Presents case studies and instructions on how to solve data analysis problems using Python.
python for data analysis
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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!
If you need to learn how to use the Python Programming Language to implement your own Data Analysis solution, and you are searching for a reference to start from, then keep reading. Data Analysis represents now one of the most interesting and useful applications among all the possible applications of Machine Learning and Artificial Intelligence. It represents also an incredible opportunity if you want to grow your business or if you are searching for a new job, but it requires a solid background in terms of processes and technologies involved. Developing your first Data Analysis project is a challenging task, and it can be vital to rely on a good guide. This book can assist you in understanding What is and how Data Analysis works How to set up your home computer to start your projects How to develop a Data Analysis project How to use the most popular Data Analysis libraries Even if you have not a degree in computer science or math, even if you have never worked on a Data Analysis project, with this book you can understand how to benefit from this set of technologies and unlock their extraordinary potential. If you want to give a new meaning to your data using the most modern Data Analysis techniques, scroll up and click the buy now button!
This book is for programmers, scientists, and engineers who have knowledge of the Python language and know the basics of data science. It is for those who wish to learn different data analysis methods using Python and its libraries. This book contains all the basic ingredients you need to become an expert data analyst.
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
Learn how to apply powerful data analysis techniques with popular open source Python modules About This Book Find, manipulate, and analyze your data using the Python 3.5 libraries Perform advanced, high-performance linear algebra and mathematical calculations with clean and efficient Python code An easy-to-follow guide with realistic examples that are frequently used in real-world data analysis projects. Who This Book Is For This book is for programmers, scientists, and engineers who have the knowledge of Python and know the basics of data science. It is for those who wish to learn different data analysis methods using Python 3.5 and its libraries. This book contains all the basic ingredients you need to become an expert data analyst. What You Will Learn Install open source Python modules such NumPy, SciPy, Pandas, stasmodels, scikit-learn,theano, keras, and tensorflow on various platforms Prepare and clean your data, and use it for exploratory analysis Manipulate your data with Pandas Retrieve and store your data from RDBMS, NoSQL, and distributed filesystems such as HDFS and HDF5 Visualize your data with open source libraries such as matplotlib, bokeh, and plotly Learn about various machine learning methods such as supervised, unsupervised, probabilistic, and Bayesian Understand signal processing and time series data analysis Get to grips with graph processing and social network analysis In Detail Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries. Style and approach The book takes a very comprehensive approach to enhance your understanding of data analysis. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work. Packed with clear, easy to follow examples, this book will turn you into an ace data analyst in no time.
If you are looking to master the fundamental concepts of Data Analysis and Data Science driven by the Python programming language to develop a solid understanding of all the latest innovative technologies, then this is just that one comprehensive book you have been waiting for. If you are looking to learn how to write effective and efficient codes in Python and master this extremely intuitive and flexible programming language that can be used for a variety of coding projects including machine learning algorithms, web applications, data mining and visualization, game development. Then this is just the book that you need. Some of the highlights of this book include: What the Python language is all about and how we are able to utilize it to get a lot of coding done. A look at the data analysis and how we can benefit, no matter what industry we are. How Python is able to work well with the data analysis and why it is the number one language to help you handle this. A look at some of the steps that we are able to utilize when it comes to our data analysis so we can get it all done the right way. How to install and use the NumPy library, one of the best extensions with Python, to help us get our data analysis done. How to work with the Pandas and IPython extensions so that we are able to get things done with your analysis. The practical uses of the data analysis to help you get it done. A look at the Matplotlib library to help you create some of your own visuals with your data when the analysis is done. How to work with data visuals and how they are so important to your work. The five major stages of the TDSP lifecycle that outline the interactive steps required for project execution along with the deliverables created at each stage. Installation instructions for Python so you can download and install Python on your operating system and get hands-on coding experience. Python coding concepts such as data types, classes, and objects variables, numbers, constructor functions, Booleans and much more. Learn the functioning of various data science libraries like Scikit-Learn, which has evolved as the gold standard for machine learning and data analysis. Deep dive into the Matplotlib library, which offers visualization tools and science computing modules supported by SciPy and learn how to create various graphs using Matplotlib and Pandas library. Learn how machine learning allows analysis of large volumes of data and delivers faster and more accurate results. Overview of four different machine-learning algorithms that can be used to cater to the available data set and create a desired machine-learning model. Learn how companies are able to employ a predictive analytics model to gain an understanding of customer interactions with their products or services based on customer's feelings or emotions shared on the social media platforms. All industries are able to benefit when it comes to working with data analysis and seeing some of the results that you would like. It is not always easy, and it is something that takes longer than many companies are prepared. However, when you are able to put it all together, and you work with the Python language to get it done, you will find that it can provide you with some insights that are invaluable. When you are ready to learn how to complete your own Python data analysis and Python data science, make sure to check out this guidebook to help you get started. Even if you have never studied Python language before, you can learn it quickly. So what are you waiting for? Go to the top of the page and click Buy Now
Are you looking for a book with in-depth information on data analysis with Python? Do you want to know the basics of programming and know more in a short time? Know the data analysis with an easy and accessible programming language? "Python for Data Analysis" is the book that will give you the information you need to access the basics of programming related to data analysis with Python. Python has transitioned over the years to provide accurate solutions for the day-to-day problems that face society. More programmers are getting into the programming field to improve what has already been there. However, that process requires dedication and self-motivation. Programming is not for the faint-hearted, but with the right mindset, you can achieve more within a short duration. All you need to know are the basics of Python programming to begin the coding journey. This book combines information that is crucial in data analysis when using Python. The area of computer programming is now a popular discipline among many computer users. The emergence of this field as a popular area has seen the explosion of various software programs that can assist in data analysis and interpretation. At the moment, there exist different computer languages which can support a computer user to handle data through creation, storage, and recovery of information. The impact of these programs is to substitute the standard forms of analysis that are tedious and time-consuming. The plans have various attributes that enable users to explain data and create theories. This means that these programs are robust and reliable in conducting qualitative data analysis. The computer qualitative data analysis techniques, on the other hand, are liked by many because of their simplicity and have outstanding attributes that assist in data analysis. These attributes save time, are simple to study and understand and apply fewer finances when compared to programming languages. You will learn: Definition of Python programming as well as the skills that learners should possess for a successful adventure. The History of Python programming How to download and install Python The definition of Python variables and the crucial naming sequence that programmers should be well conversant with. Types of variables and data variables in Python Computer data management and analysis Top 10 Python Libraries ...and more. Would You Like To Know More? Scroll to the top of the page and select the buy now button
Are you interested in learning more about your customers' needs? What would it be like if you were able to go through and really reduce waste, make good decisions, and beat out the competition? What if you could finally put all of that data you have been collecting for all of this time to good use, and ensure that you were really reaching your full potential? All of this and more can be done with the help of data analysis and data science, with the help of Python and machine learning. This guidebook is going to lead you from your data analysis idea, all of the way to actually completing your own analysis in no time. You will learn: What a data analysis is all about and why businesses are trying to make one of these works on all the data they have collected. The steps to complete our own data analysis. Why the Python language is one of the best choices for data analysis. How the world of machine learning is able to fit into this to make things easier. Data preparation and cleaning. Some of the most common libraries to complete data analysis, including Pandas, NumPy, and IPython. How these data visuals can help you to understand the data you are working with. Some of the main industries that are using data to improve their business. Even if you never used data analysis, learning it is easier than it looks, you just need the right guidance. This practical guide can bring your business to the front of competition. Scroll up and Buy now to make the most of your data.
You want to learn Python for data analysis using NumPy, Pandas, and IPython, and you don't know how to start? You don't need a big boring and expensive textbook. This book is the best one for everyone.Get your copy Now!!Why this book? Here are the reasons: The author has explored everything about python for data analysis using pandas, NumPy, Ipython and Matplotlib libraries from the basics. A simple language has been used. Many examples have been given, both theoretically and programmatically. Screenshots showing program outputs have been added. The book is written chronologically, in a step-by-step manner.Book Objectives: The Aims and Objectives of the Book: To help you understand why you should choose Python for data analysis tasks. To help you know the various data analysis libraries supported by Python and how to use them. To help you know how to analyze your business data and draw meaningful insights for effective decision making. To equip you with data analysis skills using Python programming language. To help you know where data analysis is applied today and how to use it in your everyday life. Who is this Book is for?: Here are the target readers for this book: Anybody who is a complete beginner to data analysis with Python or data analysis in general. Anybody who wants to advance their data analysis skills with Python programming language. Anybody who wants to know how to use data analysis for the benefit of their business or brand. Professionals in data science, computer programming, computer scientist. Professors, lecturers or tutors who are looking to find better ways to explain python for data analysis to their students in the simplest and easiest way. Students and academicians, especially those focusing on python programming, computer science, neural networks, machine learning, and deep learning. What do you need for this Book?: You are required to have installed the following on your computer: Python 3.X Numpy Pandas Matplotlib The Author guides you on how to install and configure the rest of the Python libraries that are required for data analysis.What is inside the book?: Why Python for Data Analysis? Exploring the Libraries Installation and Setup Using IPython Numpy Arrays and Vectorized Computation Pandas Library Data Wrangling Data Visualization Data Aggregation Working with Time Series Data Applications of Data Analysis Today The content of this book is all about data analysis with Python programming language using NumPy, Pandas, and IPython. It has been grouped into chapters, with each chapter exploring a different aspect of data analysis. The author has provided Python codes for doing different data analysis tasks. All these codes have been tested to ensure they are working correctly. Corresponding explanations have also been provided alongside each piece of code to help the reader understand the meaning of the various lines of the code. In addition to this, screenshots showing the output that each code should return have been given. The author has used a simple language to make it easy even for beginners to understand. The author begins by exploring the basic to the complex tasks in data analysis.