Selection: Data Science

Author: Grigory Sapunov
Course
Description
Dates
21
Obtainingdata
Obtainingdata
Learn how to gather, clean, and manage data from a variety of sources. This is the third course in the Johns Hopkins Data Science Specialization. Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from...
1 June 2015, 4 weeks
1 June 2015, 4 weeks
6 July 2015, 4 weeks
3 August 2015, 4 weeks
7 September 2015, 4 weeks
5 October 2015, 4 weeks
2 November 2015, 4 weeks
7 December 2015, 4 weeks
Favored by 4 people
22
Exploratorydataanalysis
Exploratorydataanalysis
Learn the essential exploratory techniques for summarizing data. This is the fourth course in the Johns Hopkins Data Science Specialization. This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help...
1 June 2015, 4 weeks
1 June 2015, 4 weeks
6 July 2015, 4 weeks
3 August 2015, 4 weeks
7 September 2015, 4 weeks
5 October 2015, 4 weeks
2 November 2015, 4 weeks
7 December 2015, 4 weeks
Favored by 5 people
23
Datascientiststoolbox
Datascientiststoolbox
Johns Hopkins University
Mathematics, Statistics and Data Analysis
Gb Paid Free
Course added: 22 January 2014
Get an overview of the data, questions, and tools that data analysts and data scientists work with. This is the first course in the Johns Hopkins Data Science Specialization. In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview...
1 June 2015, 4 weeks
22 June 2015, 4 weeks
6 July 2015, 4 weeks
3 August 2015, 4 weeks
7 September 2015, 4 weeks
5 October 2015, 4 weeks
2 November 2015, 4 weeks
7 December 2015, 4 weeks
Favored by 4 people
24
Developingdataproducts
Developingdataproducts
Learn the basics of creating data products using Shiny, R packages, and interactive graphics. This is the ninth course in the Johns Hopkins Data Science Specialization. A data product is the production output from a statistical analysis. Data products automate complex analysis tasks or use technology...
1 June 2015, 4 weeks
6 July 2015, 4 weeks
3 August 2015, 4 weeks
7 September 2015, 4 weeks
5 October 2015, 4 weeks
2 November 2015, 4 weeks
7 December 2015, 4 weeks
Favored by 6 people
25
Databases
Databases
Jennifer Widom
Stanford
Computer Science Engineering & Technology
Gb Free
Course added: 25 January 2014
Learn about Databases, one of the most prevalent technologies underlying internet and computing applications today. Overview About This Course "Introduction to Databases" was one of Stanford's inaugural three massive open online courses in the fall of 2011 and was offered again in early 2013. January...
6 January 2014
Favored by 1 person
26
6.00.2x_computational_thinking_course_tile262x136_verified
6.00.2x_computational_thinking_course_tile262x136_verified
An introduction to using computation to understand real-world phenomena. About this Course 6.00.2x is aimed at students with some prior programming experience in Python and a rudimentary knowledge of computational complexity. We have chosen to focus on breadth rather than depth. The goal is to provide...
21 October 2014, 9 weeks
Favored by 2 people
27
Logo
Logo
Н.Г. Загоруйко, Ю.А. Аникин, Е.Н. Павловский, И.А. Борисова, А.О. Зырянов
ИНТУИТ
Computer Science
Ru Free
Course added: 1 March 2014
Курс представляет возможность познакомиться с основными понятиями в области аналитической обработки больших данных. В нем изложены основы машинного обучения, визуализации и хранения больших данных. По результатам изучения курса читатель сможет переводить проблемы предметной области на язык технологи...
Free schedule
28
Databases_tile
Databases_tile
Jennifer Widom
Stanford
Computer Science
Gb Free
Course added: 30 May 2014
Details to be announced
Favored by 1 person
29
Mmds_logo2
Mmds_logo2
Stanford University
Computer Science
Gb Free
Course added: 18 August 2014
This class teaches algorithms for extracting models and other information from very large amounts of data. The emphasis is on techniques that are efficient and that scale well. We introduce the participant to modern distributed file systems and MapReduce, including what distinguishes good MapReduce algorithms...
31 January 2015, 7 weeks
12 September 2015, 7 weeks
Favored by 2 people
30
Dreamstime_l_21055560
Dreamstime_l_21055560
Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. Data science...
7 October 2015, 8 weeks
Favored by 3 people
31
Coursera_logo
Coursera_logo
This is an intensive, advanced summer school (in the sense used by scientists) in some of the methods of computational, data-intensive science. It covers a variety of topics from applied computer science and engineering, and statistics, and it requires a strong background in computing, statistics, and...
2 September 2014, 2 weeks
Favored by 1 person
32
Ampcamp4-logo
Ampcamp4-logo
The exercises we cover today will have you working directly with the Spark specific components of the AMPLab’s open-source software stack, called the Berkeley Data Analytics Stack (BDAS).
Free schedule
Favored by 1 person
33
Logisticregression-logo
Logisticregression-logo
This course provides theoretical and practical training on the increasingly popular logistic regression model, which has become the standard analytical method for use with a binary response variable. This Applied Logistic Regression course provides theoretical and practical training for epidemiologists...
Details to be announced
Favored by 1 person
34
Appliedregression-logo
Appliedregression-logo
Regression modeling is the standard method for analysis of continuous response data. This course provides theoretical and practical training in statistical modeling with particular emphasis on linear and multiple regression. Statistical modeling is a fundamental element of analysis for statisticians...
Details to be announced
Favored by 2 people
35
Introduction-to-big-data-with-apache-spark_378x225
Introduction-to-big-data-with-apache-spark_378x225
Anthony D. Joseph
UC BerkeleyX
Computer Science
Gb Free
Course added: 29 December 2014
Learn how to apply data science techniques using parallel programming in Apache Spark to explore big (and small) data. Organizations use their data for decision support and to build data-intensive products and services, such as recommendation, prediction, and diagnostic systems. The collection of skills...
1 June 2015, 5 weeks
Favored by 3 people
36
Scalable-machine-learning_378x225
Scalable-machine-learning_378x225
Ameet Talwalkar
UC BerkeleyX
Computer Science Mathematics, Statistics and Data Analysis
Gb Free
Course added: 29 December 2014
Learn the underlying principles required to develop scalable machine learning pipelines and gain hands-on experience using Apache Spark. Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability and optimization. Learning algorithms...
29 June 2015, 5 weeks
Favored by 4 people
37
6d09a6d0-6f0d-42e6-91ad-5b0b3499951e-ec69fee15b60.small
6d09a6d0-6f0d-42e6-91ad-5b0b3499951e-ec69fee15b60.small
Learn how to use Hadoop technologies in Microsoft Azure HDInsight to process big data. More and more organizations are taking on the challenge of analyzing big data. This course teaches you how to use the Hadoop technologies in Microsoft Azure HDInsight to build batch processing solutions that cleanse...
1 October 2019
Favored by 1 person
38
77bc8f62-8fbf-411f-9dc3-13069d00a506-b2f8769fa3c9.small
77bc8f62-8fbf-411f-9dc3-13069d00a506-b2f8769fa3c9.small
Dinesh Kumar
IIMBx
Mathematics, Statistics and Data Analysis
Gb Paid Free
Course added: 14 October 2015
Master the tools of predictive analytics in this statistics based analytics course. Decision makers often struggle with questions such as: What should be the right price for a product? Which customer is likely to default in his/her loan repayment? Which products should be recommended to an existing customer...
30 January 2020
Favored by 1 person
39
F89c9ea8-b0ad-4c0c-8347-1b61dbd25125-51fcebc76a9c.small
F89c9ea8-b0ad-4c0c-8347-1b61dbd25125-51fcebc76a9c.small
Filip Schouwenaars, Jonathan Sanito
Microsoft
Computer Science Mathematics, Statistics and Data Analysis
Gb Paid Free
Course added: 17 December 2015
The ability to analyze data with Python is critical in data science. Learn the basics, and move on to create stunning visualizations. Python is a very powerful programming language used for many different applications. Over time, the huge community around this open source language has created quite a...
1 October 2019
Favored by 1 person
40
Advanced_distributed_machine_learning_with_spark_cs125x_378x225
Advanced_distributed_machine_learning_with_spark_cs125x_378x225
Learn how to develop and deploy distributed machine leaning pipelines and gain the expertise to write efficient, scalable code in Apache Spark.  Building on the core ideas presented in Distributed Machine Learning with Spark, this course covers advanced topics for training and deploying large-scale...
2 November 2016, 4 weeks
Favored by 2 people

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