Predictive analytics is the process of analyzing historical data to estimate the future results. Pandas and scikit-learn are popular open source Python packages that provide fast, high performance data structures for performing efficient data manipulation and analysis. They have quickly emerged as a popular choice of tool for analysts to solve real-world analytical problems. So, if you’re familiar with the basics of the Python language and want to step into the world of data analysis, then you should surely go for this Learning Path. Packts Video Learning Path is a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. The highlights of this Learning Path are: Explore and work with different kinds of data sets to analyze and visualize your data Get to know how to use Pandas to make predictions using machine learning and scikit-learn Take your Pandas to the next level by learning advanced techniques To start off with your learning journey, you will begin with absolute basics such as installing and setting up of the Pandas library. You will then be introduced to fundamental data structures in Pandas and the different data types, indexing, and more. You will also learn to implement the basic functionalities of the Pandas library such as working with different kinds of data, indexing, and handling missing data. Next, you will learn to analyze and model your data, and organize the results of your analysis in the form of plots or other visualization means. Moving ahead, you will learn to perform predictive analysis on your data along with building machine learning models using scikit-learn and Pandas. Finally, you will walk through various machine learning algorithms. By the end of this Learning Path, you will be confident to use Pandas and scikit-learn for different data science tasks and perform predictive analysis on your own. Meet Your Expert: We have the best works of the following esteemed authors to ensure that your learning journey is smooth: Harish Garg is a data analyst, author, and software developer who is really passionate about data science and the Python programming language. He is a graduate from Udacity’s data analyst nanodegree program. He has 17 years of industry experience, which includes data analysis using Python, developing and testing enterprise and consumer software, managing projects and software teams, and creating training material and tutorials. Harish also worked for 11 years for Intel Security (previously McAfee, Inc.). He regularly contributes articles and tutorials on data analysis and Python. He is also active in the open data community and is a contributing member of the Data4Democracy open data initiative. He has written data analysis pieces for think tan takshashila. Alvaro Fuentes is a data scientist with an M.S. in Quantitative Economics and a M.S. in Applied Mathematics with more than 10 years of experience in analytical roles. He worked in the Central Bank of Guatemala as an economic analyst, building models for economic and financial data. He founded Quant Company to provide consulting and training services in data science topics and has been a consultant for many projects in fields such as; business, education, psychology, and mass media. He also has taught many (online and in-site) courses to students from around the world in topics such as data science, mathematics, statistics, R programming, and Python. Predictive analytics is a topic in which he has both professional and teaching experience. Having solved practical problems in his consulting practice using the Python tools for predictive analytics and the topics of predictive analytics are part of a more general course on data science with Python that he teaches online.
