Coursera Statistics, Making Sense of Data: A applied Statistics course that teaches the complete pipeline of statistical analysis MIT: Statistical Thinking and Data Analysis: Introduction to probability, sampling, regression, common distributions, and inference. While R is the de facto standard for performing statistical analysis, it has quite a high learning curve and there are other areas of data science for which it is not well suited. To avoid learning a new language for a specific problem domain, we recommend trying to perform the exercises of these courses with Python and its numerous statistical libraries. You will find that much of the functionality of R can be replicated with NumPy, @SciPy, @Matplotlib, and @Python Data Analysis Library Books Well-written books can be a great reference (and supplement) to these courses, and also provide a more independent learning experience. These may be useful if you already have some knowledge of the subject or just need to fill in some gaps in your understanding: O'Reilly Think Stats: An Introduction to Probability and Statistics for Python programmers Introduction to Probability: Textbook for Berkeley's Stats 134 class, an introductory treatment of probability with complementary exercises.
Udacity Data Science Courses, learn data science from industry experts at Facebook, Cloudera, MongoDB, Georgia Tech, and more. Udemy Data Science-related courses UH Data Mining Hypertextbook and Course, a new model for the data mining course, which includes a significant project on real-world data, and surveying the learning styles of students. Video lectures from conferences, workshops and the scientific lectures in the areas of machine learning, data and text mining, and semantic web.
Learning new skills to enhance your abilities to do a task effectively can be a hectic schedule especially if you are an employee. It's hard to chase coaching or learning centers after spending 8-10 hours in the office per day. And when it comes to becoming technology-efficient specifically in the field of data science, you need to have the best qualification, handy experiences to get better job opportunities in this high in-demand profession. To ease out people's hectic schedules without compromising with the quality of the education, online platforms like Coursera, Udemy, eDX and many more have a collection of data science certification and courses. Adding a touch of extra bonanza, these courses are free of cost.
Machine learning was the fifth and latest guide. And now I'm back to conclude this series with even more resources. For each of the five major guides in this series, I spent several hours trying to identify every online course for the subject in question, extracting key bits of information from their syllabi and reviews, and compiling their ratings. My goal was to identify the three best courses available for each subject and present them to you. The 13 supplemental topics -- like databases, big data, and general software engineering -- didn't have enough courses to justify full guides. But over the past eight months, I kept track of them as I came across them. I also scoured the internet for courses I may have missed. For these tasks, I turned to none other than the open source Class Central community, and its database of thousands of course ratings and reviews.
I grew up an artsy nerdy kid, singing in choir, playing in band, as comfortable with a soldering iron, with fixing or hacking an old radio or electronic organ, as with chord progressions or improvising harmonies on the fly. In high school, I sang in every choir, played in every band, and did theater and speech. I also kept a keen and interested eye toward technology, especially music technology. My original goal in going to conservatory in 1973 was to become a band/choir teacher, double-majoring in trombone and voice, with education and techniques courses for choir and band certification.