Data Science Fundamentals Part 1: Learning Basic Concepts, Data Wrangling, and Databases with Python / Dinu, Jonathan.

Format:

Video

Author/Creator:

Dinu, Jonathan, author.

Series:

LiveLessons

Language:

English

Subjects (All):

Python (Computer program language).

Application software--Development.

Application software.

Machine learning.

Application program interfaces (Computer software).

Genre:

Electronic videos.

Physical Description:

1 online resource (1 video file, approximately 20 hr., 54 min.)

Edition:

1st edition

Other Title:

Learning basic concepts, data wrangling, and databases with Python

Place of Publication:

Addison-Wesley Professional, 2017.

System Details:

video file

Summary:

20 Hours of Video Instruction Data Science Fundamentals LiveLessons teaches you the foundational concepts, theory, and techniques you need to know to become an effective data scientist. The videos present you with applied, example-driven lessons in Python and its associated ecosystem of libraries, where you get your hands dirty with real datasets and see real results. Description If nothing else, by the end of this video course you will have analyzed a number of datasets from the wild, built a handful of applications, and applied machine learning algorithms in meaningful ways to get real results. And along the way you learn the best practices and computational techniques used by a professional data scientist. More specifically, you learn how to acquire data that is openly accessible on the Internet by working with APIs. You learn how to parse XML and JSON data to load it into a relational database. About the Instructor Jonathan Dinu is an author, researcher, and most importantly, an educator. He is currently pursuing a Ph.D. in Computer Science at Carnegie Mellon's Human Computer Interaction Institute (HCII), where he is working to democratize machine learning and artificial intelligence through interpretable and interactive algorithms. Previously, he founded Zipfian Academy (an immersive data science training program acquired by Galvanize), has taught classes at the University of San Francisco, and has built a Data Visualization MOOC with Udacity. In addition to his professional data science experience, he has run data science trainings for a Fortune 500 company and taught workshops at Strata, PyData, and DataWeek (among others). He first discovered his love of all things data while studying Computer Science and Physics at UC Berkeley, and in a former life he worked for Alpine Data Labs developing distributed machine learning algorithms for predictive analytics on Hadoop. Jonathan has always had a passion for sharing the things he has learned in the most creative ways he can. When he is not working with students, you can find him blogging about data, visualization, and education at hopelessoptimism.com or rambling on Twitter @jonathandinu. Skill Level Beginner What You Will Learn How to get up and running with a Python data science environment The essentials of Python 3, including object-oriented programming The basics of the data science process and what each step entails How to build a simple (yet powerful) recommendation engine for Air...

Participant:

Presenter, Jonathan Dinu.

Notes:

Online resource; Title from title screen (viewed August 31, 2017)

Title and publication information from resource description page (Safari, viewed September 14, 2017).

ISBN:

9780134660141

0134660145

OCLC:

1003645627