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
(216)
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
(216)
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 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.

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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