PYTHON DATA ANALYTICS: Harnessing the Power of Python for Data Exploration, Analysis, and Visualization (2024)

By NED MUNOZ

"Python Data Analytics" is your comprehensive guide to leveraging the capabilities of Python for robust data exploration and analysis. Whether you're a data enthusiast, a budding data scientist, or a seasoned analyst, this book equips you with the skills and techniques to extract meaningful insights, visualize data effectively, and drive informe

• Language: English
• Publisher: NED MUNOZ
• Release date: Jan 28, 2024
• ISBN: 9783988317858

NED MUNOZ

Ned Munoz, a distinguished data scientist hailing from the vibrant tech community in San Francisco, is known for simplifying the complexities of Python data analytics. With a passion for making data exploration and analysis accessible, Munoz empowers readers to harness the full potential of Python for visualization and actionable insights.

Book preview

Ned Munoz

PYTHON DATA ANALYTICS

Copyright © 2023 by Ned Munoz

All rights reserved. No part of this publication may be reproduced, stored or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise without written permission from the publisher. It is illegal to copy this book, post it to a website, or distribute it by any other means without permission.

First edition

This book was professionally typeset on Reedsy

Find out more at reedsy.com

Contents

1. Introduction

2. An Introduction to Data Science And Data Analytics

3. Types of Data Analytics

4. Data Types and Variables

5. Conditional Statements

6. Data Structures

7. Working with Strings

8. How to Use Files

9. Working with Functions

10. Data Visualization

11. Visualization Tools for the Digital Age

12. An Introduction To Outlier Detection In Python

13. An Introduction To Regression Analysis

14. Classification Algorithm

15. Clustering Algorithms

16. Conclusion

1

Introduction

In the 21st century, the most precious and costly asset is not Gold, Oil, or Diamonds; it’s Data. The realm of knowledge, commonly referred to as Data Science, along with Data Analytics and Machine Learning, is experiencing rapid growth and development. Professionals who possess the ability to navigate this vast ocean of data and effectively organize it for the benefit of a company are highly regarded as valuable assets to any organization. When data is efficiently harnessed, it can yield substantial profits for a business. IBM forecasts that the demand for data scientists in the United States alone will reach 2.7 million job openings by 2020. This underscores the importance of gaining knowledge on how to work with and analyze data.

This book will guide you through various aspects of data analytics. You will gain insights into what data analytics entails and explore diverse techniques for conducting analytics. Additionally, you will acquire a fundamental understanding of Python, an essential programming language for building and customizing different models.

Throughout the book, you will also acquire knowledge about various data visualization methods and tools. Furthermore, the book will provide you with insights into various algorithms that can be employed for performing data analytics.

2

An Introduction to Data Science And Data Analytics

Let’s quickly recap the key concepts we’ve covered about Data Science in the initial book of this series.

What is Data Science Exactly?

For more than a decade, there has been an ongoing effort to define Data Science accurately. In 2010, Hugh Conway introduced a Venn diagram comprising three circles that provide a comprehensive understanding of Data Science. These three circles represent the following areas of expertise:

Mathematics and Statistics

Subject Knowledge (pertaining to the domain under observation)

Hacking skills

The intersection of these three circles forms the zone that truly encapsulates the field of Data Science. If an individual possesses proficiency in all three of these skills, it can be concluded that they excel in Data Science.

Data Science is a process involving the sorting, cleaning, organization, and analysis of vast amounts of data to determine its potential utility. Data is sourced from various outlets, and data scientists collect it from these sources, applying various external factors such as predictive analysis, machine learning, sentiment analysis, and more to extract critical information. Subsequently, data scientists aim to comprehend this extracted data from a business perspective, translating it into accurate insights and predictions that can inform decision-making for the given business.

What Knowledge is Essential for a Data Scientist?

Ideally, anyone aspiring to establish a career in Data Science should possess proficiency in skills and tools that are advantageous in three core domains:

Domain expertise

Analytics

Programming

This serves as a broad categorization of the requirements for a data scientist. Delving deeper, the following skills are considered essential for individuals pursuing a career as data scientists:

Proficiency in programming languages like Scala, R, Python, and SAS

Competency and hands-on experience with SQL databases

Ability to collect and organize data from unstructured and disorganized sources such as digital and social media

Familiarity with various analytical functions

Knowledge and curiosity regarding machine learning

Who is a Data Analyst?

A data analyst can be defined as an individual who can provide basic and descriptive statistics, visualize and interpret data, and convert it into data points for drawing conclusions. It is expected that a data analyst possesses an understanding of statistics, has good knowledge of databases, can create new perspectives, and possesses the perceptiveness required for data visualization. Data analytics is considered the foundational aspect of data science, which is more extensive and evolved.

Skills Required to Become a Data Analyst

What do we anticipate from a data analyst? It is crucial for a data analyst to have the ability to take a specific topic or question and present raw data in a format that is easily comprehensible to stakeholders within a company. The four key skills listed below are vital for those embarking on the path to becoming a data analyst:

Profound knowledge of mathematical statistics

Proficiency in programming languages like R and Python

Understanding of PIG/HIVE

Data Wrangling

Do Data Analytics and Data Science Overlap?

Data science encompasses smaller subsets, including data mining, data analytics, machine learning, and related disciplines. A data analyst primarily extracts data from all available sources, whereas a data scientist possesses the skills to understand patterns within the extracted data, forecast behaviors based on historical patterns in datasets, and formulate queries. A data analyst, on the other hand, assists in finding answers to those queries.

Understanding Machine Learning

Machine learning can be described as the practice of developing and implementing algorithms to utilize available data, learn from it, and predict future trends within a given subject. Machine learning traditionally involves statistical and predictive analysis to identify concealed insights within collected data. Let’s simplify this concept with an example.

Consider how machine learning is applied on Facebook, the world’s most popular social media platform. Facebook employs machine learning algorithms that continuously analyze a user’s behavior on the platform. Based on past behavior, the algorithm discerns the user’s interests by examining their previous likes, followed pages, and predicts other relevant articles of similar interest to display on their news feed. This is akin to Amazon, where user purchases prompt Amazon’s algorithms to suggest related products. Another example is Netflix, which recommends movies based on a user’s past viewing preferences.

Skills Required to Become a Machine Learning Expert

Machine learning is essentially a digital approach to statistics. To forge a career in machine learning, the following skills are considered crucial:

Fundamental computer knowledge

Proficiency in programming

Proficiency in probability and statistics

Evaluation skills and data modeling

Do Machine Learning and Data Science Converge?

As previously established, data science is a broader field encompassing various disciplines, including software engineering, machine learning, data analysis, data engineering, business analytics, predictive analysis, and more. Data science encompasses the processes of data extraction, collection, analysis, and transformation, often involving Big Data. Data science