Key FeaturesClean, 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 dataBook DescriptionData 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 course, we will get you started with Python data analysis by introducing the basics of data analysis and supported Python libraries such as matplotlib, NumPy, and pandas. Create visualizations by choosing color maps, different shapes, sizes, and palettes then delve into statistical data analysis using distribution algorithms and correlations. Youll then find your way around different data and numerical problems, get to grips with Spark and HDFS, and set up migration scripts for web mining. Youll be able to quickly and accurately perform hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making. Finally, you will delve into advanced techniques such as performing regression, quantifying cause and effect using Bayesian methods, and discovering how to use Pythons tools for supervised machine learning.The course provides you with highly practical content explaining data analysis with Python, from the following Packt books:Getting Started with Python Data Analysis.Python Data Analysis Cookbook.Mastering Python Data Analysis.By the end of this course, you will have all the knowledge you need to analyze your data with varying complexity levels, and turn it into actionable insights.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 reportsAbout the AuthorPhuong Vothihong has an MSc in Computer Science, related to the area of machine learning. After graduating, she worked as a data scientist. She has significant experience in analyzing users behavior and building recommendation systems based on a users web history. Phuong is interested in reading machine learning, mathematics, and algorithm books, as well as data analysis articles.Martin Czygan studied German Literature and Computer Science in Leipzig, Germany. He has been working professionally as a software engineer for about 10 years. For the past eight years, he has been delving into Python and still enjoying it. In recent years he has been helping clients to build data-processing pipelines and search and analytics systems. His consultancy can be found at: http://www.xvfz.net.Ivan Idris was born in Bulgaria to Indonesian parents. He moved to the Netherlands and graduated in experimental physics. His graduation thesis had a strong emphasis on applied computer science. After graduating, he worked for several companies as a software developer, data warehouse developer, and QA analyst. His professional interests are business intelligence, big data, and cloud computing. He enjoys writing clean, testable code and interesting technical articles. He is the author of NumPy Beginners Guide, NumPy Cookbook, Learning NumPy, and Python Data Analysis, all by Packt Publishing.Magnus Vilhelm Persson is a scientist with a passion for Python and open source software usage and development. He obtained his PhD in Physics/Astronomy from Copenhagen Universitys Centre for Star and Planet Formation (StarPlan) in 2013. Since then, he has continued his research in Astronomy at various academic institutes across Europe. In his research, he uses various types of data and analysis to gain insights into how stars are formed. He has participated in radio shows about Astronomy and also organized workshops and intensive courses about the use of Python for data analysis. You can check out his web page at: http://vilhelm.nu.Luiz.Luiz Felipe Martins holds a PhD in applied mathematics from Brown University and has worked as a researcher and educator for more than 20 years. His research is mainly in the field of applied probability. He has been involved in developing code for the open source homework system, WeBWorK, where he wrote a library for the visualization of systems of differential equations. He was supported by an NSF grant for this project. Currently, he is an Associate Professor in the Department of Mathematics at Cleveland State University, Cleveland, Ohio, where he has developed several courses in applied mathematics and scientific computing. His current duties include coordinating all first-year calculus sessions.Table of ContentsIntroducing Data Analysis and LibrariesNumPy Arrays and Vectorized ComputationData Analysis with PandasData VisualizationTime SeriesInteracting with DatabasesData Analysis Application ExamplesMachine Learning Models with scikit-learnLaying the Foundation for Reproducible Data AnalysisCreating Attractive Data VisualizationsStatistical Data Analysis and ProbabilityDealing with Data and Numerical IssuesWeb Mining, Databases, and Big DataSignal Processing and TimeseriesSelecting Stocks with Financial Data AnalysisText Mining and Social Network AnalysisEnsemble Learning and Dimensionality ReductionEvaluating Classifiers, Regressors, and ClustersAnalyzing ImagesParallelism and PerformanceGlossaryFunction ReferenceOnline ResourcesTips and Tricks for Command-Line and Miscellaneous ToolsTools of the TradeExploring DataLearning About ModelsRegressionClusteringBayesian MethodsSupervised and Unsupervised LearningTime Series AnalysisMore on Jupyter Notebook and matplotlib Styles