Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Python Strategy
Download Python Strategy full books in PDF, epub, and Kindle. Read online Python Strategy ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Python Strategy by : Tigran Petrosian
Download or read book Python Strategy written by Tigran Petrosian and published by Quality Chess. This book was released on 2015-06-17 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tigran Petrosian is a titan of chess history. All agree he was a genius of strategy, defense and sacrifice, but didn t he take too many draws? Possibly so, but when Petrosian selected and annotated his best games, that flaw disappeared, leaving only brilliance and profound chess understanding. As Garry Kasparov said: "My games with the 9th World Champion broadened my understanding of chess. Had it not been for these two defeats, I would possibly not have reached the top in chess." This book is an English translation of a Russian classic that is a favorite of many grandmasters, including Mihail Marin and Levon Aronian. A literal translation of the Russian title might be 'The Strategy of Soundness' but Python Strategy is a better fit for Petrosian s more ambitious games. As another World Champion, Max Euwe, once wrote: "Petrosian is not a tiger that pounces on its prey, but rather a python that smothers its victim."
Book Synopsis Python for Algorithmic Trading by : Yves Hilpisch
Download or read book Python for Algorithmic Trading written by Yves Hilpisch and published by O'Reilly Media. This book was released on 2020-11-12 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithmic trading, once the exclusive domain of institutional players, is now open to small organizations and individual traders using online platforms. The tool of choice for many traders today is Python and its ecosystem of powerful packages. In this practical book, author Yves Hilpisch shows students, academics, and practitioners how to use Python in the fascinating field of algorithmic trading. You'll learn several ways to apply Python to different aspects of algorithmic trading, such as backtesting trading strategies and interacting with online trading platforms. Some of the biggest buy- and sell-side institutions make heavy use of Python. By exploring options for systematically building and deploying automated algorithmic trading strategies, this book will help you level the playing field. Set up a proper Python environment for algorithmic trading Learn how to retrieve financial data from public and proprietary data sources Explore vectorization for financial analytics with NumPy and pandas Master vectorized backtesting of different algorithmic trading strategies Generate market predictions by using machine learning and deep learning Tackle real-time processing of streaming data with socket programming tools Implement automated algorithmic trading strategies with the OANDA and FXCM trading platforms
Download or read book Powerful Python written by Aaron Maxwell and published by . This book was released on 2017-05-07 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are many books for those new to Python, new to programming, or both. Powerful Python is different. Written for experienced developers like you, its carefully crafted chapters teach intermediate and advanced strategies, patterns, and tools for modern Python. Focused on Python 3, with full support for 2.7. DRM-free digital upgrade: powerfulpython.com/book-upgrade "Feels like Neo learning Jiu jitsu in the Matrix." - John Beauford (@johnbeauford) "I just wanted to let you know what an excellent book this is... I keep going back to your book to learn Python." - Fahad Qazi, London, UK "Thanks. Keep up the good work. Your chapter on decorators is the best I have seen on that topic." - Leon Tietz, Minnesota, USA "Powerful Python is already helping me get huge optimization gains." - Timothy Dobbins (@TmthyDobbins) "What have I found good and valuable about the book so far? Everything honestly. The clear explanations, solid code examples have really helped me advance as a Python coder... Thank you! It has really helped me grasp some advanced concepts that I felt were beyond my abilities." - Nick S., Colorado, USA For data scientists, back-end engineers, web developers, sysadmins, devops, QA testers and more. What's included: An unrelenting selective spotlight on what's most valuable and impactful to working, full-time, professional Python developers Well-researched, detailed, realistic code on almost every page, powerfully illustrating key points. Very little "toy code" How to use decorators to add rich features to functions and classes; untangle distinct, frustratingly intertwined concerns in your code; and build powerful, extensible software frameworks How to use Python in ways that incentivize other developers to use and re-use your code, again and again... amplifying the impact of the code you write, and boosting your reputation among your peers Powerfully and easily weave iterators and generators throughout your applications, making them massively scalable, highly performant, and far more readable and maintainable How to fully leverage Python's exception and error model... giving you a detailed understanding even experienced Pythonistas often lack, and putting some of the most powerfully Pythonic exception-handling patterns in your toolbox How "magic methods" imbue natural, readable, expressive syntax into your classes and objects... and how to "break the rules" to craft stunningly intuitive, compellingly reusable library interfaces Valuable and powerful design patterns, and how Python's special language features give you uniquely powerful implementations not possible in other languages Deep and detailed instruction on how to write practical, realistic unit tests... using test-driven development to easily get into a state of flow... where you find yourself implementing feature after feature, keeping your focus with ease for long periods of time How to rapidly set up effective logging for scripts, sprawling Python applications, and everything in between An enthusiastic and unapologetic focus on Python 3, and what makes it great... with full explanation and support for getting the same results with Python 2.7 More at PowerfulPython.com.
Book Synopsis Hands-On Financial Trading with Python by : Jiri Pik
Download or read book Hands-On Financial Trading with Python written by Jiri Pik and published by Packt Publishing Ltd. This book was released on 2021-04-29 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build and backtest your algorithmic trading strategies to gain a true advantage in the market Key FeaturesGet quality insights from market data, stock analysis, and create your own data visualisationsLearn how to navigate the different features in Python's data analysis librariesStart systematically approaching quantitative research and strategy generation/backtesting in algorithmic tradingBook Description Creating an effective system to automate your trading can help you achieve two of every trader's key goals; saving time and making money. But to devise a system that will work for you, you need guidance to show you the ropes around building a system and monitoring its performance. This is where Hands-on Financial Trading with Python can give you the advantage. This practical Python book will introduce you to Python and tell you exactly why it's the best platform for developing trading strategies. You'll then cover quantitative analysis using Python, and learn how to build algorithmic trading strategies with Zipline using various market data sources. Using Zipline as the backtesting library allows access to complimentary US historical daily market data until 2018. As you advance, you will gain an in-depth understanding of Python libraries such as NumPy and pandas for analyzing financial datasets, and explore Matplotlib, statsmodels, and scikit-learn libraries for advanced analytics. As you progress, you'll pick up lots of skills like time series forecasting, covering pmdarima and Facebook Prophet. By the end of this trading book, you will be able to build predictive trading signals, adopt basic and advanced algorithmic trading strategies, and perform portfolio optimization to help you get —and stay—ahead of the markets. What you will learnDiscover how quantitative analysis works by covering financial statistics and ARIMAUse core Python libraries to perform quantitative research and strategy development using real datasetsUnderstand how to access financial and economic data in PythonImplement effective data visualization with MatplotlibApply scientific computing and data visualization with popular Python librariesBuild and deploy backtesting algorithmic trading strategiesWho this book is for If you're a financial trader or a data analyst who wants a hands-on introduction to designing algorithmic trading strategies, then this book is for you. You don't have to be a fully-fledged programmer to dive into this book, but knowing how to use Python's core libraries and a solid grasp on statistics will help you get the most out of this book.
Book Synopsis Algorithmic Trading with Python by : Chris Conlan
Download or read book Algorithmic Trading with Python written by Chris Conlan and published by Independently Published. This book was released on 2020-04-09 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithmic Trading with Python discusses modern quant trading methods in Python with a heavy focus on pandas, numpy, and scikit-learn. After establishing an understanding of technical indicators and performance metrics, readers will walk through the process of developing a trading simulator, strategy optimizer, and financial machine learning pipeline. This book maintains a high standard of reprocibility. All code and data is self-contained in a GitHub repo. The data includes hyper-realistic simulated price data and alternative data based on real securities. Algorithmic Trading with Python (2020) is the spiritual successor to Automated Trading with R (2016). This book covers more content in less time than its predecessor due to advances in open-source technologies for quantitative analysis.
Book Synopsis Python for Finance Cookbook by : Eryk Lewinson
Download or read book Python for Finance Cookbook written by Eryk Lewinson and published by Packt Publishing Ltd. This book was released on 2020-01-31 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandas Key FeaturesUse powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial dataExplore unique recipes for financial data analysis and processing with PythonEstimate popular financial models such as CAPM and GARCH using a problem-solution approachBook Description Python is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries. In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks. By the end of this book, you’ll have learned how to effectively analyze financial data using a recipe-based approach. What you will learnDownload and preprocess financial data from different sourcesBacktest the performance of automatic trading strategies in a real-world settingEstimate financial econometrics models in Python and interpret their resultsUse Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessmentImprove the performance of financial models with the latest Python librariesApply machine learning and deep learning techniques to solve different financial problemsUnderstand the different approaches used to model financial time series dataWho this book is for This book is for financial analysts, data analysts, and Python developers who want to learn how to implement a broad range of tasks in the finance domain. Data scientists looking to devise intelligent financial strategies to perform efficient financial analysis will also find this book useful. Working knowledge of the Python programming language is mandatory to grasp the concepts covered in the book effectively.
Book Synopsis Python Algorithmic Trading Cookbook by : Pushpak Dagade
Download or read book Python Algorithmic Trading Cookbook written by Pushpak Dagade and published by Packt Publishing Ltd. This book was released on 2020-08-28 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build a solid foundation in algorithmic trading by developing, testing and executing powerful trading strategies with real market data using Python Key FeaturesBuild a strong foundation in algorithmic trading by becoming well-versed with the basics of financial marketsDemystify jargon related to understanding and placing multiple types of trading ordersDevise trading strategies and increase your odds of making a profit without human interventionBook Description If you want to find out how you can build a solid foundation in algorithmic trading using Python, this cookbook is here to help. Starting by setting up the Python environment for trading and connectivity with brokers, you’ll then learn the important aspects of financial markets. As you progress, you’ll learn to fetch financial instruments, query and calculate various types of candles and historical data, and finally, compute and plot technical indicators. Next, you’ll learn how to place various types of orders, such as regular, bracket, and cover orders, and understand their state transitions. Later chapters will cover backtesting, paper trading, and finally real trading for the algorithmic strategies that you've created. You’ll even understand how to automate trading and find the right strategy for making effective decisions that would otherwise be impossible for human traders. By the end of this book, you’ll be able to use Python libraries to conduct key tasks in the algorithmic trading ecosystem. Note: For demonstration, we're using Zerodha, an Indian Stock Market broker. If you're not an Indian resident, you won't be able to use Zerodha and therefore will not be able to test the examples directly. However, you can take inspiration from the book and apply the concepts across your preferred stock market broker of choice. What you will learnUse Python to set up connectivity with brokersHandle and manipulate time series data using PythonFetch a list of exchanges, segments, financial instruments, and historical data to interact with the real marketUnderstand, fetch, and calculate various types of candles and use them to compute and plot diverse types of technical indicatorsDevelop and improve the performance of algorithmic trading strategiesPerform backtesting and paper trading on algorithmic trading strategiesImplement real trading in the live hours of stock marketsWho this book is for If you are a financial analyst, financial trader, data analyst, algorithmic trader, trading enthusiast or anyone who wants to learn algorithmic trading with Python and important techniques to address challenges faced in the finance domain, this book is for you. Basic working knowledge of the Python programming language is expected. Although fundamental knowledge of trade-related terminologies will be helpful, it is not mandatory.
Book Synopsis Trading Evolved by : Andreas F. Clenow
Download or read book Trading Evolved written by Andreas F. Clenow and published by . This book was released on 2019-08-07 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: Systematic trading allows you to test and evaluate your trading ideas before risking your money. By formulating trading ideas as concrete rules, you can evaluate past performance and draw conclusions about the viability of your trading plan. Following systematic rules provides a consistent approach where you will have some degree of predictability of returns, and perhaps more importantly, it takes emotions and second guessing out of the equation. From the onset, getting started with professional grade development and backtesting of systematic strategies can seem daunting. Many resort to simplified software which will limit your potential. Trading Evolved will guide you all the way, from getting started with the industry standard Python language, to setting up a professional backtesting environment of your own. The book will explain multiple trading strategies in detail, with full source code, to get you well on the path to becoming a professional systematic trader. This is a highly practical book, where every aspect is explained, all source code shown and no holds barred. Written by Andreas F. Clenow, author of the international best sellers Following the Trend and Stocks on the Move, Trading Evolved goes into greater depth and covers strategies for trading both futures and equities. "Trading Evolved is an incredible resource for aspiring quants. Clenow does an excellent job making complex subjects easy to access and understand. Bravo." -- Wes Gray, PhD, CEO Alpha Architect
Book Synopsis Machine Learning for Algorithmic Trading by : Stefan Jansen
Download or read book Machine Learning for Algorithmic Trading written by Stefan Jansen and published by Packt Publishing Ltd. This book was released on 2020-07-31 with total page 822 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.
Book Synopsis Hands-On Data Science for Marketing by : Yoon Hyup Hwang
Download or read book Hands-On Data Science for Marketing written by Yoon Hyup Hwang and published by Packt Publishing Ltd. This book was released on 2019-03-29 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimize your marketing strategies through analytics and machine learning Key FeaturesUnderstand how data science drives successful marketing campaignsUse machine learning for better customer engagement, retention, and product recommendationsExtract insights from your data to optimize marketing strategies and increase profitabilityBook Description Regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. With this book, you will learn to implement data science techniques to understand the drivers behind the successes and failures of marketing campaigns. This book is a comprehensive guide to help you understand and predict customer behaviors and create more effectively targeted and personalized marketing strategies. This is a practical guide to performing simple-to-advanced tasks, to extract hidden insights from the data and use them to make smart business decisions. You will understand what drives sales and increases customer engagements for your products. You will learn to implement machine learning to forecast which customers are more likely to engage with the products and have high lifetime value. This book will also show you how to use machine learning techniques to understand different customer segments and recommend the right products for each customer. Apart from learning to gain insights into consumer behavior using exploratory analysis, you will also learn the concept of A/B testing and implement it using Python and R. By the end of this book, you will be experienced enough with various data science and machine learning techniques to run and manage successful marketing campaigns for your business. What you will learnLearn how to compute and visualize marketing KPIs in Python and RMaster what drives successful marketing campaigns with data scienceUse machine learning to predict customer engagement and lifetime valueMake product recommendations that customers are most likely to buyLearn how to use A/B testing for better marketing decision makingImplement machine learning to understand different customer segmentsWho this book is for If you are a marketing professional, data scientist, engineer, or a student keen to learn how to apply data science to marketing, this book is what you need! It will be beneficial to have some basic knowledge of either Python or R to work through the examples. This book will also be beneficial for beginners as it covers basic-to-advanced data science concepts and applications in marketing with real-life examples.
Book Synopsis Principles of Package Design by : Matthias Noback
Download or read book Principles of Package Design written by Matthias Noback and published by Apress. This book was released on 2018-11-13 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: Apply design principles to your classes, preparing them for reuse. You will use package design principles to create packages that are just right in terms of cohesion and coupling, and are user- and maintainer-friendly at the same time. The first part of this book walks you through the five SOLID principles that will help you improve the design of your classes. The second part introduces you to the best practices of package design, and covers both package cohesion principles and package coupling principles. Cohesion principles show you which classes should be put together in a package, when to split packages, and if a combination of classes may be considered a "package" in the first place. Package coupling principles help you choose the right dependencies and prevent wrong directions in the dependency graph of your packages. What You'll LearnApply the SOLID principles of class designDetermine if classes belong in the same packageKnow whether it is safe for packages to depend on each other Who This Book Is For Software developers with a broad range of experience in the field, who are looking for ways to reuse,share, and distribute their code
Book Synopsis Python in a Nutshell by : Alex Martelli
Download or read book Python in a Nutshell written by Alex Martelli and published by "O'Reilly Media, Inc.". This book was released on 2006-07-14 with total page 734 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume offers Python programmers a straightforward guide to the important tools and modules of this open source language. It deals with the most frequently used parts of the standard library as well as the most popular and important third party extensions.
Download or read book Design Patterns written by Erich Gamma and published by Pearson Deutschland GmbH. This book was released on 1995 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: Software -- Software Engineering.
Book Synopsis Python 3 Object-oriented Programming by : Dusty Phillips
Download or read book Python 3 Object-oriented Programming written by Dusty Phillips and published by Packt Publishing Ltd. This book was released on 2015-08-20 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unleash the power of Python 3 objects About This Book Stop writing scripts and start architecting programs Learn the latest Python syntax and libraries A practical, hands-on tutorial that teaches you all about abstract design patterns and how to implement them in Python 3 Who This Book Is For If you're new to object-oriented programming techniques, or if you have basic Python skills and wish to learn in depth how and when to correctly apply object-oriented programming in Python to design software, this is the book for you. What You Will Learn Implement objects in Python by creating classes and defining methods Separate related objects into a taxonomy of classes and describe the properties and behaviors of those objects via the class interface Extend class functionality using inheritance Understand when to use object-oriented features, and more importantly when not to use them Discover what design patterns are and why they are different in Python Uncover the simplicity of unit testing and why it's so important in Python Grasp common concurrency techniques and pitfalls in Python 3 Exploit object-oriented programming in key Python technologies such as Kivy and Django. Object-oriented programming concurrently with asyncio In Detail Python 3 is more versatile and easier to use than ever. It runs on all major platforms in a huge array of use cases. Coding in Python minimizes development time and increases productivity in comparison to other languages. Clean, maintainable code is easy to both read and write using Python's clear, concise syntax. Object-oriented programming is a popular design paradigm in which data and behaviors are encapsulated in such a way that they can be manipulated together. Many modern programming languages utilize the powerful concepts behind object-oriented programming and Python is no exception. Starting with a detailed analysis of object-oriented analysis and design, you will use the Python programming language to clearly grasp key concepts from the object-oriented paradigm. This book fully explains classes, data encapsulation, inheritance, polymorphism, abstraction, and exceptions with an emphasis on when you can use each principle to develop well-designed software. You'll get an in-depth analysis of many common object-oriented design patterns that are more suitable to Python's unique style. This book will not just teach Python syntax, but will also build your confidence in how to program. You will also learn how to create maintainable applications by studying higher level design patterns. Following this, you'll learn the complexities of string and file manipulation, and how Python distinguishes between binary and textual data. Not one, but two very powerful automated testing systems will be introduced in the book. After you discover the joy of unit testing and just how easy it can be, you'll study higher level libraries such as database connectors and GUI toolkits and learn how they uniquely apply object-oriented principles. You'll learn how these principles will allow you to make greater use of key members of the Python eco-system such as Django and Kivy. This new edition includes all the topics that made Python 3 Object-oriented Programming an instant Packt classic. It's also packed with updated content to reflect recent changes in the core Python library and covers modern third-party packages that were not available on the Python 3 platform when the book was first published. Style and approach Throughout the book you will learn key object-oriented programming techniques demonstrated by comprehensive case studies in the context of a larger project.
Book Synopsis Learn Quantum Computing with Python and Q# by : Sarah C. Kaiser
Download or read book Learn Quantum Computing with Python and Q# written by Sarah C. Kaiser and published by Simon and Schuster. This book was released on 2021-06-22 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: "For software developers. No prior experience with quantum computing required"--Back cover.
Book Synopsis Trend Following Strategies in Python by : Sofien Kaabar
Download or read book Trend Following Strategies in Python written by Sofien Kaabar and published by Independently Published. This book was released on 2021-10-31 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: What is this book all about? This book is a modest attempt at presenting a more modern version of technical analysis based on objective measures rather than subjective ones. A sizeable chunk of this beautiful type of analysis revolves around trend-following technical indicators which is what this book covers. I believe it is time to be creative with indicators. The following chapters present trend-following indicators and how to code/use them. The code included in the book is available in the GitHub repository. A QR code link will be provided in the book. What am I going to gain? You will gain exposure to many new indicators and strategies that will change the way you think about trading, and you will find yourself busy experimenting and choosing the strategy that suits you the best. How is it organized? The order of the chapter is not very important, although reading the introductory Python chapter is helpful. The book is divided into four parts: Part 1 deals with different types of moving averages, Part 2 deals with trend-following indicators, Part3 deals with market regime detection techniques, and finally, Part 4 will present many different trend-following technical strategies. What level of knowledge do I need to follow this book? Although a basic or a good understanding of trading and coding is considered very helpful, it is not necessary. At the beginning of the book, I have included a chapter that deals with some Python concepts, but this book is not about Python.
Book Synopsis Python: Real-World Data Science by : Dusty Phillips
Download or read book Python: Real-World Data Science written by Dusty Phillips and published by Packt Publishing Ltd. This book was released on 2016-06-10 with total page 1255 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unleash the power of Python and its robust data science capabilities About This Book Unleash the power of Python 3 objects Learn to use powerful Python libraries for effective data processing and analysis Harness the power of Python to analyze data and create insightful predictive models Unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics Who This Book Is For Entry-level analysts who want to enter in the data science world will find this course very useful to get themselves acquainted with Python's data science capabilities for doing real-world data analysis. What You Will Learn Install and setup Python Implement objects in Python by creating classes and defining methods Get acquainted with NumPy to use it with arrays and array-oriented computing in data analysis Create effective visualizations for presenting your data using Matplotlib Process and analyze data using the time series capabilities of pandas Interact with different kind of database systems, such as file, disk format, Mongo, and Redis Apply data mining concepts to real-world problems Compute on big data, including real-time data from the Internet Explore how to use different machine learning models to ask different questions of your data In Detail The Python: Real-World Data Science course will take you on a journey to become an efficient data science practitioner by thoroughly understanding the key concepts of Python. This learning path is divided into four modules and each module are a mini course in their own right, and as you complete each one, you'll have gained key skills and be ready for the material in the next module. The course begins with getting your Python fundamentals nailed down. After getting familiar with Python core concepts, it's time that you dive into the field of data science. In the second module, you'll learn how to perform data analysis using Python in a practical and example-driven way. The third module will teach you how to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis to more complex data types including text, images, and graphs. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. In the final module, we'll discuss the necessary details regarding machine learning concepts, offering intuitive yet informative explanations on how machine learning algorithms work, how to use them, and most importantly, how to avoid the common pitfalls. Style and approach This course includes all the resources that will help you jump into the data science field with Python and learn how to make sense of data. The aim is to create a smooth learning path that will teach you how to get started with powerful Python libraries and perform various data science techniques in depth.
