Udacity
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The Introduction to Data Science class will survey the foundational topics in data science, namely Data Manipulation, Data Analysis with Statistics and Machine Learning, Data Communication with Information Visualization, and Data at Scale -- Working with Big Data. The class will focus on breadth and present the topics briefly instead of focusing on a single topic in depth. This will give you the opportunity to sample and apply the basic techniques of data science.
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This course introduces students to the real world challenges of implementing machine learning based trading strategies including the algorithmic steps from information gathering to market orders. The focus is on how to apply probabilistic machine learning approaches to trading decisions. We consider statistical approaches like linear regression, KNN and regression trees and how to apply them to actual stock trading situations.
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Taking this course will provide you with a basic foundation in Java syntax, which is the first step towards becoming a successful Java developer. YouÕll learn how computers make decisions and how Java keeps track of information through variables and data types. YouÕll learn to create conditional statements, functions, and loops to process information and solve problems. YouÕll even learn to use IntelliJ, a Java IDE (Integrated Development Environment) that professional developers use, to build, compile, and debug your code. These are foundational programming skills, and mastering them is a must for all aspiring programmers.
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This course will introduce you to some of the most powerful programming concepts in Java, including: objects, inheritance and collections. You will learn how to use these object-oriented programming concepts in code examples, discover how these concepts are used in applications that require user input, and understand the benefits of mastering these concepts in Java.By the end of the course, you will create your own game, understand how and when to apply foundational Java concepts, and will gain a deeper understanding of the tools and logic that professional Java developers use everyday.
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This course will cover the design and analysis of A/B tests, also known as split tests, which are online experiments used to test potential improvements to a website or mobile application. Two versions of the website are shown to different users - usually the existing website and a potential change. Then, the results are analyzed to determine whether the change is an improvement worth launching. This course will cover how to choose and characterize metrics to evaluate your experiments, how to design an experiment with enough statistical power, how to analyze the results and draw valid conclusions, and how to ensure that the the participants of your experiments are adequately protected.
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Machine Learning is a graduate-level course covering the area of Artificial Intelligence concerned with computer programs that modify and improve their performance through experiences. The first part of the course covers Supervised Learning, a machine learning task that makes it possible for your phone to recognize your voice, your email to filter spam, and for computers to learn a bunch of other cool stuff. In part two, you will learn about Unsupervised Learning. Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? Such answers can be found in this section! Finally, can we program machines to learn like humans? This Reinforcement Learning section will teach you the algorithms for designing self-learning agents like us!
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Inferential statistics allows us to draw conclusions from data that might not be immediately obvious. This course focuses on enhancing your ability to develop hypotheses and use common tests such as t-tests, ANOVA tests, and regression to validate your claims.
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C++ For Programmers
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This course focuses on 'how' as opposed to 'what'. For example, in the lesson on functions, we do not teach what a function is, but rather how to create a function in C++. The lessons are taught by several different instructors who have used C++ in their professional careers, so students get to experience different perspectives.
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This course is a quick, fun introduction to using a relational database from your code, using examples in Python. You'll learn the basics of SQL (the Structured Query Language) and database design, as well as the Python API for connecting Python code to a database. You'll also learn a bit about protecting your database-backed web apps from common security problems. After taking this course, you'll be able to write code using a database as a backend to store application data reliably and safely.
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In this course, youÕll learn to use Structured Query Language (SQL) to extract and analyze data stored in databases. YouÕll first learn to extract data, join tables together, and perform aggregations. Then youÕll learn to do more complex analysis and manipulations using subqueries, temp tables, and window functions. By the end of the course, youÕll be able to write efficient SQL queries to successfully handle a variety of data analysis tasks.