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Author: Abhinaba Banerjee
Publisher: Orange Education Pvt Ltd
ISBN: 8197081913
Category : Computers
Languages : en
Pages : 283
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Book Description
Test your Data Analysis skills to its fullest using Python and other no-code tools KEY FEATURES ● Comprehensive coverage of Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, Julius AI for data acquisition, preparation, analysis, and visualization ● Real-world projects and practical applications for hands-on learning ● In-depth exploration of low-code and no-code tools for enhanced productivity DESCRIPTION Ultimate Data Analysis and Visualization with Python is your comprehensive guide to mastering the intricacies of data analysis and visualization using Python. This book serves as your roadmap to unlocking the full potential of Python for extracting insights from data using Pandas, NumPy, Matplotlib, Seaborn, and Julius AI. Starting with the fundamentals of data acquisition, you'll learn essential techniques for gathering and preparing data for analysis. From there, you’ll dive into exploratory data analysis, uncovering patterns and relationships hidden within your datasets. Through step-by-step tutorials, you'll gain proficiency in statistical analysis, time series forecasting, and signal processing, equipping you with the tools to extract actionable insights from any dataset. What sets this book apart is its emphasis on real-world applications. With a series of hands-on projects, you’ll apply your newfound skills to analyze diverse datasets spanning industries such as finance, healthcare, e-commerce, and more. By the end of the book, you'll have the confidence and expertise to tackle any data analysis challenge with Python. To aid your journey, the book includes a handy Python cheat sheet in the appendix, serving as a quick reference guide for common functions and syntax. WHAT WILL YOU LEARN ● Acquire data from various sources using Python, including web scraping, APIs, and databases. ● Clean and prepare datasets for analysis, handling missing values, outliers, and inconsistencies. ● Conduct exploratory data analysis to uncover patterns, trends, and relationships within your data. ● Perform statistical analysis using Python libraries such as NumPy and Pandas, including hypothesis testing and regression analysis. ● Master time series analysis techniques for forecasting future trends and making data-driven decisions. ● Apply signal processing methods to analyze and interpret signals in data, such as audio, image, and sensor data. ● Engage in real-world projects across diverse industries, from finance to healthcare, to reinforce your skills and experience. ● Utilize Python for in-depth analysis of real-world datasets, gaining practical experience and insights. ● Refer to the Python cheat sheet in the appendix for quick access to common functions and syntax, aiding your learning and development. WHO IS THIS BOOK FOR? This book is ideal for beginners, professionals, or students aiming to enhance their careers through hands-on experience in data acquisition, preparation, analysis, time series, and signal processing. Prerequisite knowledge includes basic Python and introductory statistics. Whether starting fresh or seeking to refresh skills, this comprehensive guide helps readers upskill effectively. TABLE OF CONTENTS 1. Introduction to Data Analysis and Data Visualization using Python 2. Data Acquisition 3. Data Cleaning and Preparation 4. Exploratory Data Analysis 5. Statistical Analysis 6. Time Series Analysis and Forecasting 7. Signal Processing 8. Analyzing Real-World Data Sets using Python APPENDIX A Python Cheat Sheet Index
Author: Abhinaba Banerjee
Publisher: Orange Education Pvt Ltd
ISBN: 8197081913
Category : Computers
Languages : en
Pages : 283
Get Book Here
Book Description
Test your Data Analysis skills to its fullest using Python and other no-code tools KEY FEATURES ● Comprehensive coverage of Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, Julius AI for data acquisition, preparation, analysis, and visualization ● Real-world projects and practical applications for hands-on learning ● In-depth exploration of low-code and no-code tools for enhanced productivity DESCRIPTION Ultimate Data Analysis and Visualization with Python is your comprehensive guide to mastering the intricacies of data analysis and visualization using Python. This book serves as your roadmap to unlocking the full potential of Python for extracting insights from data using Pandas, NumPy, Matplotlib, Seaborn, and Julius AI. Starting with the fundamentals of data acquisition, you'll learn essential techniques for gathering and preparing data for analysis. From there, you’ll dive into exploratory data analysis, uncovering patterns and relationships hidden within your datasets. Through step-by-step tutorials, you'll gain proficiency in statistical analysis, time series forecasting, and signal processing, equipping you with the tools to extract actionable insights from any dataset. What sets this book apart is its emphasis on real-world applications. With a series of hands-on projects, you’ll apply your newfound skills to analyze diverse datasets spanning industries such as finance, healthcare, e-commerce, and more. By the end of the book, you'll have the confidence and expertise to tackle any data analysis challenge with Python. To aid your journey, the book includes a handy Python cheat sheet in the appendix, serving as a quick reference guide for common functions and syntax. WHAT WILL YOU LEARN ● Acquire data from various sources using Python, including web scraping, APIs, and databases. ● Clean and prepare datasets for analysis, handling missing values, outliers, and inconsistencies. ● Conduct exploratory data analysis to uncover patterns, trends, and relationships within your data. ● Perform statistical analysis using Python libraries such as NumPy and Pandas, including hypothesis testing and regression analysis. ● Master time series analysis techniques for forecasting future trends and making data-driven decisions. ● Apply signal processing methods to analyze and interpret signals in data, such as audio, image, and sensor data. ● Engage in real-world projects across diverse industries, from finance to healthcare, to reinforce your skills and experience. ● Utilize Python for in-depth analysis of real-world datasets, gaining practical experience and insights. ● Refer to the Python cheat sheet in the appendix for quick access to common functions and syntax, aiding your learning and development. WHO IS THIS BOOK FOR? This book is ideal for beginners, professionals, or students aiming to enhance their careers through hands-on experience in data acquisition, preparation, analysis, time series, and signal processing. Prerequisite knowledge includes basic Python and introductory statistics. Whether starting fresh or seeking to refresh skills, this comprehensive guide helps readers upskill effectively. TABLE OF CONTENTS 1. Introduction to Data Analysis and Data Visualization using Python 2. Data Acquisition 3. Data Cleaning and Preparation 4. Exploratory Data Analysis 5. Statistical Analysis 6. Time Series Analysis and Forecasting 7. Signal Processing 8. Analyzing Real-World Data Sets using Python APPENDIX A Python Cheat Sheet Index
Author: Andrew Park
Publisher: Andrew Park
ISBN: 9781801779142
Category :
Languages : en
Pages : 138
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Book Description
★ 55% OFF for Bookstores! NOW at $33,97 instead of $43,97!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!
Author: Mark Graph
Publisher:
ISBN: 9781674067483
Category :
Languages : en
Pages : 212
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Book Description
This book gives you the perfect foray into data analysis. We discuss data analysis in Python in a way that will benefit you irrespective of your expertise level in Python. At the beginner level, you will appreciate the simple but elaborate approach we use to introduce you to basic Python concepts necessary for data analysis. With this knowledge, you can establish your foundation in data analysis, and build on that over time as you become accustomed to more complex subjects. For intermediate and expert users, you can also benefit from this book by using it as a reminder of some of the key points that define data science. When you dwell in a field for a long time, it is easy to take some things for granted. This happens to many programmers and developers. This book reminds you of the basic building principles that have helped you become one of the best data analysts in your field. Python libraries are some of the most important features in Python programming. The libraries help you perform tasks that would have otherwise been impossible to perform, or cumbersome. We discuss the major Python libraries you will use all the time, and highlight the main ones relevant to data analysis so you can get the distinction. Take note that data science is not an isolated subject. Most of the disciplines that involve Python programming depend on data, so you can expect to use the knowledge learned in this book in other fields, too. For example, when you advance into machine learning, your ability to perform exceptional data analysis will be required to help you build and train relevant machine learning models. Therefore, this book will not just get you ready for data analysis, it will prepare you for various fields in Python programming, including artificial intelligence, deep learning, and machine learning. Besides discussing the main Python libraries, we investigate the major data analysis libraries like Pandas and Matplotlib in-depth. These libraries will form the foundation of most of the data analysis work you perform over the years. Data analysis in Python will help you become an all-rounded developer. The good thing about learning Python is that you can use the knowledge gained to further your career in other programming languages like R. It is important to learn Python for data analysis from a conceptual and fundamental framework so that you set the right tone on which you can build your career further and advance into the future. Unlike other books, I don't claim that this book will make you a master of data science after a single read. That's not realistic, in fact, it's even a bit absurd. What I claim is that you will definitely learn about the basics. The rest is practice. The more you practice the better you code.
Author: Matt Foster
Publisher:
ISBN: 9781801091947
Category : Computers
Languages : en
Pages : 132
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Book Description
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, Get Your Copy Now!
Author: Ethan Williams
Publisher:
ISBN: 9781686688164
Category :
Languages : en
Pages : 141
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Book Description
IntroductionHave you ever thought about data analytics? Are you looking for an excellent tool to use in your data analysis? Well, you have come to the right place. Python is one of the best tools that you can use for your data analysis for several reasons;Flexibility & Ease of learningIf you are trying something creative that no one has ever done before, then Python is the best way to go. It also ideal for any developer that is looking for a program that will allow them to script websites and applications. The best thing that I love about Python is its readability and simplicity, which goes a long way in boosting a gradual and relatively low learning curve. This is precisely what makes Python an ideal tool for beginners. It is open sourceThis means that Python is an open-source program that also has built a valuable community-based model. It is designed to run on different OS ranging from Windows to Linux environments. The good thing with this language is that you can easily port it to a wide range of platforms. There are so many open-source python libraries such as Data manipulation, statistics, visualization, machine learning, mathematics, and natural language processing, among others.It is well-supportedDid you know that anything that could go wrong goes wrong? Think about it, if you are using something that you did not have to pay for, will you get the help that you need quickly? Well, the truth is a definite-NO!The most fortunate thing with Python is that it is widely used both in academics and industrial levels. This means that there are so many analytical libraries at your disposal. Therefore, if as a python user, you get stuck and need help, you can get it quickly on Stack Overflow's user-contributed codes, mailing lists, and documentation. The more python gains popularity, the more the users keep contributing information on their experience with the programming language. That said, one thing that is important to note is that Python is not an overly complicated language to use. The price is right and all the support you need out there to ensure that while working on your project, you do not come to a screeching halt whenever you hit a snag.
Author: Bhagvan Kommadi
Publisher: Orange Education Pvt Ltd
ISBN: 8197256209
Category : Computers
Languages : en
Pages : 302
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Book Description
TAGLINE Creating Next Gen Apps in Finance KEY FEATURES ● Master the Python libraries and packages essential for financial applications, enabling robust development. ● Utilize Python for developing applications that process financial information, visualize data in diverse formats, and create insightful representations. ● Derive analytical insights from mathematical models integrated into Python applications for data-driven decision-making in finance and fintech. DESCRIPTION Dive into the dynamic world where finance meets fintech with Python's versatile capabilities in this 'Ultimate Python for Fintech Solutions'. Whether you're aiming to build secure trading platforms, conduct deep statistical analysis, or pioneer next-generation financial technologies, this book quips you with the knowledge, tools, and practical insights to succeed. This book starts with Python's foundational programming techniques, essential for understanding financial principles and laying the groundwork for robust applications. You will learn to build scalable solutions that handle complex financial data with ease by using Python for analysis, forecasting, and data visualization. Next, it moves to explore advanced topics like AI/ML applications tailored for finance, enabling you to unlock predictive insights and streamline decision-making processes. You will discover how Python integrates cutting-edge technologies such as Big Data and Blockchain, to offer innovative solutions for modern fintech challenges. By the end of this expansive book, you will gain the expertise needed to develop sophisticated financial applications, visualize data effectively across desktop and web platforms, and drive innovation in fintech. WHAT WILL YOU LEARN ● Learn to build robust applications tailored for financial analysis, modeling, and fintech solutions using Python. ● Learn to analyze large volumes of financial data, and visualize insights effectively. ● Apply advanced AI/ML techniques to predict trends, optimize financial strategies, and automate decision-making processes. ● Integrate Python with Big Data platforms and Blockchain technologies to work with massive datasets and decentralized financial systems. ● Acquire the knowledge and skills to innovate in the fintech space to address modern financial challenges and opportunities. WHO IS THIS BOOK FOR? This book is for working professionals, students, business managers, consultants, technical/functional analysts, anyone wishing to improve their skills in Fintech with Python. This book will be a great start for a programmer who wants to start on the Python tech stack and make a career in Fintech space. The prerequisites for the reader will be basic mathematics and advanced math topics such as time series, derivatives, and integrals. The outcome for the reader will be to understand mathematical modeling and to have capability to develop next gen financial apps. TABLE OF CONTENTS 1. Getting Started on Python Infrastructure and Building Financial Apps 2. Learning Financial Concepts Using Python 3. Data Structures and Algorithms Using Python 4. Object Oriented Programming Using Python 5. Building Simulation and Mathematical Analysis Tools Using Python 6. Stochastic Mathematics and Building Models Using Python 7. Prediction Algorithms Using Python 8. Data Science and Statistical Algorithms Using Python 9. Desktop and Web Charting Using Python 10. AI/ML Apps Using Python 11. Big Data/Blockchain-Based Solutions Using Python 12. Next Generation FinTech Apps Using Python with Financial Singularity Index
Author: Simhadri Govindappa
Publisher: Orange Education Pvt Ltd
ISBN: 8197396574
Category : Computers
Languages : en
Pages : 367
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Book Description
TAGLINE Master the Hadoop Ecosystem and Build Scalable Analytics Systems KEY FEATURES ● Explains Hadoop, YARN, MapReduce, and Tez for understanding distributed data processing and resource management. ● Delves into Apache Hive and Apache Spark for their roles in data warehousing, real-time processing, and advanced analytics. ● Provides hands-on guidance for using Python with Hadoop for business intelligence and data analytics. DESCRIPTION In a rapidly evolving Big Data job market projected to grow by 28% through 2026 and with salaries reaching up to $150,000 annually—mastering big data analytics with the Hadoop ecosystem is most sought after for career advancement. The Ultimate Big Data Analytics with Apache Hadoop is an indispensable companion offering in-depth knowledge and practical skills needed to excel in today's data-driven landscape. The book begins laying a strong foundation with an overview of data lakes, data warehouses, and related concepts. It then delves into core Hadoop components such as HDFS, YARN, MapReduce, and Apache Tez, offering a blend of theory and practical exercises. You will gain hands-on experience with query engines like Apache Hive and Apache Spark, as well as file and table formats such as ORC, Parquet, Avro, Iceberg, Hudi, and Delta. Detailed instructions on installing and configuring clusters with Docker are included, along with big data visualization and statistical analysis using Python. Given the growing importance of scalable data pipelines, this book equips data engineers, analysts, and big data professionals with practical skills to set up, manage, and optimize data pipelines, and to apply machine learning techniques effectively. Don’t miss out on the opportunity to become a leader in the big data field to unlock the full potential of big data analytics with Hadoop. WHAT WILL YOU LEARN ● Gain expertise in building and managing large-scale data pipelines with Hadoop, YARN, and MapReduce. ● Master real-time analytics and data processing with Apache Spark’s powerful features. ● Develop skills in using Apache Hive for efficient data warehousing and complex queries. ● Integrate Python for advanced data analysis, visualization, and business intelligence in the Hadoop ecosystem. ● Learn to enhance data storage and processing performance using formats like ORC, Parquet, and Delta. ● Acquire hands-on experience in deploying and managing Hadoop clusters with Docker and Kubernetes. ● Build and deploy machine learning models with tools integrated into the Hadoop ecosystem. WHO IS THIS BOOK FOR? This book is tailored for data engineers, analysts, software developers, data scientists, IT professionals, and engineering students seeking to enhance their skills in big data analytics with Hadoop. Prerequisites include a basic understanding of big data concepts, programming knowledge in Java, Python, or SQL, and basic Linux command line skills. No prior experience with Hadoop is required, but a foundational grasp of data principles and technical proficiency will help readers fully engage with the material. TABLE OF CONTENTS 1. Introduction to Hadoop and ASF 2. Overview of Big Data Analytics 3. Hadoop and YARN MapReduce and Tez 4. Distributed Query Engines: Apache Hive 5. Distributed Query Engines: Apache Spark 6. File Formats and Table Formats (Apache Ice-berg, Hudi, and Delta) 7. Python and the Hadoop Ecosystem for Big Data Analytics - BI 8. Data Science and Machine Learning with Hadoop Ecosystem 9. Introduction to Cloud Computing and Other Apache Projects Index
Author: Guido Van Smit
Publisher:
ISBN: 9781712559215
Category :
Languages : en
Pages : 298
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Book Description
Get complete instructions for manipulating, processing, cleaning, and crunching datasets in PythonPython for Data Analysis represents now one of the most interesting and useful applications among all the possible applications of Machine Learning and Artificial Intelligence. This guidebook is the ultimate guide to learning insights and strategies to help you grow your business, save time, resources, and energy or if you are looking for a new job, but it requires a solid background in terms of processes and technologies involved. It will walk you through the entire program from A to Z and offers a straightforward approach to Python with plenty of opportunities for hands-on learning and improving your skills. Inside, you'll find: What is and how Data Analysis works Essentials Python Libraries: NumPy, Pandas, IPython and Jupyter Data Types in Python Text Analysis in Python 3 Analyze and manipulate regular and irregular time series data Practical applications to put into use today And so much more! Installation and Setup If you're ready to fully grasp Python for Data Analysis, this book is the perfect guide to help you!
Author: Daniel Howard
Publisher:
ISBN: 9781711119649
Category :
Languages : en
Pages : 162
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Book Description
Are you searching for a professional guide for learning data analysis from scratch? In this crash course, you will find everything you need to become proficient in this discipline, regardless of your previous experience. Starting from the basics, Daniel Howard will teach you the most up-to-date techniques to manipulate and process data, using Python libraries such us NumPy, Pandas and Matplotlib. Python is one of the most famous programming languages and it is recognized as the most effective tool for machine learning and data science, thanks to its large number of dedicated resources. Since it is designed for everyone, you will be amazed by the large number of programs that you will be able to create in no time, even as a beginner. Here is a preview of what you will learn: What data analysis is, and why it is fundamental in hundreds of business and technological applications Effective computational methods to manipulate large amounts of data Data visualization tools and techniques How to develop a successful career in one of the best paid sectors Real-world applications of machine learning and artificial intelligence What predictive modeling is How to build neural networks with Python If you are a student or a professional looking for more technical skills, or if you are simply curious about data analytics and its powerful applications, then click the BUY button and get your copy!
Author: Jessica Rizzo
Publisher:
ISBN: 9781081068004
Category :
Languages : en
Pages : 334
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Book Description
If you're tired of licensing third-party software for data analysis, Python Data Science will help you do it for yourself! Recently, more and more companies are learning that they need to make DATA-DRIVEN decisions. And with big data and data science on the rise, we now have more data than we know what to do with. In fact, without a doubt, you have already experienced data science in one way or another. Obviously, you are interacting with data science products every time you search for information on the web by using search engines such as Google, or asking for directions with your mobile phone. Data science is the science and technology focused on collecting raw data and processing it in an effective manner. It is the combination of concepts and methods that make it possible to give meaning and understandability to huge volumes of data. Data science has been the force behind resolving some of our most common daily tasks for several years. In nearly all of our daily work, we directly or indirectly work on storing and exchanging data. With the rapid development of technology, the need to store data effectively is also increasing. That's why it needs to be handled properly. Basically, data science unearths the hidden insights of raw-data and uses them for productive output. Python is often used in data science today because it is a mature programming language that has excellent properties for newbie programmers. Some of the most remarkable of these properties are its easy to read code, suppression of non-mandatory delimiters, dynamic typing, and dynamic memory usage. Python is an interpreted language, and it can be executed in the Python console without any need to compile to machine language. "Python Data Science" teaches a complete course of data science, including key topics like data integration, data mining, python etc. We will explore NumPy for numerical data, Pandas for data analysis, IPython, Scikit-learn and Tensorflow for machine learning and business. Each of the chapters in this book is devoted to one of the most interesting aspects of data analysis and processing. The following are some of the major topics covered in Python Data Science: Understanding Data Science Getting Started with Python for Data Scientists Descriptive statistics Data Analysis and Libraries NumPy Arrays and Vectorized Computation Data Analysis with Pandas Data Visualization Data Mining Classifying with Scikit-learn Estimators Giving Computers the Ability to Learn from Data Training Machine Learning Algorithms The Python ecosystem for data science discussed within Python Data Science includes SciPy, NumPy, Matplotlib, Pandas, and Scikit-learn, which provides all of the data science algorithms. Data processing and analysis is one of the hottest areas of IT, where developers who can handle projects of any level, from social networks to trained systems, are constantly required. We hope this book will be the starting point for your journey into the fascinating world of Data Science. To get started on your Python adventure, just scroll back up and click the 'Buy' button.
