Data Science

 

 


 

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E.g., 2017-01-25
E.g., 2017-01-25
E.g., 2017-01-25
Feb 1st 2017

Digital images of earth’s surface produced by remote sensing are the basis of modern mapping. They are also used to create valuable information products across a spectrum of industries. This free online course is for everyone who is interested in applications of earth imagery to increase productivity, save money, protect the environment, and even save lives.

Average: 3.8 (5 votes)
Jan 30th 2017

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.

Average: 7.7 (6 votes)
Jan 30th 2017

This course will introduce the learner to the basics of the python programming environment, including how to download and install python, expected fundamental python programming techniques, and how to find help with python programming questions. The course will also introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the DataFrame as the central data structure for data analysis.

Average: 7.3 (4 votes)
Jan 30th 2017

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies.

Average: 5.8 (9 votes)
Jan 30th 2017

A data product is the production output from a statistical analysis. Data products automate complex analysis tasks or use technology to expand the utility of a data informed model, algorithm or inference. This course covers the basics of creating data products using Shiny, R packages, and interactive graphics. The course will focus on the statistical fundamentals of creating a data product that can be used to tell a story about data to a mass audience.

Average: 3.3 (12 votes)
Jan 30th 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 30th 2017

In this course you will learn how to create models for decision making. We will start with cluster analysis, a technique for data reduction that is very useful in market segmentation. You will then learn the basics of Monte Carlo simulation that will help you model the uncertainty that is prevalent in many business decisions.

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Jan 30th 2017

Making predictions is not enough! Effective data scientists know how to explain and interpret their results, and communicate findings accurately to stakeholders to inform business decisions. Visualization is the field of research in computer science that studies effective communication of quantitative results by linking perception, cognition, and algorithms to exploit the enormous bandwidth of the human visual cortex. In this course you will learn to recognize, design, and use effective visualizations.

Average: 8.6 (5 votes)
Jan 30th 2017

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

Average: 5.9 (18 votes)
Jan 30th 2017

This course explores Excel as a tool for solving business problems. In this course you will learn the basic functions of excel through guided demonstration. Each week you will build on your excel skills and be provided an opportunity to practice what you’ve learned. Finally, you will have a chance to put your knowledge to work in a final project.

Average: 3 (6 votes)
Jan 30th 2017

Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. 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.

Average: 6.5 (6 votes)
Jan 30th 2017

The use of Excel is widespread in the industry. It is a very powerful data analysis tool and almost all big and small businesses use Excel in their day to day functioning. This course is designed to give you a working knowledge of Excel with the aim of getting to use it for more advance topics in Business Statistics.

Average: 6.5 (6 votes)
Jan 30th 2017

Want to understand your data network structure and how it changes under different conditions? Curious to know how to identify closely interacting clusters within a graph? Have you heard of the fast-growing area of graph analytics and want to learn more? This course gives you a broad overview of the field of graph analytics so you can learn new ways to model, store, retrieve and analyze graph-structured data. After completing this course, you will be able to model a problem into a graph database and perform analytical tasks over the graph in a scalable manner. Better yet, you will be able to apply these techniques to understand the significance of your data sets for your own projects.

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Jan 30th 2017

This course will expose you to the data analytics practices executed in the business. We will explore such key areas of data analytics as the analytical process, how data is created, stored, and accessed, and how the organization works with data and creates the environment in which analytics can flourish.

Average: 5 (1 vote)
Jan 30th 2017

Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective.

Average: 8 (1 vote)
Jan 30th 2017

Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems.

Average: 7.9 (8 votes)
Jan 30th 2017

Case Studies: Finding Similar Documents. A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover?

Average: 8 (4 votes)
Jan 30th 2017

Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for pattern-based classification and some interesting applications of pattern discovery.

No votes yet
Jan 30th 2017

How do Java programs deal with vast quantities of data? Many of the data structures and algorithms that work with introductory toy examples break when applications process real, large data sets. Efficiency is critical, but how do we achieve it, and how do we even measure it? In this course, you will use and analyze data structures that are used in industry-level applications, such as linked lists, trees, and hashtables.

Average: 6.3 (4 votes)
Jan 30th 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)

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