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Master Computer Science on Coursera



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Jan 16th 2017

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: 6.5 (4 votes)
Jan 16th 2017

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.

Average: 7.5 (4 votes)
Jan 16th 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 16th 2017

This class provides an introduction to the Python programming language and the iPython notebook. This is the third course in the Genomic Big Data Science Specialization from Johns Hopkins University.

Average: 2.9 (7 votes)
Jan 16th 2017

The Raspberry Pi is a small, affordable single-board computer that you will use to design and develop fun and practical IoT devices while learning programming and computer hardware.

Average: 7.8 (12 votes)
Jan 16th 2017

Behind every mouse click and touch-screen tap, there is a computer program that makes things happen. This course introduces the fundamental building blocks of programming and teaches you how to write fun and useful programs using the Python language. A computer program is a set of instructions for a computer to follow, just as a recipe is a set of instructions for a chef. Laptops, kitchen appliances, MP3 players, and many other electronic devices all run computer programs. Programs have been written to manipulate sound and video, write poetry, run banking systems, predict the weather, and analyze athletic performance. This course is intended for people who have never seen a computer program.

Average: 8 (1 vote)
Jan 16th 2017

Why do the prices of some companies’ stocks seem to move up and down together while others move separately? What does portfolio “diversification” really mean and how important is it? What should the price of a stock be? How can we discover and exploit the relationships between equity prices automatically?

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Jan 16th 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 16th 2017

Are you interested in learning how to program (in Python) within a scientific setting? This course will cover algorithms for solving various biological problems along with a handful of programming challenges helping you implement these algorithms in Python. Each of the four weeks in the course will consist of two required components. First, an interactive textbook provides Python programming challenges that arise from real biological problems.

Average: 7 (1 vote)
Jan 13th 2017

This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.

Average: 6.1 (34 votes)
Jan 10th 2017

Learn everything you need to know to get started building a MongoDB-based app. This course will go over basic installation, JSON, schema design, querying, insertion of data, indexing and working with the Python driver. We will also cover working in sharded and replicated environments. In the course, you will build a blogging platform, backed by MongoDB. A brief Python introduction is included in the course.

Average: 4.9 (9 votes)
Jan 10th 2017

A new and updated introduction to computer science as a tool to solve real-world analytical problems using Python 3.5. This course is the first of a two-course sequence: Introduction to Computer Science and Programming Using Python, and Introduction to Computational Thinking and Data Science. Together, they are designed to help people with no prior exposure to computer science or programming learn to think computationally and write programs to tackle useful problems.

Average: 7.3 (12 votes)
Jan 9th 2017

This class provides a series of Python programming exercises intended to explore the use of numerical modeling in the Earth system and climate sciences. The scientific background for these models is presented in a companion class, Global Warming I: The Science and Modeling of Climate Change. This class assumes that you are new to Python programming (and this is indeed a great way to learn Python!), but that you will be able to pick up an elementary knowledge of Python syntax from another class or from on-line tutorials.

Average: 5.8 (5 votes)
Jan 9th 2017

This two-part course builds upon the programming skills that you learned in our Introduction to Interactive Programming in Python course. We will augment those skills with both important programming practices and critical mathematical problem solving skills. These skills underlie larger scale computational problem solving and programming.

Average: 6.5 (16 votes)
Jan 9th 2017

Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part class is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to computational problems.

Average: 6 (6 votes)
Jan 9th 2017

This two-part course is designed to help students with very little or no computing background learn the basics of building simple interactive applications. Our language of choice, Python, is an easy-to learn, high-level computer language that is used in many of the computational courses offered on Coursera. To make learning Python easy, we have developed a new browser-based programming environment that makes developing interactive applications in Python simple. These applications will involve windows whose contents are graphical and respond to buttons, the keyboard and the mouse.

Average: 5.6 (23 votes)
Jan 9th 2017

This course will introduce the core data structures of the Python programming language. We will move past the basics of procedural programming and explore how we can use the Python built-in data structures such as lists, dictionaries, and tuples to perform increasingly complex data analysis.

Average: 4.6 (9 votes)
Jan 9th 2017

Are you interested in predicting future outcomes using your data? This course helps you do just that! Machine learning is the process of developing, testing, and applying predictive algorithms to achieve this goal. Make sure to familiarize yourself with course 3 of this specialization before diving into these machine learning concepts. Building on Course 3, which introduces students to integral supervised machine learning concepts, this course will provide an overview of many additional concepts, techniques, and algorithms in machine learning, from basic classification to decision trees and clustering.

Average: 6.9 (7 votes)
Jan 9th 2017

This course will show how one can treat the Internet as a source of data. We will scrape, parse, and read web data as well as access data using web APIs. We will work with HTML, XML, and JSON data formats in Python.

Average: 7.1 (10 votes)
Jan 9th 2017

In this course, you will develop and test hypotheses about your data. You will learn a variety of statistical tests, as well as strategies to know how to apply the appropriate one to your specific data and question. Using your choice of two powerful statistical software packages (SAS or Python), you will explore ANOVA, Chi-Square, and Pearson correlation analysis. This course will guide you through basic statistical principles to give you the tools to answer questions you have developed. Throughout the course, you will share your progress with others to gain valuable feedback and provide insight to other learners about their work.

Average: 8.5 (4 votes)

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