Python: Real-World Data Science by Sebastian Raschka

Unleash the power of Python and its robust data science capabilitiesAbout This BookUnleash the power of Python 3 objectsLearn to use powerful Python libraries for effective data processing and analysisHarness the power of Python to analyze data and create insightful predictive modelsUnlock deeper insights into machine learning with this vital guide to cutting-edge predictive analyticsWho This Book Is ForEntry-level analysts who want to enter in the data science world will find this course very useful to get themselves acquainted with Pythons data science capabilities for doing real-world data analysis.What You Will LearnInstall and setup PythonImplement objects in Python by creating classes and defining methodsGet acquainted with NumPy to use it with arrays and array-oriented computing in data analysisCreate effective visualizations for presenting 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 data mining concepts to real-world problemsCompute on big data, including real-time data from the InternetExplore how to use different machine learning models to ask different questions of your dataIn DetailThe 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, youll 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, its time that you dive into the field of data science. In the second module, youll 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, well 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.