Download or read book Colorful Statistics with Basic Steps in Python Programming Language written by Rehan Zafar Paracha and published by . This book was released on 2019-10-20 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a try to make statistics colorful along with basic instructions in Python Programming Language ...It has handy illustrations on a huge number of statistical topics with beautiful colors as can be found through the table of contents.

Download or read book Lite Statistics with Basic Steps in Python Programming Language written by Usman Zafar Paracha and published by . This book was released on 2020-06-07 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a try to make statistics easy-to-understand along with basic instructions in Python Programming Language ...The statistics-related topics can be found in the table of contents.N.B. It has lower price as compared to "Colorful Statistics with Basic Steps in Python (Programming Language)", but almost similar information as that book.

Download or read book Python for Data Science For Dummies written by John Paul Mueller and published by John Wiley & Sons. This book was released on 2019-01-25 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fast and easy way to learn Python programming and statistics Python is a general-purpose programming language created in the late 1980s—and named after Monty Python—that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library. Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud. Get started with data science and Python Visualize information Wrangle data Learn from data The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.

Download or read book Python for Data Analysis written by Matt Foster and published by . This book was released on 2020-01-05 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master the Python Programming Language and Data Analysis With This Comprehensive Guide! If you would like to... Grow your business Get an amazing job Make great business decisions Get rid of the competition... This book will teach you how to achieve all that with the help of data analysis and data science. It might sound like a lot of work, but with proper guidance, you don't need to spend hours bent over textbooks and trying to make sense of a huge amount of information. The goal of this book is not only to learn about data analysis but to go from this theoretical to practical knowledge and application. In other words, you'll be able to complete your own analysis, implement its methods in your business, and master the Python Programming Language! Here's what you'll learn with this book: The importance of data analysis and why every successful business and industry are using it How to process data with tools and techniques used by data scientists The concepts behind Python programming How to use the "data munging" process How to use Python libraries such as Pandas and NumPy for data analysis The importance of data visualization How to create the right analytical algorithm for predicting the market trends How to write codes, and create programs and databases And much more! Even if this is the first time you're hearing about Data Analysis and Python, you can still successfully learn everything this book offers. The instructions are incredibly simple, the methods explained to the finest details and the guides are presented in a step-by-step way. You don't have to be a computer or math expert to develop this skill. You simply need a straightforward guide on the steps you have to take, with clear background explanations to help you understand those steps. If you want to modernize your company and your skills, make the most of your data and become a competitive force on the market, Scroll up, click on "Buy Now with 1-Click", and Get Your Copy Now!

Download or read book Statistics for Machine Learning written by Himanshu Singh and published by BPB Publications. This book was released on 2021-01-15 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical guide that will help you understand the Statistical Foundations of any Machine Learning Problem Ê KEY FEATURESÊ _ Develop a Conceptual and Mathematical understanding of Statistics _ Get an overview of Statistical Applications in Python _ Learn how to perform Hypothesis testing in Statistics _ Understand why Statistics is important in Machine Learning _ Learn how to process data in Python Ê DESCRIPTIONÊÊ This book talks about Statistical concepts in detail, with its applications in Python. The book starts with an introduction to Statistics and moves on to cover some basic Descriptive Statistics concepts such as mean, median, mode, etc.Ê You will then explore the concept of Probability and look at different types of Probability Distributions. Next, you will look at parameter estimations for the unknown parameters present in the population and look at Random Variables in detail, which are used to save the results of an experiment in Statistics. You will then explore one of the most important fields in Statistics - Hypothesis Testing, and then explore various types of tests used to check our hypothesis. The last part of our book will focus on how you can process data using Python, some elements of Non-parametric statistics, and finally, some introduction to Machine Learning. Ê WHAT YOU WILLÊ LEARNÊÊ _ Understand the basics of Statistics _ Get to know more about Descriptive Statistics _ Understand and learn advanced Statistics techniques _ Learn how to apply Statistical concepts in Python _ Understand important Python packages for Statistics and Machine Learning Ê WHO THIS BOOK IS FORÊ This book is for anyone who wants to understand Statistics and its use in Machine Learning. This book will help you understand the Mathematics behind the Statistical concepts and the applications using the Python language. Having a working knowledge of the Python language is a prerequisite. TABLE OF CONTENTSÊ 1. Introduction to Statistics 2. Descriptive Statistics 3. Probability 4. Random Variables 5. Parameter Estimations 6. Hypothesis Testing 7. Analysis of Variance 8. Regression 9. Non Parametric Statistics 10. Data Analysis using Python 11. Introduction to Machine Learning

Download or read book Python Programming for Data Analysis written by José Unpingco and published by Springer Nature. This book was released on 2021-05-04 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively, especially for data analysis applications. Certain low-level language features are discussed in detail, especially Python memory management and data structures. Using Python effectively means taking advantage of its vast ecosystem. The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules. The section on object-oriented programming explains features of the language that facilitate common programming patterns. After developing the key Python language features, the book moves on to third-party modules that are foundational for effective data analysis, starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then, the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis, key modules such as Matplotlib are developed in detail, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly. The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes, which is especially helpful for newcomers to both Python and data analysis. To get the most out of this book, open a Python interpreter and type along with the many code samples.

Download or read book Python for Probability Statistics and Machine Learning written by José Unpingco and published by Springer. This book was released on 2019-06-29 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms. As with the prior edition, there are new and updated *Programming Tips* that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy. Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy, Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels, and Keras. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.

Download or read book Python for Data Analysis written by Andrew Park and published by Andrew Park. This book was released on 2021-04-22 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: ★ 55% OFF for Bookstores! NOW at $41,97 instead of $51,97!Do you want to learn more about Data Analysis and how to master it with Python?Your Customers Will Love This Amazing Guide! Everyone talks about data today. You have probably come across the term "data" more times than you can remember in one day. Data as a concept is so wide. One thing that is true about data is that it can be used to tell a story. The story could be anything from explaining an event to predicting the future. Data is the future. Businesses, governments, organizations, criminals-everyone needs data for some reason. Entities are investing in different data approaches to help them understand their current situation, and use it to prepare for the unknown. The world of technology as we know it is evolving towards an open-source platform where people share ideas freely. This is seen as the first step towards the decentralization of ideas and eliminating unnecessary monopolies. Therefore, the data, tools, and techniques used in the analysis are easily available for anyone to interpret data sets and get relevant explanations. With Python for Data Analysis you will learn about the main steps that are needed to correctly implement Data Analysis and the procedures to help you extract the right insights from the right data. Some of the topics that we will discuss inside include: What Data Analysis is all about and why businesses are investing in this sector The 5 steps of a Data Analysis Pandas, Jupyter and PyTorch The 7 Python libraries that make Python one of the best choices for Data Analysis Neural Network How Data Visualization and Matplotlib can help you to understand the data you are working with. Some of the main industries that are using data to improve their business with 14 real-world applications And Much More! While most books focus on how to implement advanced predictive models, this book takes the time to explain the basic concepts and all the necessary steps to correctly implement Data Analysis, including Data Visualization and providing practical examples and simple coding scripts. Don't miss the opportunity to learn more about these topics. Even if you never used Data Analysis, learning it is easier than it looks, you just need the right guidance. This practical guide provides all the knowledge you need in a simple and practical way. Regardless of your previous experience, you will learn the steps of Data Analysis, how to implement them in Python, and the most important real-world applications. Would You Like To Know More? Buy it NOW and Let Your Customers Get Addicted to This Amazing Book!

Download or read book Python for Data Analysis written by Brady Ellison and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Ready to learn Data Science through Python language? Python for Data Analysis is a step-by-step guide for beginners and dabblers-alike. This book is designed to offer working knowledge of Python and data science and some of the tools required to apply that knowledge. It’s possible that you have little experience with or knowledge of data analysis and are interested in it. You might have some experience in coding. You may have worked with data before and want to use Python. We have made this book in a way that will be helpful to all these groups and more besides in varying ways. This can serve as an introduction to the most current tools and functions of those tools used by data scientists. In this book You will learn: Data Science/Analysis and its applications IPython and Jupyter - an introduction to the basic tools and how to navigate and use them. You will also learn about its importance in a data scientist’s ecosystem. Pandas - a powerful data management Python library that lets you do interesting things with data. You will learn all the basics you need to get started. NumPy - a powerful numerical library for Python. You will learn more about its advantages. Get your copy now

Download or read book Python for Data Analysis written by Wes McKinney and published by "O'Reilly Media, Inc.". This book was released on 2017-09-25 with total page 676 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

Download or read book Data Analysis from Scratch with Python written by Peters Morgan and published by Createspace Independent Publishing Platform. This book was released on 2017-08-18 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: ***** BUY NOW (will soon return to 24.99 $)******Free eBook for customers who purchase the print book from Amazon****** Are you thinking of becoming a data analyst using Python? (For Beginners) If you are looking for a complete guide to data analysis using Python language and its library that will help you to become an effective data scientist, this book is for you. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses.To get the most out of the concepts that would be covered, readers are advised to adopt hands on approach, which would lead to better mental representations. Step By Step Guide and Visual Illustrations and Examples The Book give complete instructions for manipulating, processing, cleaning, modeling and crunching datasets in Python. This is a hands-on guide with practical case studies of data analysis problems effectively. You will learn pandas, NumPy, IPython, and Jupiter in the Process. Target Users This book is a practical introduction to data science tools in Python. It is ideal for analyst's beginners to Python and for Python programmers new to data science and computer science. Instead of tough math formulas, this book contains several graphs and images. What's Inside This Book? Introduction Why Choose Python for Data Science & Machine Learning Prerequisites & Reminders Python Quick Review Overview & Objectives A Quick Example Getting & Processing Data Data Visualization Supervised & Unsupervised Learning Regression Simple Linear Regression Multiple Linear Regression Decision Tree Random Forest Classification Logistic Regression K-Nearest Neighbors Decision Tree Classification Random Forest Classification Clustering Goals & Uses of Clustering K-Means Clustering Anomaly Detection Association Rule Learning Explanation Apriori Reinforcement Learning What is Reinforcement Learning Comparison with Supervised & Unsupervised Learning Applying Reinforcement Learning Neural Networks An Idea of How the Brain Works Potential & Constraints Here's an Example Natural Language Processing Analyzing Words & Sentiments Using NLTK Model Selection & Improving Performance Sources & References Frequently Asked Questions Q: Is this book for me and do I need programming experience?A: if you want to smash Python for data analysis, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK.Q: Does this book include everything I need to become a data science expert?A: Unfortunately, no. This book is designed for readers taking their first steps in data analysis and further learning will be required beyond this book to master all aspects.Q: Can I have a refund if this book is not fitted for me?A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at [email protected].

Download or read book Data Science from Scratch with Python written by Peters Morgan and published by Createspace Independent Publishing Platform. This book was released on 2018-07-03 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: ***** BUY NOW (Will soon return to 35.59) ******Free eBook for customers who purchase the print book from Amazon****** Are you thinking of learning data science from scratch using Python? If you are looking for a complete step by step guide to data science using Python from scratch, this book is for you.After his great success with his first book "Data Analysis from Scratch with Python", Peters Morgan publish his second book focusing now in data science and machine learning. It is considered by practitioners as the easiest guide ever written in this domain. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. If you need to see the quality of our job, AI Sciences Company offering you a free eBook in Machine Learning with Python written by Alain Kaufmann at https://aisciences.lpages.co/ai-science-l1/.To get the most out of the concepts that would be covered, readers are advised to adopt hands on approach, which would lead to better mental representations. Step By Step Guide and Visual Illustrations and Examples The Book give complete instructions for manipulating, processing, cleaning, modeling and crunching datasets in Python. This is a hands-on guide with practical case studies of data analysis problems effectively. You will learn pandas, NumPy, IPython, and Jupiter in the Process. Target Users Target UsersThe most suitable users would include: Beginners who want to approach data science, but are too afraid of complex math to start Newbies in computer science techniques and data science Professionals in data science and social sciences Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way Students and academicians, especially those focusing on data science What's Inside This Book? Part 1: Data Science Fundamentals, Concepts and Algorithms Introduction Statistics Probability Bayes' Theorem and Naïve Bayes Algorithm Asking the Right Question Data Acquisition Data Preparation Data Exploration Data Modelling Data Presentation Supervised Learning Algorithms Unsupervised Learning Algorithms Semi-supervised Learning Algorithms Reinforcement Learning Algorithms Overfitting and Underfitting Correctness The Bias-Variance Trade-off Feature Extraction and Selection Part 2: Data Science in Practice Overview of Python Programming Language Python Data Science Tools Jupyter Notebook Numerical Python (Numpy) Pandas Scientific Python (Scipy) Matplotlib Scikit-Learn K-Nearest Neighbors Naive Bayes Simple and Multiple Linear Regression Logistic Regression GLM models Decision Trees and Random forest Perceptrons Backpropagation Clustering Natural Language Processing Frequently Asked Questions Q: Is this book for me and do I need programming experience?A: if you want to smash Python for data science and machine learning, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK.Q: Can I have a refund if this book is not fitted for me?A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at [email protected].

Download or read book Python for Kids written by StoryBuddiesPlay and published by StoryBuddiesPlay. This book was released on 2024-05-04 with total page 59 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unleash the Power of Python: A Comprehensive Guide for Beginners and Beyond Are you ready to unlock the potential of one of the world's most versatile programming languages? This comprehensive guide takes you on a journey from Python's beginner-friendly fundamentals to advanced concepts that empower you to tackle complex projects. Master the Essentials: Solid Foundation: Grasp core programming principles like variables, data types, operators, and control flow. Build a strong understanding of the syntax and structure of Python code. Problem-Solving Prowess: Learn how to break down problems into logical steps and translate them into efficient Python solutions using loops, conditional statements, and functions. Data Structures & Algorithms: Explore essential data structures like lists, tuples, dictionaries, and sets. Discover powerful algorithms for sorting, searching, and manipulating data effectively. Embrace Advanced Techniques: Object-Oriented Programming (OOP): Delve into the world of classes and objects, the building blocks for creating modular and maintainable code. Master the concepts of inheritance, polymorphism, and encapsulation. Modules & Libraries: Discover the vast ecosystem of Python modules and libraries that extend its capabilities. Leverage tools like NumPy for scientific computing, Pandas for data analysis, Matplotlib for visualizations, and Tkinter for building graphical user interfaces. File Handling & Data Persistence: Learn how to read, write, and manipulate data in various file formats like text files, CSV files, and even images. Explore data serialization techniques for storing complex data structures. Build Exhilarating Projects: Game Development: Craft your own interactive games, from simple text-based adventures to visually stunning graphical experiences. Implement game mechanics like turn-based combat, item collection, and level progression. Data Analysis & Visualization: Harness the power of Python to analyze large datasets, extract insights, and create compelling visualizations. Gain valuable skills in data cleaning, manipulation, and storytelling with data. Web Development: Dive into the world of web development with Python frameworks like Django or Flask. Build dynamic web applications that interact with databases and provide user-friendly interfaces. This is just the beginning! As you progress through this guide, you'll gain the confidence and knowledge to tackle increasingly challenging projects and explore the ever-expanding possibilities of Python programming.

Download or read book Statistics for Beginners in Data Science written by Ai Publishing and published by . This book was released on 2020-04-18 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistics for Beginners in Data Science Statistical methods are an integral part of data science. Hence, a formal training in statistics is indispensable for data scientists. If you are keen on getting your foot into the lucrative data science and analysis universe, you need to have a fundamental understanding of statistical analysis. Besides, Python is a versatile programming language you need to master to become a career data scientist. As a data scientist, you will identify, clean, explore, analyze, and interpret trends or possible patterns in complex data sets. The explosive growth of Big Data means you have to manage enormous amounts of data, clean it, manipulate it, and process it. Only then the most relevant data can be used. Python is a natural data science tool as it has an assortment of useful libraries, such as Pandas, NumPy, SciPy, Matplotlib, Seaborn, StatsModels, IPython, and several more. And Python's focus on simplicity makes it relatively easy for you to learn. Importantly, the ease of performing repetitive tasks saves you precious time. Long story short--Python is simply a high-priority data science tool. How Is This Book Different? The book focuses equally on the theoretical as well as practical aspects of data science. You will learn how to implement elementary data science tools and algorithms from scratch. The book contains an in-depth theoretical and analytical explanation of all data science concepts and also includes dozens of hands-on, real-life projects that will help you understand the concepts better. The ready-to-access Python codes at various places right through the book are aimed at shortening your learning curve. The main goal is to present you with the concepts, the insights, the inspiration, and the right tools needed to dive into coding and analyzing data in Python. The main benefit of purchasing this book is you get quick access to all the extra content provided with this book--Python codes, exercises, references, and PDFs--on the publisher's website, at no extra price. You get to experiment with the practical aspects of Data Science right from page 1. Beginners in Python and statistics will find this book extremely informative, practical, and helpful. Even if you aren't new to Python and data science, you'll find the hands-on projects in this book immensely helpful. The topics covered include: Introduction to Statistics Getting Familiar with Python Data Exploration and Data Analysis Pandas, Matplotlib, and Seaborn for Statistical Visualization Exploring Two or More Variables and Categorical Data Statistical Tests and ANOVA Confidence Interval Regression Analysis Classification Analysis Click the BUY button and download the book now to start learning and coding Python for Data Science.

Download or read book Data Analysis for Social Science and Marketing Research Using Python written by Manoj Morais and published by Createspace Independent Publishing Platform. This book was released on 2016-05-22 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is written for researchers in social science and marketing field, especially for those with little or no knowledge in computer programming. Data analytics has become part and parcel in the contemporary technologically fast paced world. We have amazing tools and software that allow us to analyse data available in various formats. However, most of the popular paid software and packages for data analysis is not affordable or not even accessible for the students, researchers. This is true in the case of many NGOs and agencies how are involved in community based research in developing countries. We have popular open source platforms and tools such as R and Python for data analysis. This book makes use of Python because of its simplicity, adaptability, broader scope and greater potential in advanced data mining and text mining contexts. We found it as a need to educate and train the researchers from social science and marketing research background, so that they could make use of Python, a promising tool to meet simple to extremely complex data analyses needs free of cost. The learnings from this book will not only help them in doing their conventional data analyses but also enable them to pursue advanced knowledge in machine learning algorithms, text analytics and other new generation techniques with the support of freely accessible open source platforms. Since the objective of the book is to educate the researchers with no programming background, we have made every effort to give hands-on experience in learning some basic coding in Python, which is sufficient for the readers to follow the book. The step-by-step procedure to do various data processing and analysis described in this book will make it easy for the users. Apart from that, we have tried our level best to give explanations on specific codes and how they perform to get us the desired output. We also request you to give you valuable comments and suggestions on the book, via our blog, so that we could improve the same in the upcoming volumes. We commit ourselves to providing explanations to the readers' questions related to the codes and analysis provided in this book. The book specifically deals with data sets of row and column format, as the general format commonly used in social science research, which most of the researchers are familiar with. So we do not work with arrays and dictionaries, except in one or two occasions (only to make you familiar with that) instead prefer to make use of Excel data and pandas data frame. The book consists of thirteen chapters. The first chapter gives an introduction to Python and its relevance and scope in contemporary data analysis contexts. Ch. 2 teaches the basics and Python coding, Ch. 3-7, provide a step-by-step narration of how to enter data, process it, preliminary analysis and data cleaning with the help of Python, Ch.8-9, present data visualizations and narration techniques using Python; Ch.10.demonstrate how Python can use for statistical analysis. The remaining chapters are focusing on giving more real life situations in data analysis and the practical solutions to handle them. The exercises provided in the book are similar to real analysis situations, and that will help the reader for an easy transition to the data analyst jobs. The authors have taken utmost care identifying and providing solutions to all practical difficulties the readers may face while using Python for data analysis purpose. The authors have developed a series of codes and have incorporated them to make data processing and analysis convenient and easy for the researchers. The self-learning materials given in this book will help social science and marketing researchers to deepen their understanding of various steps in data processing and analyses and to gain advanced skills in using Python for this purpose.

Download or read book Python for Data Analysis written by Cooper Turner and published by . This book was released on 2020-04-16 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: MASTER A CRUCIAL SKILL FOR FINANCIAL GROW AND SUCCESS Would you like to catch up on today's rushing world of science and technology? Do you know how important data analysis in almost every business is? Would you like to have a practical guide that will help you to understand and use all the data you have? If you answered "Yes" to at least one of these questions, then keep reading... Data analysis plays a very important role in many aspects of your everyday life today. From the moment you wake up, you interact with data at different levels. A lot of important decisions are made based on data analytics. None of the businesses would function and run successfully without people who know how to use master this powerful tool. Businesses use data to Understand Their Customer Needs and produce the Best Possible Product or Service. Python Programming Language is one of the best systems when it comes to data analysis, and if you are thinking about opening your own business someday or already have one, this is definitely a tool you must understand and use. In this book, you are going to learn the most important aspects of Python Data Analysis, as well as the foundation of the programming language. It is going to be your final tool for Business and Personal Growth. Here are just a few things you are going to get out of this book: The foundation of Python Programming Step-by-step strategies to use Python for Data Analysis How Coding with Python works, and why you need to understand it? Different types of data structures for Small and Medium-Sized Businesses What is Panda Manipulation Tool, and how to use it? Data Science Tips and Trick for a Massive Growth Much much more... I am about to start my own online business, will this book help? Yes, it is one of the main focus of this book - how to use data analysis to start and build a successful business. Why this book over other Python Programing books? This guide is complete, from basic programming for beginners to more advanced practical advice - strategies inside this book you can use in a lot of real-life situations, not only finance and business. If you came to this point, you are definitely ready to take action. Scroll up, click on "Buy Now" and discover the power of Data Analysis!

Download or read book Python For Data Science written by Ben Chan and published by . This book was released on 2021-02-08 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: Python for Data Science: The Best Crash Course Guide for Beginners Ever! Master the art of Python for data science with this guide. A must-have book, Python for Data Science, comes with top tips that every beginner needs to have. No more learning frustration and drama, study about the power of Python and learn to code, do data analysis and computer programming that works. Here's what you will love about this book: What is Python, anyway? Here's How to Get Started. A Beginners' Friendly Python Coding Guide with Easy to Follow Steps. Discover Data Analysis. This Method Ensures that Your Learning Experience is Easy, Fast and Effective. Thinking About Computer Programming with Python? Find out the Reasons Why it's Time to Start! Creative Ways You Can Master Data Types & Variables. Simple Ways the Pros use to Work Closely with Lists (that anyone can do). Learn the Secret Tips that Will Make You a Guru in Python in no time. And much more! Python for data science doesn't have to be hard. Read these awesome tips. Get actionable steps to learn the way you always wanted. Get your copy of Python for Data Science. The Crash Curse Guide for Beginners. Learn Right Now Python Coding, Data Analysis, and Computer Programming (for Women, Men, and Kids). And start your journey today. Scroll up and click the "add to cart" button to buy now!