Preface
Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form - It needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications to deliver insights that are of tremendous value to their organizations.This course is your guide to getting started with Predictive Analytics using Python. You will see how to process data and make predictive models from it. We balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and numpy. Later you will learn the process of turning raw data into powerful insights. Through case studies and code examples using popular open-source Python libraries, this course illustrates the complete development process for analytic applications and how to quickly apply these methods to your own data to create robust and scalable prediction services. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates not only how these methods work, but how to implement them in practice. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring the insights of predictive modeling to life. Finally, you will see the best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world.
What this learning path covers
Module 1, Learning Predictive Analytics with Python, is your guide to getting started with Predictive Analytics using Python. You will see how to process data and make predictive models from it. It is the perfect balance of both statistical and mathematical concepts, and implementing them in Python using libraries such as pandas, scikit-learn, and numpy.
Module 2, Mastering Predictive Analytics with Python, will show you the process of turning raw data into powerful insights. Through case studies and code examples using popular open-source Python libraries, this course illustrates the complete development process for analytic applications and how to quickly apply these methods to your own data to create robust and scalable prediction services.
What you need for this learning path
Module 1:
In order to make the best use of this module, you will require the following:
- All the datasets that have been used to illustrate the concepts in various chapters. These datasets can be downloaded from this URL: https://goo.gl/zjS4C6. There is a sub-folder containing required datasets for each chapter.
- Your computer should have any of the Python distribution installed. The examples in the module have been worked upon in IPython Notebook. Following the examples will be much easier if you use IPython Notebook.This comes with Anaconda distribution that can be installed from https://www.continuum.io/downloads.
- The Python packages which are used widely, for example, pandas, matplotlib, scikit-learn, NumPy, and so on, should be installed. If you install Anaconda these packages will come pre-installed.
- One of the best ways to use this module will be to take the dataset used to illustrate concepts and flow along with the chapter. The concepts will be easier to understand if the reader works hands on the examples.
- A basic aptitude for mathematics is expected. It is beneficial to understand the mathematics behind the algorithms before applying them.
- Prior experience or knowledge of coding will be an added advantage. But, it is not a pre-requisite at all.
- Similarly, knowledge of statistics and some algorithms will be beneficial, but it is not a pre-requisite.
- An open mind curious to learn the tips and tricks of a subject that is going to be an indispensable skillset in the coming future.
Module 2:
You'll need latest Python version and PySpark version installed, along with the Jupyter notebook.
Who this learning path is for
This course is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move from a conceptual understanding of advanced analytics to an expert in designing and building advanced analytics solutions using Python. If you are familiar with coding in Python (or some other programming/statistical/scripting language) but have never used or read about Predictive Analytics algorithms, this course will also help you.
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