Last but not least, this course will provide you with the foundation you need to succeed in later courses in the Data Science MicroMasters program. You will learn these tools all within the context of solving compelling data science problems.Īfter completing this course, you'll be able to find answers within large datasets by using python tools to import data, explore it, analyze it, learn from it, visualize it, and ultimately generate easily sharable reports.īy learning these skills, you'll also become a member of a world-wide community which seeks to build data science tools, explore public datasets, and discuss evidence-based findings. This course, part of the Data Science MicroMasters program, will introduce you to a collection of powerful, open-source, tools needed to analyze data and to conduct data science. But if you had access to a large dataset, would you be able to find the answers you seek? SweetViz offers an in-depth EDA (target analysis, comparison, feature analysis, correlation) and interactive EDA in two lines of code In addition, SweetViz allows you to compare two data sets, such as training and test data sets for your machine learning projects. Within this data are answers to compelling questions across many societal domains (politics, business, science, etc.). Top 15 Data Analysis Tools MonkeyLearn perform no-code text analysis RapidMiner build predictive analysis models KNIME create data science workflows. Exploratory Data Analysis (EDA) Using Python 3. In the information age, data is all around us. These are the basic libraries that transform Python from a general purpose programming language into a powerful and robust tool for data analysis and.
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