
Create high-end visualizations utilizing Python
review and follow the linear regression strategy to estimate the relationships between variables.
construct advice engines with a few of the collaborative filtering algorithms
follow the ensemble tips on how to enhance your predictions
paintings with immense facts applied sciences to address information at scale

In Detail

Data technological know-how is a comparatively new wisdom area that is utilized by a variety of enterprises to make info pushed judgements. info scientists need to put on a variety of hats to paintings with info and to derive price from it. The Python programming language, past having conquered the clinical neighborhood within the final decade, is now an necessary device for the knowledge technological know-how practitioner and a must-know instrument for each aspiring information scientist. utilizing Python will provide you with a quick, trustworthy, cross-platform, and mature atmosphere for information research, computing device studying, and algorithmic challenge solving.

This accomplished advisor is helping you progress past the hype and go beyond the idea via supplying you with a hands-on, complex examine of information science.

Beginning with the necessities of Python in information technology, you are going to discover ways to deal with information and practice linear algebra in Python. you'll circulate directly to deriving inferences from the research by way of acting inferential data, and mining information to bare hidden styles and tendencies. you'll use the matplot library to create high-end visualizations in Python and discover the basics of desktop studying. subsequent, you are going to follow the linear regression process and in addition learn how to practice the logistic regression strategy to your purposes, sooner than growing suggestion engines with quite a few collaborative filtering algorithms and bettering your predictions by way of utilizing the ensemble methods.

Finally, you'll practice K-means clustering, in addition to an research of unstructured facts with various textual content mining options and leveraging the ability of Python in giant information analytics.
Style and approach

This publication is an easy-to-follow, finished advisor on info technological know-how utilizing Python. the subjects coated within the booklet can all be utilized in genuine international eventualities.

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Additional resources for Mastering Python for Data Science

Example text

53. 50. 61. 60. 57. 68. 43. 35. 56. ] 45. 42. 33. 43. 49. 54. 45. 54. 48. 55. The NumPy package has a random module that has a normal function, where 50 is given as the mean of the distribution, 10 is the standard deviation of the distribution, and 60 is the number of values to be generated. 334. To make more sense of the z-score, we'll use the standard normal table. This table helps in determining the probability of a score. We would like to know what the probability of getting a score above 60 would be.

Aggregate(sum) Here, the aggregate method is utilized. The sum function is passed to obtain the required results. It's also possible to obtain multiple kinds of aggregations on the same metric. This can be achieved by the following command: >>> df['NO. 905591 Summary In this chapter, we got familiarized with the NumPy and pandas packages. We understood the different datatypes in pandas and how to utilize them. We learned how to perform data cleansing and manipulation, in which we handled missing values and performed string operations.

For example, the Point of Sale system at a retail shop might have malfunctioned and inputted some data with missing values. We'll be learning how to handle such data in the following section. [ 12 ] Chapter 1 Checking the missing data Generally, most data will have some missing values. There could be various reasons for this: the source system which collects the data might not have collected the values or the values may never have existed. Once you have the data loaded, it is essential to check the missing elements in the data.

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Mastering Python for Data Science by Samir Madhavan

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