Coursera is a wonderful resource that has aided millions of students all throughout the world. We can only give for such a large service they provide by paying for their accreditation, which isn't a significant issue. Coursera courses, on the other hand, are not worth the money they cost.
Usually its anything from two to three weeks. Not more than that. Do potential employers take certificates from Coursera seriously or should I never showcase them on my CV? I would put them on your CV.
The courses usually say how long they take and the estimate effort per week. I just checked Ng's course and it doesn't for some reason. Naturally this changes from person to person, depending on your previous knowledge and if you are a fast learner or not. It is very rare that I don't use the full estimate of the course.
It is very rare that I don't use the full estimate of the course. So a course with 4-8 hours per week usually takes me 8 hours per week. If it is a 4 week course I expect a 32 hour total effort.
Statistical inference and underlying concepts are abstract, which makes them difficult in an introductory statistics course from the point of the learner.
Statistical Inference ProcedureBegin with a theory.Create a research hypothesis.Operationalize the variables.Recognize the population to which the study results should apply.Formulate a null hypothesis for this population.Accumulate a sample from the population and continue the study.More items...
Description. Statistical inference is the process of using data analysis to draw conclusions about a population or process beyond the existing data. Inferential statistical analysis infers properties of a population by testing hypotheses and deriving estimates.
Step 1: Learn Descriptive Statistics. Udacity course on descriptive statistics from Udacity. ... Step 2: Learn Inferential statistics. Undergo the course on Inferential statistics from Udacity. ... Step 3: Predictive Model (Learning ANOVA, Linear and Logistic Regression on SAS)
The two main branches of statistics are descriptive statistics and inferential statistics. Both of these are employed in scientific analysis of data and both are equally important for the student of statistics.
The purpose of statistical inference is to estimate this sample to sample variation or uncertainty.
There are two broad areas of statistical inference: statistical estimation and statistical hypothesis testing.
Inferential statistics help us draw conclusions from the sample data to estimate the parameters of the population. The sample is very unlikely to be an absolute true representation of the population and as a result, we always have a level of uncertainty when drawing conclusions about the population.
A theory of statistical inference was developed by Charles S. Peirce in "Illustrations of the Logic of Science" (1877–1878) and "A Theory of Probable Inference" (1883), two publications that emphasized the importance of randomization-based inference in statistics.
If you choose to learn statistics on your own and devote six to eight hours a day to your studies, you can become a master statistician in just a couple of months. However, if you decide to enroll in a college degree program, it will take anywhere from two to four years, depending on your degree.
On average, you will need to study around 500 hours of lectures to learn data science adequately. For around 100 hours, you can understand the basics of data science. The numbers can vary depending on your knowledge of programming, calculus, and statistics.
Statistics is a whole field of study in itself, so if you think you can learn enough in one sitting then you are gravely mistaken, I am afraid. However, you don't need to be a trained mathematician to understand some pretty sophisticated statistical tools and method.
As with any other online course, Coursera courses can be used in many ways; for instance: Use them to better equip yourself for a current job or to improve your candidacy for a new job. The knowledge acquired from these courses can be used to start or improve a business. Use them to get a raise or a promotion.
Learning How to Learn is just as critically acclaimed and targets just as wide of an audience, but the instructors are very specific with their estimate: “…about 3 hours of video, 3 hours of exercises, 3 hours of bonus material”. They’re able to offer this estimate because most of the topics begin from square one.
If you are attending/watching all the lectures on time along with reading & practising the study material. (Problem-solving) then it will take 6 - 7 hours to prepare a good or above average assignment.
Machine Learning doesn’t attempt to estimate the number of hours it takes each week because the course does all three of the above to a great extent. Prof. Ng wanted to make ML accessible to almost anyone, so it has a wide audience.
Paid Coursera courses are cheaper than college/university programs. To receive a certificate, you'll have to complete all the coursework, including a capstone course and a project-based assessment. This allows you to get hands-on experience, making paid courses better than their free counterparts.
Some of the courses are synchronous, meaning they are only available at a given time. All in all, Coursera is one of the best MOOC providers out there.