This 30-part course consists of tutorials, quizzes, hands-on assignments and real-world projects to learn data science, as well as advanced python tools for data science.
Trillions of gigabytes of data is being produced yearly, and the number is still growing exponentially. It is estimated that for every person, 1.7 megabytes of data will be produced every second by 2020.
Our society is increasingly becoming data dependent. Data is only a raw material and extracting information from it requires further work. Data Science helps us make sense of data.
- Understand what data science is and its key components - programming, data, statistics and probability, machine learning, big data technologies.
- Learn advanced Python data science libraries such as Pandas, NumPy, Matplotlib, etc.
- Understand what machine learning is and learn some popular machine learning algorithms.
- Implement some machine learning algorithms and apply them to solve problems like image classification, sentiment classification and provide movie recommendations.
- Understand the end-to-end workflow of a typical data science project (using Pandas, NumPy, Matplotlib, etc).
- Understand databases and systems used to store, manage and retrieve data.
- Understand and implement the map-reduce framework used to perform computation in a large-scale distributed setting.
- Implement a real-world project using data science techniques.
Prerequisites: Python and Linear Algebra, Statistics and Probability (Review).
Related course: Machine Learning.
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