Key FeaturesLearn 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 resultsBook DescriptionYou 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 predictive models from data. You will get balanced information on statistical and mathematical concepts, and implement them in Python using libraries such as Pandas, scikit-learn, and NumPy. Youll learn more about the best predictive modeling algorithms such as Linear Regression, Decision Tree, and Logistic Regression. Finally, you will master best practices in predictive modeling.After this, you will get all the practical guidance you need to help you on the journey to effective data visualization. Starting with a chapter on data frameworks, which explains the transformation of data into information and eventually knowledge, this path subsequently cover the complete visualization process using the most popular Python libraries with working examplesThis Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:Getting Started with Python Data Analysis, Phuong Vo.T.H &Martin CzyganLearning Predictive Analytics with Python, Ashish KumarMastering Python Data Visualization, Kirthi RamanWhat 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 D3About the AuthorPhuong Vo.T.H completed her MSc degree in computer science and then worked in some companies as a data scientist. She is experienced in analyzing users behavior and building recommendation systems based on users web histories.Martin Czygan studied computer science has been working as a software engineer for more than 10 years. He has been helping clients to build data processing pipelines and search and analytics systems.Ashish Kumar has a B. Tech from IIT Madras and is a data science enthusiast with extensive work experience in the field. He has also implemented predictive algorithms to glean actionable insights for clients from transport and logistics, online payment, and healthcare industries.Kirthi Raman is currently working as a lead data engineer with Neustar Inc, based in Mclean, Virginia USA. Kirthi has worked on data visualization, with a focus on JavaScript, Python, R, and Java, and is a distinguished engineer. 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-learnGetting Started with Predictive ModellingData CleaningData WranglingStatistical Concepts for Predictive ModellingLinear Regression with PythonLogistic Regression with PythonClustering with PythonTrees and Random Forests with PythonBest Practices for Predictive ModellingA List of LinksA Conceptual Framework for Data VisualizationData Analysis and VisualizationGetting Started with the Python IDENumerical Computing and Interactive PlottingFinancial and Statistical ModelsStatistical and Machine LearningBioinformatics, Genetics, and Network ModelsAdvanced VisualizationGo Forth and Explore VisualizationBibliography