Doing Data Science with Python

This course shows you how to work on an end-to-end data science project including processing data, building & evaluating machine learning model, and exposing the model as an API in a standardized approach using various Python libraries.
Course info
Rating
(241)
Level
Beginner
Updated
Dec 28, 2017
Duration
6h 24m
Table of contents
Course Overview
Course Introduction
Setting up Working Environment
Extracting Data
Exploring and Processing Data - Part 1
Exploring and Processing Data - Part 2
Exploring and Processing Data - Part 3
Building and Evaluating Predictive Models – Part 1
Building and Evaluating Predictive Models – Part 2
Description
Course info
Rating
(241)
Level
Beginner
Updated
Dec 28, 2017
Duration
6h 24m
Description

Do you want to become a Data Scientist? If so, 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 an end-to-end data science project cycle. You'll learn everything 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.

Course FAQ
Course FAQ
Is Python good for data science?

Yes! Python's robust libraries are ideal for manipulating data and it is a relatively easy language to learn for data analyst beginners!

Is Python better than R for data science?

Python and R are both great programming languages geared towards data science. However, Python is often easier for beginners, and is a more general purpose language with easy to read syntax. Python is better for raw data scraping, while R is more useful in analyzing already scrubbed data.

Will we be using Python libraries?

Yes. We will go over various standard Python libraries such as NumPy, Scikit-Learn, Pandas, Pickle, Matplotlib, and Flask to help with extracting, cleaning, and processing data, and building machine learning models.

What is data science with Python?

Simply put, it is a combination of statistical and machine learning techniques through the use of Python programming to help analyze and interpret data.

Are there prerequisites to this course?

Some previous exposure to Python or its libraries may come in handy, but is not required. Just come with an interest in data science.

Why learn data science?

Data science is a super popular field these days. Through data science we can find meaningful and valuable insights, and provide data-driven evidence to help organizations be more efficient and successful.

About the author
About the author

Abhishek Kumar is a data science consultant, author, and Google Developers Expert (GDE) in machine learning. He holds a master’s degree from the University of California, Berkeley, and has been featured in the "Top 40 under 40 Data Scientist" list.

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