Search

Leverage the power of Python to clean, scrape, analyze, and visualize your dataAbout This BookClean, format, and explore your data using the popular Python libraries and get valuable insights from itAnalyze big data sets; create attractive visualizations; manipulate and process various data types using NumPy, SciPy, and matplotlib; and morePacked with easy-to-follow examples to develop advanced computational skills for the analysis of complex dataWho This Book Is ForThis course is for developers, analysts, and data scientists who want to learn data analysis from scratch. This course will provide you with a solid foundation from which to analyze data with varying complexity. A working knowledge of Python (and a strong interest in playing with your data) is recommended.What You Will LearnUnderstand the importance of data analysis and master its processing stepsGet comfortable using Python and its associated data analysis libraries such as Pandas, NumPy, and SciPyClean and transform your data and apply advanced statistical analysis to create attractive visualizationsAnalyze images and time series dataMine text and analyze social networksPerform web scraping and work with different databases, Hadoop, and SparkUse statistical models to discover patterns in dataDetect similarities and differences in data with clusteringWork with Jupyter Notebook to produce publication-ready figures to be included in reportsIn DetailData analysis is the process of applying logical and analytical reasoning to study each component of data present in the system. Python is a multi-domain, high-level, programming language that offers a range of tools and libraries suitable for all purposes, it has slowly evolved as one of the primary languages for data science. Have you ever imagined becoming an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? If yes, look no further, this is the course you need!In this cours

Understand, evaluate, and visualize dataAbout This BookLearn basic steps of data analysis and how to use Python and its packagesA step-by-step guide to predictive modeling including tips, tricks, and best practicesEffectively visualize a broad set of analyzed data and generate effective resultsWho This Book Is ForThis book is for Python Developers who are keen to get into data analysis and wish to visualize their analyzed data in a more efficient and insightful manner.What You Will LearnGet acquainted with NumPy and use arrays and array-oriented computing in data analysisProcess and analyze data using the time-series capabilities of PandasUnderstand the statistical and mathematical concepts behind predictive analytics algorithmsData visualization with MatplotlibInteractive plotting with NumPy, Scipy, and MKL functionsBuild financial models using Monte-Carlo simulationsCreate directed graphs and multi-graphsAdvanced visualization with D3In DetailYou will start the course with an introduction to the principles of data analysis and supported libraries, along with NumPy basics for statistics and data processing. Next, you will overview the Pandas package and use its powerful features to solve data-processing problems. Moving on, you will get a brief overview of the Matplotlib API .Next, you will learn to manipulate time and data structures, and load and store data in a file or database using Python packages. You will learn how to apply powerful packages in Python to process raw data into pure and helpful data using examples. You will also get a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or building helpful products such as recommendations and predictions using Scikit-learn.After this, you will move on to a data analytics specialization—predictive analytics. Social media and IOT have resulted in an avalanche of data. You will get started with predictive analytics using Python. You will see how to create predic

Leverage the power of Python to clean, scrape, analyze, and visualize your dataAbout This BookClean, format, and explore your data using the popular Python libraries and get valuable insights from itAnalyze big data sets; create attractive visualizations; manipulate and process various data types using NumPy, SciPy, and matplotlib; and morePacked with easy-to-follow examples to develop advanced computational skills for the analysis of complex dataWho This Book Is ForThis course is for developers, analysts, and data scientists who want to learn data analysis from scratch. This course will provide you with a solid foundation from which to analyze data with varying complexity. A working knowledge of Python (and a strong interest in playing with your data) is recommended.What You Will LearnUnderstand the importance of data analysis and master its processing stepsGet comfortable using Python and its associated data analysis libraries such as Pandas, NumPy, and SciPyClean and transform your data and apply advanced statistical analysis to create attractive visualizationsAnalyze images and time series dataMine text and analyze social networksPerform web scraping and work with different databases, Hadoop, and SparkUse statistical models to discover patterns in dataDetect similarities and differences in data with clusteringWork with Jupyter Notebook to produce publication-ready figures to be included in reportsIn DetailData analysis is the process of applying logical and analytical reasoning to study each component of data present in the system. Python is a multi-domain, high-level, programming language that offers a range of tools and libraries suitable for all purposes, it has slowly evolved as one of the primary languages for data science. Have you ever imagined becoming an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? If yes, look no further, this is the course you need!In this cours

Understand, evaluate, and visualize dataAbout This BookLearn basic steps of data analysis and how to use Python and its packagesA step-by-step guide to predictive modeling including tips, tricks, and best practicesEffectively visualize a broad set of analyzed data and generate effective resultsWho This Book Is ForThis book is for Python Developers who are keen to get into data analysis and wish to visualize their analyzed data in a more efficient and insightful manner.What You Will LearnGet acquainted with NumPy and use arrays and array-oriented computing in data analysisProcess and analyze data using the time-series capabilities of PandasUnderstand the statistical and mathematical concepts behind predictive analytics algorithmsData visualization with MatplotlibInteractive plotting with NumPy, Scipy, and MKL functionsBuild financial models using Monte-Carlo simulationsCreate directed graphs and multi-graphsAdvanced visualization with D3In DetailYou will start the course with an introduction to the principles of data analysis and supported libraries, along with NumPy basics for statistics and data processing. Next, you will overview the Pandas package and use its powerful features to solve data-processing problems. Moving on, you will get a brief overview of the Matplotlib API .Next, you will learn to manipulate time and data structures, and load and store data in a file or database using Python packages. You will learn how to apply powerful packages in Python to process raw data into pure and helpful data using examples. You will also get a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or building helpful products such as recommendations and predictions using Scikit-learn.After this, you will move on to a data analytics specialization—predictive analytics. Social media and IOT have resulted in an avalanche of data. You will get started with predictive analytics using Python. You will see how to create predic

Learn to use powerful Python libraries for effective data processing and analysisAbout This BookLearn the basic processing steps in data analysis and how to use Python in this area through supported packages, especially Numpy, Pandas, and MatplotlibCreate, manipulate, and analyze your data to extract useful information to optimize your systemA hands-on guide to help you learn data analysis using PythonWho This Book Is ForIf you are a Python developer who wants to get started with data analysis and you need a quick introductory guide to the python data analysis libraries, then this book is for you.What You Will LearnUnderstand the importance of data analysis and get familiar with its processing stepsGet acquainted with Numpy to use with arrays and array-oriented computing in data analysisCreate effective visualizations to present your data using MatplotlibProcess and analyze data using the time series capabilities of PandasInteract with different kind of database systems, such as file, disk format, Mongo, and RedisApply the supported Python package to data analysis applications through examplesExplore predictive analytics and machine learning algorithms using Scikit-learn, a Python libraryIn DetailData analysis is the process of applying logical and analytical reasoning to study each component of data. Python is a multi-domain, high-level, programming language. It's often used as a scripting language because of its forgiving syntax and operability with a wide variety of different eco-systems. Python has powerful standard libraries or toolkits such as Pylearn2 and Hebel, which offers a fast, reliable, cross-platform environment for data analysis.With this book, we will get you started with Python data analysis and show you what its advantages are.The book starts by introducing the principles of data analysis and supported libraries, along with NumPy basics for statistic and data processing. Next it provides an overview of the Pandas package and uses its powerful featu