Doing Data Science with Python

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Created by: Abhishek Kumar

Produced in 2017

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Course Description

This course will equip you with concepts and tools that can bring you to speed and you can utilize the skills acquired in this course to work on any data science project in a standardized approach. This course, Doing Data Science with Python, follows a pragmatic approach to tackle end-to-end data science project cycle right from extracting data from different types of sources to exposing your machine learning model as API endpoints that can be consumed in a real-world data solution. This course will not only help you to understand various data science related concepts, but also help you to implement the concepts in an industry standard approach by utilizing Python and related libraries. First, you will be introduced to the various stages of a typical data science project cycle and a standardized project template to work on any data science project. Then, you will learn to use various standard libraries in the Python ecosystem such as Pandas, NumPy, Matplotlib, Scikit-Learn, Pickle, Flask to tackle different stages of a data science project such as extracting data, cleaning and processing data, building and evaluating machine learning model. Finally you'll dive into exposing the machine learning model as APIs. You will also go through a case study that will encompass the whole course to learn end-to-end execution of a data science project. By the end of this course, you will have a solid foundation to handle any data science project and have the knowledge to apply various Python libraries to create your own data science solutions.

Beginner Level

Time: 6h 30m

Instructor Details

Abhishek Kumar

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Abhishek Kumar is a data science consultant, author and speaker. He holds Master's degree from University of California, Berkeley. His focus area is machine learning & deep learning at scale. He is also a recipient of Hal Varian award for his work on deep learning at University of California, Berkeley. He helps large enterprises in utilizing their data assets by applying principles of data science using latest techniques. He also specializes in building and managing scalable data products from conceptualization to deployment phase.