LiveLesssons - Data Science Fundamentals Part 1 Free Download

LiveLesssons – Data Science Fundamentals Part 1
English | Size: 5.69 GB
Category: Programming

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 Airbnb listings
Where to find quality data sources and how to work with APIs programmatically
Strategies for parsing JSON and XML into a structured form
The basics of relational databases and how to use an ORM to interface with them in Python
Best practices of data validation, including common data quality checks

Who Should Take This Course

Aspiring data scientists looking to break into the field and learn the essentials necessary
Journalists, consultants, analysts, or anyone else who works with data and looking to take a programmatic approach to exploring data and conducting analyses
Quantitative researchers interested in applying theory to real projects and taking a computational approach to modeling.
Software engineers interested in building intelligent applications driven by machine learning
Practicing data scientists already familiar with another programming environment looking to learn how to do data science with Python
Course Requirements
Basic understanding of programming
Familiarity with Python and statistics are a plus

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LiveLesssons – Data Science Fundamentals Part 1

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