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E.g., 2016-10-25
E.g., 2016-10-25
E.g., 2016-10-25
Oct 31st 2016

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: 4.9 (7 votes)
Oct 31st 2016

Case Studies: Analyzing Sentiment & Loan Default Prediction
In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank.

Average: 10 (1 vote)
Oct 31st 2016

Case Study - Predicting Housing Prices
In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets.

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Oct 31st 2016

This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results.

Average: 6.3 (9 votes)
Oct 24th 2016

You will find this course exciting and rewarding if you already have a background in statistics, can use R or another programming language and are familiar with databases and data analysis techniques such as regression, classification, and clustering. However, it contains a number of recitals and R Studio tutorials which will consolidate your competences, enable you to play more freely with data and explore new features and statistical functions in R. With this course, you’ll have a first overview on Strategic Business Analytics topics. We’ll discuss a wide variety of applications of Business Analytics.

Average: 8.5 (2 votes)
Jul 12th 2016

Learn how to use math and science to solve everyday problems and better prepare yourself to continue studying science in high school and beyond. Have you ever wondered how you can apply math and science skills to real life? Do you wish you could go beyond what you've learned in the classroom? This science course will advance your knowledge as we unpack some important scientific thinking skills using real-world examples.

Average: 4 (3 votes)