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Analytics Insight Presents the list of Top Free Online Courses in Statistics and Data Analysis
Would you like to understand data science statistics without undergoing a time-consuming and pricey class? There’s great news! Using solely free online resources, you may understand basic topics such as probability, Bayesian thinking, and statistical deep learning.
This article will show you the statistical thinking skills you’ll need for data science along with the top free online courses for Statistics and Data Analysis. It will give you a tremendous leg up on other budding data scientists who are attempting to get by without it. After all, after you’ve understood how to programme, it can be appealing to jump right into using machine learning programmes. It’s fine if you want to start with real-world projects at first. However, you should never, ever disregard statistics and probability concepts. It’s necessary if you want to advance as a data scientist.
Statistics Needed for Data Science
Statistics is a vast field with several applications in a variety of fields. The science of the collection, analysis, interpretation, presentation and organising of data is statistics, according to Encyclopaedia. As a result, it should come as no shock that data scientists require statistical knowledge.
Data analysis, for particular, necessitates at the very least descriptive statistics and probability theory. These ideas will assist you in making better company decisions based on data. Probability distributions, statistical significance, hypothesis testing, and regression are all important issues.
Artificial learning also necessitates knowledge of Bayesian thinking. The act of upgrading beliefs when new evidence is gathered is known as Bayesian thinking, and it’s at the heart of many machine learning frameworks. Conditional probability, priors and posteriors, and maximum probability are all important topics. Wouldn’t fret if those terms seem like jargon to you. When you get your hands grimy and actually learn, everything will sound familiar.
How to Learn Statistics for Data Science?
You’ve surely observed that “the self-starter route to learning X” frequently includes skipping classroom instruction in favour of “doing stuff.”
It’s no different when it comes to mastering statistics for data science.
In fact, we’ll be tackling important statistical ideas by programming them with coding! This will be a lot of fun, we promise. If you don’t have a formal math background, you’ll find that this method is far more natural than having a hard time figuring out difficult equations. It works by stimulating through each calculation’s logical phases. If you have a strong math background, this method will assist you in putting theory into practice while also providing some enjoyable programming difficulties.
You’ll be prepared to undertake harder machine learning issues and popular real-world data science applications after finishing these three levels. The three steps to studying the statistics and probability needed for data science are as follows:
Step 1: Core Statistics Concepts
It’s a good idea to start learning statistics for data science by examining how it will be applied.
Now let us look at some real-world studies or implementations that you might encounter as a data scientist:
1. Experimental design: Your firm is launching a new line of products, but it will only be available in brick-and-mortar locations. You’ll need to create an A/B test that accounts for geographic variances. You’ll also have to figure out just how many outlets you’ll need to test in order to get statistically relevant findings.
2. Regression modelling: Your enterprise needs to be able to forecast consumption for certain product lines in its outlets more accurately. Both understocking and overstocking are costly. You’re thinking of creating a set of regularized regression models.
3. Data transformation: You’re evaluating a number of machine learning model options. Several of them include assumptions about input data probability distributions, and you must be able to spot them so that you can either convert the data correctly or determine when the presumptions may be eased.
Step 2: Bayesian Thinking
The disagreement between Bayesians and frequentists is one of the philosophical arguments in statistics. While mastering statistics for data science, the Bayesian side is more important.
Frequentists, in an essence, solely employ probability to model sampling processes. This means that they only allocate probability to data that they’ve already gathered.
Step 3: Intro to Statistical Machine Learning
After you’ve grasped essential principles and Bayesian thinking, there’s no better way to learn statistics for data science than by experimenting with statistical machine learning models.
The sciences of statistics and machine learning are inextricably intertwined, and “statistical” machine learning is the predominant method of current machine learning.
In this stage, you’ll create a few machine learning models from the ground up. This will assist you in gaining a genuine knowledge of their dynamics.
Top Free Courses
1. Coursera (Duke University): Statistics with R Specialisation
- Time Period: 10 weeks
- Background knowledge: No prior programming expertise is necessary; just simple mathematics skills are required.
2. Udacity (Stanford University): Intro to Statistics
- Time Period: 8 weeks
- Background knowledge: No prior experience is necessary; an introductory course is required
3. Stanford University: Statistical Learning
- Time Period: 10 weeks
- Background knowledge: A basic understanding of statistics, linear algebra and computing are necessary.
4. Leada: Introduction to R
- Time Period: Self-Paced
- Background knowledge: No prior experience is necessary; an introductory course is required
5. Udacity (San Jose State University): Statistics: The Science of Decisions
- Time Period: Self-Paced; approximately 4 months
- Background knowledge: Basic proportions (fractions, decimals, and percentages), negative values, fundamental algebra (solving equations), and exponential and square roots.
6. Saylor: Introduction to Probability Theory
- Time Period: Self-Paced
- Background knowledge: Topics in single-variable and multivariate calculus, numerical analysis, and differential equations, or equivalents, must be completed.
7. EDX (Columbia University): Statistical Thinking for Data Science and Analytics
- Time Period: 5 weeks
- Background knowledge: No prior experience is necessary; an introductory course is required
8. EDX (University of Texas): Statistics Using R
- Time Period: 6 weeks
- Background knowledge: No prior experience is necessary; an introductory course is required
9. Caltech: Learning from Data
- Time Period: Self-Paced
- Background knowledge: No prior experience is necessary; an introductory course is required
Conclusion
We hope that we were able to provide you with the best free courses for Statistics and Data Analysis. They are ranked from 1 to 9 with short details which will help you pick your courses according to your convenience. So, hurry up and get yourself a course now!