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Data Science: Statistics and Machine Learning Specialization

Even while you can acquire most of the Data Science and Machine Learning concepts through online courses that solve the math problem for you and allow you to focus exclusively on deep learning theories, you will still need to refresh your maths and statistics concepts from school or college.

Not knowing statistics and mathematics will not work as an excuse in a real job where you must come up with your own adaptation to solve the unique problem you have in your hands, which is why I recommend brushing up on your statistics and mathematics skills once you grasp Machine Learning fundamentals.

Top 5 Statistics and Math Courses for Data Science in 2022

The quantity of mathematics involved in Data Science and Machine Learning, particularly in deep learning and neural network training, is one of the key hurdles for programmers learning it. We also live in a world of Big Data, which necessitates someone to make sense of it all. Demand for Data Scientists is booming, but it’s not a natural field to enter. Most of the data scientists I’ve encountered have a Ph.D. and are quite proficient in math and statistics.

Without wasting any more of your time, here is a list of some of the greatest statistics and mathematics courses for Data Science and Machine Learning.

1. Statistics for Data Science and Business Analysis

This is one of the greatest courses for learning the principles of statistics, not only for data scientists but for everyone who wants to analyze data using statistics. This course will teach you how to evaluate data quickly, generate hypotheses, and reason about what the big data is telling you.

The course will also cover fundamentals such as calculating correlation and covariance, as well as measures of central tendency, asymmetry, and variability, among other topics.

2. Mathematics for Machine Learning Specialization

Many higher-level Machine Learning and Deep Learning courses will require you to review basic maths and statistics such as probability. These are topics you may have learned in school or university but were taught in a different context or not very intuitively, making it difficult for you to relate them to how they are applied in Computer Science.

This Specialization seeks to close that gap by bringing you up to speed on the underlying mathematics, allowing you to develop an intuitive understanding, and connecting it to Machine Learning and Data Science.

3. Become a Probability and Statistics Master

This is one of the most concentrated Probabilities and Statistics courses available. Data distribution, such as mean, variance, and standard deviation, normal distributions, and z-scores, as well as Data Visualization, such as bar graphs, pie charts, Venn diagrams, histograms, dot plots, and more, will all be covered in Probability and Statistics.

You’ll also learn about data analysis, such as mean, median, and mode, as well as range and IQR and box-and-whisker plots. Inferential statistics, significance levels, type I and II errors, test statistics, and p-values are all used in hypothesis testing.

4. Statistics with R Specialisation

This is just another fantastic online resource course for data scientists. In this Specialization, you will learn about how to analyze and visualize data in the R programming language and develop repeatable data analysis reports.

You’ll also study statistical inference, such as Bayesian statistical inference and modeling, in order to better comprehend natural phenomena and make data-driven decisions, as well as appropriately convey statistical results.

5. Statistics Foundations: Understanding Probability and Distributions

This is a great online course for learning how to sample and analyze data, as well as fundamental probability theory and Bayes’ rule. You’ll look at different sorts of sampling strategies and talk about how they affect the breadth of inference.

Many exploratory data analysis approaches, such as numeric summary statistics and basic data visualization, will be covered in this course.

Conclusion

Good expertise in these areas will help you analyze and make sense of the Big data you’ll be dealing with as part of your career.

Building or upgrading these foundations will go a long way toward helping you become the successful Data Scientist or Data Engineer you’ve always wanted to be. For more queries, you can review the Data Science Interview Questions and Answers.

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