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E.g., 2017-01-21
E.g., 2017-01-21
E.g., 2017-01-21
Jan 23rd 2017

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 Specialisation. 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 of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, Github, R, and Rstudio.

Average: 4.1 (21 votes)
Jan 23rd 2017

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 the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.

Average: 5.4 (13 votes)
Jan 23rd 2017

This course is designed to impact the way you think about transforming data into better decisions. Recent extraordinary improvements in data-collecting technologies have changed the way firms make informed and effective business decisions. The course on operations analytics, taught by three of Wharton’s leading experts, focuses on how the data can be used to profitably match supply with demand in various business settings. In this course, you will learn how to model future demand uncertainties, how to predict the outcomes of competing policy choices and how to choose the best course of action in the face of risk. The course will introduce frameworks and ideas that provide insights into a spectrum of real-world business challenges, will teach you methods and software available for tackling these challenges quantitatively as well as the issues involved in gathering the relevant data.

Average: 10 (2 votes)
Jan 23rd 2017

Our world is rich with data sources, and technology makes data more accessible than ever before! To help ensure students are future ready to use data for making informed decisions, many countries around the world have increased the emphasis on statistics and data analysis in school curriculum–from elementary/primary grades through college. This course allows you to learn, along with colleagues from other schools, an investigation cycle to teach statistics and to help students explore data to make evidence-based claims.

Average: 9.5 (2 votes)
Jan 16th 2017

Copyright questions about different formats (data, images, music and video) can be especially difficult. Sometimes the law specifically distinguishes between these different formats, and in most cases there are media-specific considerations that impact a copyright analysis. In this course we will look at four different media, paying special attention to the unique issues for each one and the kinds of information that is important when making copyright decisions for each type of material.

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Jan 16th 2017

Data about our browsing and buying patterns are everywhere. From credit card transactions and online shopping carts, to customer loyalty programs and user-generated ratings/reviews, there is a staggering amount of data that can be used to describe our past buying behaviors, predict future ones, and prescribe new ways to influence future purchasing decisions. In this brand new course, four of Wharton’s top marketing professors will dive deeper into the key areas of customer analytics: descriptive analytics, predictive analytics, prescriptive analytics, and their application to real-world business practices including Amazon, Google, and Starbucks to name a few.

Average: 5.9 (9 votes)
Jan 16th 2017

Learn critical concepts and practical methods to support research data planning, collection, storage and dissemination.

Average: 9 (1 vote)
Oct 18th 2016

Este curso te ayudará a tomar el control sobre los datos, a darles sentido y, de esta manera, usarlos para mejorar tus decisiones.

Average: 1 (1 vote)
Sep 18th 2016

Introductory Machine Learning course covering theory, algorithms and applications. Our focus is on real understanding, not just "knowing."

Average: 6 (2 votes)
Sep 5th 2016

Learn about the role of data in a range of disciplines and about some fundamental tools for extracting knowledge from data. How can we answer questions about the world around us? How can we make decisions about what to do? Over the past years, more and more people have turned to data for help. Huge amounts of data are collected every day from millions of sources. This data has a lot to tell us! But data by itself is mute—it can only help us if we learn to make it speak and tell its story.

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Aug 8th 2016

Get a practical insight into big data analytics, and popular tools and frameworks for collecting, storing and managing data. Data is everywhere and can be obtained from many different sources. Digital data can be obtained from social media, images, audio recordings and sensors, and electronic data is quite often available as real-time data streams.

Average: 10 (1 vote)
Self-Paced until 26 Aug 2016

The course is addressed for secondary school students and teachers, who would like to fresh their research skills and for I and II year university students. During the 9 weeks long course, participants will learn how to collect and analyze data with the computer and to draw conclusions.

Average: 5.5 (2 votes)
May 17th 2016

Learn the best way to structure and represent data. Data structures provide a means to manage large amounts of data for use in databases and internet indexing services. Efficient data structures are key for designing efficient algorithms and obtaining maintainable software design. In this Computer Science course, you will start by learning basic data types, such as numbers, and gradually build a conceptual framework for organizing and managing efficient structures.

Average: 10 (1 vote)
Apr 20th 2016

Learn how to turn raw data into visual insights using Excel to help support business decisions. Struggling with data at work? Wasting valuable time working in multiple spreadsheets to gain an overview of your business? Find it hard to gain sharp insights from piles of data on your desktop? If you are looking to enhance your efficiency in the office and improve your performance by making sense of data faster and smarter, then this advanced data analysis course is for you.

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Apr 12th 2016

Through inspiring examples and stories, discover the power of data and use analytics to provide an edge to your career and your life. In the last decade, the amount of data available to organizations has reached unprecedented levels. Data is transforming business, social interactions, and the future of our society. In this course, you will learn how to use data and analytics to give an edge to your career and your life.

Average: 7.5 (2 votes)
Mar 15th 2016

Expand your use of the core design method from SPD1 to write well-crafted code that operates on more complex data like lists and trees.

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Mar 7th 2016

A computer science principles course intended to broaden participation in computing to non-traditional groups. Part 4 of 4.

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Jun 1st 2015

La información estadística oficial es un elemento esencial para el desarrollo y mejora de las condiciones de vida de la población, en la medida que permite sustentar el diseño y formulación de las políticas públicas y decisiones privadas. Conceptos tan básicos y actuales como el PIB, Renta Per Cápita, Déficit, Deuda… están en el orden del día de las noticias pero.

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May 12th 2015

Learn what is involved in using data wisely to build a culture of collaborative inquiry.

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Self Paced

Data is essential for improving outcomes for students. Whether it is informing improved instruction, empowering parents and communities, or helping policymakers make decisions and target resources, our education system needs data in order to continuously improve. In order to create a culture of trust that enables effective data use, policymakers and education professionals must ensure that the public has confidences that state and local leaders act to protect student data privacy. This self-paced course will discuss the value data brings to improve education, offer recommendations for addressing privacy concerns while promoting effective data use, and explore lessons learned from existing and emerging policies in education and other sectors.

Average: 2 (3 votes)