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Programming for Data Science with R
by Josh Bernhard , Derek Steer , Juno Lee , Richard Kalehoff , Karl Krueger- 0.0
3 Months
The Programming for Data Science Nanodegree program offers you the opportunity to learn the most important programming languages used by data scientists today. Get your start into the fascinating field of data science and learn R, SQL, terminal, and git with the help of experienced instructors. Learn the programming fundamentals required for a career in data science....
Data Science Project: MATLAB for the Real World
by Michael Reardon , Brandon Armstrong , Erin Byrne , Adam Filion , Heather Gorr , Maria Gavilan-Alfonso , Matt Rich , Isaac Bruss- 4.8
Approx. 13 hours to complete
In the capstone project, you will apply the skills learned across courses in the Practical Data Science with MATLAB specialization to explore, process, analyze, and model data. Practical Data Science with MATLAB Tasks for Exploring and Cleaning the Taxi Data Data Science Report Requirements Storytelling in Data Science Conclusion of Practical Data Science with MATLAB...
Data Product Manager
by JJ Miclat , Vaishali Agarwal , Anne Rynearson- 0.0
3 Months
Leverage market data to amplify product development. Learn how to apply data science techniques, data engineering processes, and market experimentation tests to deliver customized product experiences. Finally, learn techniques for evaluating the data from live products, including how to design and execute various A/B and multivariate tests to shape the next iteration of a product....
$399
Data Visualization and D3.js
by Ryan Orban , Chris Saden , Jonathan Dinu- 0.0
Approx. 7 weeks
Learn by doing! You will analyze existing data visualization and create new ones to learn about the field. lesson 3 Design Principles Which chart type to use for a data set. Learn about bias in the data visualization process and learn how to add context. Learn how to create a bubble map for the World Cup data set....
Free
Communicating Data Science Results
by Bill Howe- 3.6
Approx. 8 hours to complete
You will learn the foundational limitations of using technology to protect privacy and the codes of conduct emerging to guide the behavior of data scientists. You will also learn the importance of reproducibility in data science and how the commercial cloud can help support reproducible research even for experiments involving massive datasets, complex computational infrastructures, or both....
Introduction to Big Data
by Ilkay Altintas , Amarnath Gupta- 4.6
Approx. 17 hours to complete
This course is for those new to data science and interested in understanding why the Big Data Era has come to be. * Provide an explanation of the architectural components and programming models used for scalable big data analysis. Data Science 101 Foundations for Big Data Systems and Programming Programming Models for Big Data...
Spatial Data Science and Applications
by Joon Heo- 4.4
Approx. 12 hours to complete
Additionally, this course could make learners realize the value of spatial big data and the power of open source software's to deal with spatial data science problems. 1 Introduction to Spatial Data Science Understanding Spatial Data Science Solution Structures of Spatial Data Science Problems Four Disciplines for Spatial Data Science and Applications Spatial Data Science Problems...
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Exploratory Data Analysis with MATLAB
by Erin Byrne , Michael Reardon , Maria Gavilan-Alfonso , Brandon Armstrong , Nikola Trica , Cris LaPierre , Adam Filion , Heather Gorr- 4.8
Approx. 19 hours to complete
In this course, you will learn to think like a data scientist and ask questions of your data. Introduction to the Data Science Workflow Practical Data Science with MATLAB MATLAB as a Data Science Tool Overview of the Data Science Workflow Summary of Module 1: The Data Science Workflow Quiz 1: Introduction to Data Science...
Data Manipulation at Scale: Systems and Algorithms
by Bill Howe- 4.3
Approx. 20 hours to complete
The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales. Describe common patterns, challenges, and approaches associated with data science projects, and what makes them different from projects in related fields....
Introduction to Data Science in Python
by Christopher Brooks- 4.5
Approx. 31 hours to complete
The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. Describe common Python functionality and features used for data science...