The demand for data scientists, analysts, and big data experts is strong, and educational institutions are scrambling to meet the demand.
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Along with ZipCode, there are a plethora of free online courses available today -- some from well-known and respected universities, others from popular providers of massive open online classes (MOOC) -- offering help with the skills you need to succeed.
All of the courses featured here are free of charge. This means you can get a taste of data science and analytics without committing to a big capital investment in your education. If you are looking to add analytics and data science to your skill set, here are some great places to start.
Big Data Basics
This free online course from Udemy provides about an hour's worth of video and instruction for beginners interested in learning more about the technologies that make up the big data ecosystem. The course covers basic definitions of all the technologies, and cites real-world use-cases for Hadoop.
Looking for something a little more advanced? The Hadoop Starter Kit course is also designed for beginners, but requires basic Linux and Java knowledge. Students get free access to a multi-node Hadoop training cluster so they can try out what they learn in a real multi-node distributed environment.
This course from Udemy comes from a daylong workshop presented in June 2013 by the authors of the book Lean Analytics. Designed for entrepreneurs, this program helps young companies use analytics to succeed. The course covers what makes a good metric, how to match the data you track to your stage of growth, and how to change the culture in organizations of all sizes.
Getting Started With Spark
This beginner's course is among a range of offerings from Spark distribution company Databricks. The courses are conducted in conjunction with MOOC provider EdX. Additional topics include distributed machine learning, big data analysis, and more.
Apache Spark can be used as a complementary technology to Hadoop, and it helps organizations looking to harness real-time streaming data and analytics.
Looking for a primer on open source statistical programming language R? This course, from Udemy, offers lessons on how to download R and packages in R, how to use basic functions, and how to code lines. It takes about two hours to work through the videos and other materials in this self-paced course, plus additional time to complete the exercises.
Getting Started With Python
While R is one part of the data science toolkit, Python is becoming another essential development tool. This free course from Udemy will give you a taste of the basics. You'll learn to write real-world, non-complex programs with Python. You'll learn how to set up a Python environment with associated libraries, and how to load and use data from CSV and TXT files into Python, among other things.
This Stanford Engineering Everywhere online course provides an introduction to machine learning and statistical pattern recognition. According to the course description, topics covered include supervised learning, unsupervised learning, learning theory, reinforcement learning, and adaptive control.
The course also covers recent applications of machine learning, such as robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and Web data processing. Students should have basic knowledge of computer science principles and be able to write a substantial computer program. Students should also have familiarity with basic probability theory and basic linear algebra.
Probability And Statistics
Need a refresher on probability and statistics before you tackle that machine learning class? Carnegie Mellon University's Open Learning Initiative offers this course on how to choose, generate, and properly interpret appropriate descriptive and inferential methods of statistical reasoning. The course only requires knowledge of basic algebra, and gives students practice with one of several supported statistics packages, including Microsoft Excel, Minitab, R, TI calculator, or StatCrunch.
MIT Open Courseware offers online, self-paced instruction from courses taught at the school during previous semesters. Topics include algorithms and data structures, data mining, and more. The class introduces students to basic knowledge representation, problem solving, and the learning methods of AI. By the end of the course, students should be able to develop intelligent systems by assembling solutions to concrete computational problems, according to the course description.
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