Great Learning via Great Learning Academy
This Free Data Science Foundations course roffers your knowledge on the introduction to the subject and gives you insights into the different phases of its life cycle. The course covers topics about various tasks carried out in Data Science and different programming languages that are compatible to work with to accommodate the tasks efficiently, and Machine Learning, contributing to the dynamic behavior of machines and making significant associations with DS. The analytics landscape is another significant component within an organization, which you will learn in the latter part of the course, to understand workflow and asset distribution thoroughly. You will have to take an assessment to test your gain on the subject. The course also provides you with study materials for your reference at any given point after enrolling in it.
After this free, self-paced, beginner's guide to Data Science Foundations, you can embark on your Data Science career with the professional Post Graduate certificate and learn various concepts in depth with millions of aspirants across the globe!
This section gives you various examples to help you understand Data Science. It explains how you decide on a place for the vacation, how the weather is predicted, and sales during a particular time in a year using data science.
Data Science life cycle revolves around data acquisition, preprocessing, ML algorithms, pattern evaluation, knowledge representation, and analytical strategies to predict and proffer insights, which we shall learn in this module, into the procedures to yield the best results.
Data mining tasks include classification, prediction, association, clustering, and summarization. This module explains anomaly detection, continues with matching data points and explains the concept with a real-life example.
Machine learning is a learning method to process raw data based on the previously trained model for similar input data. This section explains how machines understand the patterns and the features through which it tags every data.
Most commonly used programming languages used in Data Science are R (for statistical computation) and Python (including MATLAB library), which we will know why in this section. We will also look into the famous libraries in each of these languages for Data Science.
Analytics landscape is used to generate insights from data using simple manipulation, presentation, calculation, and visualization of data. We shall look into this concept with demonstrated examples in this section.
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