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Master Computer Science on Coursera



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Apr 17th 2017

Create simple systems that respond to and control the physical world using the Raspberry Pi and Python. This free online course will introduce you to Physical Computing, showing you how easy it is to create a system that responds to and controls the physical world, using computer programs running on the Raspberry Pi.

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Apr 7th 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: 5.9 (41 votes)
Apr 3rd 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.

Average: 5.3 (18 votes)
Apr 3rd 2017

This course aims to teach everyone the basics of programming computers using Python. We cover the basics of how one constructs a program from a series of simple instructions in Python.

Average: 7 (24 votes)
Apr 3rd 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: 6.5 (11 votes)
Apr 3rd 2017

Bem vindo ao curso de Introdução à Ciência da Computação destinado aos alunos regulares da Universidade de São Paulo e a todos os demais interessados em aprender não só a programar em Python mas também os conceitos básicos da Ciência Computação!

Average: 2 (1 vote)
Apr 3rd 2017

Not all programs are created equal. In this course, we'll focus on writing quality code that runs correctly and efficiently. We'll design, code and validate our programs and learn how to compare programs that are addressing the same task.

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Apr 3rd 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)
Apr 3rd 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: 6.6 (17 votes)
Apr 3rd 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: 6.3 (6 votes)
Apr 3rd 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)
Apr 3rd 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: 6.3 (9 votes)
Apr 3rd 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: 5.8 (25 votes)
Apr 3rd 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: 6.5 (11 votes)
Apr 3rd 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 (10 votes)
Apr 3rd 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 (8 votes)
Apr 3rd 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)
Apr 3rd 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 votes)
Apr 3rd 2017

This course will introduce students to the basics of the Structured Query Language (SQL) as well as basic database design for storing data as part of a multi-step data gathering, analysis, and processing effort. The course will use SQLite3 as its database. We will also build web crawlers and multi-step data gathering and visualization processes. We will use the D3.js library to do basic data visualization.

Average: 5.5 (26 votes)
Apr 3rd 2017

Whether being used to customize advertising to millions of website visitors or streamline inventory ordering at a small restaurant, data is becoming more integral to success. Too often, we’re not sure how use data to find answers to the questions that will make us more successful in what we do. In this course, you will discover what data is and think about what questions you have that can be answered by the data – even if you’ve never thought about data before. Based on existing data, you will learn to develop a research question, describe the variables and their relationships, calculate basic statistics, and present your results clearly. By the end of the course, you will be able to use powerful data analysis tools – either SAS or Python – to manage and visualize your data, including how to deal with missing data, variable groups, and graphs. Throughout the course, you will share your progress with others to gain valuable feedback, while also learning how your peers use data to answer their own questions.

Average: 8 (9 votes)

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