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    Data Analytics with Python: Data Analytics in Python Using Pandas
    Data Analytics with Python: Data Analytics in Python Using Pandas
    Data Analytics with Python: Data Analytics in Python Using Pandas
    Ebook74 pages1 hour

    Data Analytics with Python: Data Analytics in Python Using Pandas

    By Frank Millstein

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    About this ebook

    Data is the foundation of this digital age that we live in. With this book, you are going to learn how to organize and analyze data and how to interpret vast sources of information. This book covers various topics on data analytics such as data analytics applications, data analytics process, using Python for data analytics, Python libraries for data analytics and many other that will help you kick-start your data analytics journey from the very beginning.

    In this book you are going to learn how to use Python its tools in order to interpret data and examine those interesting data trends and information, which are important in predicting the future. Whether you are dealing with some medical data, sales data, web page data, you can use Python in order to interpret data, analyze it and obtain this valuable information.

    You can also use this data for creating data analytics models and predictions.

    Here Is A Brief Preview of What You'll Learn In This Book…

    • Data analytics applications
    • Data analytics process
    • How to install and run Python
    • Python data structures and Python libraries
    • Python conditional construct and iteration
    • Data exploration using Pandas
    • Pandas series and dataframes
    • Data munging and distribution analysis
    • Carrying out binary operations
    • Data manipulation and categorical variable analysis
    • How to build a predictive model
    • And of course much, much more!

    Get this book NOW and learn more about Data Analytics With Python!

    LanguageEnglish
    PublisherFrank Millstein
    Release dateOct 18, 2019
    ISBN9781393614500
    Data Analytics with Python: Data Analytics in Python Using Pandas

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      Book preview

      Data Analytics with Python - Frank Millstein

      Frank Millstein

      WHAT IS IN THE BOOK?

      INTRODUCTION

      WHAT IS DATA ANALYTICS?

      THE DIFFERENCE BETWEEN DATA SCIENCE, BIG DATA AND DATA ANALYTICS

      DATA ANALYTICS APPLICATIONS

      DATA ANALYTICS PROCESS

      CHAPTER 1: PYTHON BASICS FOR DATA ANALYTICS

      INSTALLING PYTHON

      RUNNING PYTHON

      PYTHON DATA STRUCTURES

      PYTHON ITERATION AND CONDITIONAL CONSTRUCTS

      PYTHON LIBRARIES

      CHAPTER 2: EXPLORATORY DATA ANALYSIS USING PANDAS

      PANDAS SERIES AND DATAFRAMES

      IMPORTING LIBRARIES

      OBJECT CREATION

      CREATING SERIES AND DATAFRAME

      PANDAS BASIC FUNCTIONALITY

      CARRYING OUT BINARY OPERATIONS

      CHAPTER 3: DISTRIBUTION ANALYSIS

      CATEGORICAL VARIABLE ANALYSIS

      DATA MANIPULATION

      CHAPTER 4: DATA MUNGING

      CHECKING MISSING VALUES

      FILLING MISSING VALUES

      TREATING EXTREME VALUES IN DISTRIBUTION

      CHAPTER 5: BUILDING A PREDICTIVE MODEL

      LOGISTIC REGRESSION

      DECISION TREE

      RANDOM FOREST

      LAST WORDS

      Copyright © 2018 by Frank Millstein- All rights reserved.

      This document is geared towards providing exact and reliable information in regards to the topic and issue covered. The publication is sold with the idea that the publisher is not required to render accounting, officially permitted, or otherwise, qualified services. If advice is necessary, legal or professional, a practiced individual in the profession should be ordered.

      From a Declaration of Principles which was accepted and approved equally by a Committee of the American Bar Association and a Committee of Publishers and Associations.

      In no way is it legal to reproduce, duplicate, or transmit any part of this document by either electronic means or in printed format. Recording of this publication is strictly prohibited, and any storage of this document is not allowed unless with written permission from the publisher. All rights reserved.

      The information provided herein is stated to be truthful and consistent, in that any liability, in terms of inattention or otherwise, by any usage or abuse of any policies, processes, or directions contained within is the solitary and utter responsibility of the recipient reader. Under no circumstances will any legal responsibility or blame be held against the publisher for any reparation, damages, or monetary loss due to the information herein, either directly or indirectly.

      Respective authors own all copyrights not held by the publisher.

      The information herein is offered for informational purposes solely and is universal as so. The presentation of the information is without contract or any type of guarantee assurance.

      The trademarks that are used are without any consent, and the publication of the trademark is without permission or backing by the trademark owner. All trademarks and brands within this book are for clarifying purposes only and are owned by the owners themselves, not affiliated with this document.

      INTRODUCTION

      Today, data is literally everywhere. In fact, the amount of digital data present, is growing at a very rapid rate. More than 2.7 zettabytes of data are present in today’s digital world, and its growth will reach around 180 zettabytes of data in several years.

      All this data, from text, photos, and other sources have only recently begun to be closely analyzed to offer companies and organizations valuable insights which can help improve their business flow. Since data is so valuable, more and more companies and organizations are seeking experts and professionals who can help them give access to it for utilization.

      These days, once you get the art of data analytics right, it is very easy to become a data scientist as it is just a matter of practicing your skills enough to become proficient. Once you have perfected the art of data analytics, you can offer your skills to those who need help of a data scientist.

      Data is growing at rapid rates than ever before. For instance, each person creates 1.7 megabytes of data every second. In fact, this trend is growing. Thus, people create more and more data daily.

      Data science is a broad field, which comprises everything related to data cleansing, data preparation, and data analysis. On the other hand, big data is something which can be used to analyze the valuable, important data insights that can easily lead to better business decisions and better strategic business moves.

      Data analysis or data analytics involves automating those data insights into specific datasets as well as understands the data usage of various data aggregation procedures and queries.

      Data science algorithms are commonly used in digital advertisements, search recommendations and Internet searches while big data is used in industries such as retail, financial services, and communication while data analytics is commonly present in industries like gaming, travel, healthcare and energy management.

      WHAT IS DATA ANALYTICS?

      Data analytics or simply DA is the process of examining data to draw conclusions about the information the data contains, increasingly with the aid of specialized software and systems. Various data analytics techniques and technologies are regularly used in different commercial industries to enable companies and organizations to make better and more informed business decisions. Data scientists and researchers who disprove or verify scientific models, hypothesis and theories perform data

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