One of the main challenges for programmers learning Data Science and Machine learning is the amount of Mathematics involved in it, particularly in deep learning and neural network training. When I first started exploring deep learning, Maths came as an obstacle. Even though I was an excellent Maths student in my college, I still lack behind in Statistics, Probability, and Calculus involved while learning Data Science, and that's why I decided to refresh my knowledge and re-learn Statistics and Maths for Data Science. We also live in a world of Big data, and someone needs to make sense of all this data, and that's a demand for Data scientists is growing, but it's not a natural field to jump in. Most of the Data scientists I have met hold a Ph.D. and really good at their Maths and Statistics skills.
Even though you can learn most of Data Science and Machine learning concepts through online courses like Machine Learning by Andrew Ng, which solves the Maths problem for you; and, allow you to entirely focus on deep learning theories; you would still need to refresh your mathematics and statistics concepts which you may have studied before in school or colleges.
When you get into a real job solving real problems, not knowing Statistics, Mathematics, and Probability will not work as an excuse in a real job where you need to come up with your own adaptions to solve the unique problem you have in your hand, and that's why I suggest you brush your statistics and mathematics skills once you get hold of Machine learning fundamentals.
I have been postponing to learn Maths and Statistics for a long time, but last weekend I thought to let's start and see how it goes. I already had a couple of courses recommended to me by some knowledgeable chaps gone through this path before, and I also had my own shortlisted classes, which I am going to share with you today.
I am still learning, but these are some of the best online courses to learn Statistics and Maths for Data Science-based upon recommendations and reviews, and you should definitely check them if you need to brush up on your Mathematics, Probability or Statistics skill. It also helps in Data Science interviews; here many interviewers check your grip on these subjects.
The course will also teach you how to plot different types of data and fundamentals like calculating correlation and covariance and calculating measures of central tendency, asymmetry, and variability, etc.
You will also learn how to work with different types of data and distributions, understand the mechanics of regression analysis, and learn the concepts needed for data science, even with Python and R.
Overall, a perfect statistics course for a beginner Data Scientist. The course is also trusted by more than 34,298 students enrolled and has, on average, a 4.4 rating, which says a lot about its quality. I strongly recommend this statistics course to every data scientist.
The animation used in the course really makes it easy to understand complex Statistics and Mathematics concepts like probability.
This Specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.
The Specialization is a collection of 3 courses that will teach you Mathematics from the Machine learning point of view. You will refresh your knowledge of Linear Algebra and Calculus, along with learning other mathematical concepts that are important in Machine learning.
At the end of this Specialization, you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning and data science. Like other Coursera specializations, these are free courses if you just want to learn, but you need to pay a subscription fee if you need a certification or wish to do quizzes, assignments, and assessments.
You will also learn some about analyzing data, including mean, median, and mode, plus range and IQR and box-and-whisker plots and Hypothesis testing like inferential statistics, significance level, type I and II errors, test statistics, and p-values.
Overall, one of the most comprehensive courses to learn Probability and Statistics in a short time. The course contains more than 11 hours of watching material and also comes with 400+ practice questions to test your knowledge.
Talking about social proof, it's one of the best statistics courses in Udemy and already trusted by more than 16,462 students enrolled. It also has, on average, 4.6 ratings from 1,888 rating participants, which is fantastic. Big thanks to instructor Krista King for creating this awesome course.
You will also learn about statistical inference like Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions and, more importantly, communicate statistical results correctly. If you love R and want to be great at data analysis, this course can help you out.
If you are using R for Data Science, then this course is really great for you, but if you a Python guy like me, there are better choices available.
By the way, if you find Coursera specialization and certifications useful then I suggest you join the Coursera Plus, a great subscription plan from Coursera which gives you unlimited access to their most popular courses, specialization, professional certificate, and guided projects. It cost around $399/year but it's completely worth your money as you get unlimited certificates.
The course will also teach you many exploratory data analysis techniques like numeric summary statistics and basic data visualization.
You will also learn how to install R and RStudio (free statistical software) and use these tools for Data analysis on lab exercises and a final project. Overall a great course to learn the basics of statistics and probability.
Btw, you would need a Pluralsight membership to get access to this course, which costs around $29 per month or $299 annually (14% discount). It's more like Netflix for Software Developers, and Since learning is an essential part of our job, Pluralsight membership is a great way to stay ahead of your competition.
They also provide a 10-day free trial without any commitment, which is a great way to not just access this course for free but also to check the quality of courses before joining Pluralsight.
That's all about some of the best online courses to learn Statistics, Mathematics, and Probability for Data Science and Machine Learning. Good knowledge in these areas goes a long way in analyzing and making sense of Big data you will need to do as part of your job. A small effort building these foundations or revising it goes a long way to become the successful Data Scientist or Data Engineer you always wanted to be.
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Thanks for reading this article so far. If you like these best Mathematics and Statistics courses, then please share them with your friends and colleagues. If you have any questions or feedback, then please drop a note.
P.S. - If you are keen to learn more about Data Science and Machine Learning and just want to do one thing at this moment, go join the Data Science A-Z: Real-life Data Science course by Kirill Eremenko on Udemy. You won't regret your decision.
Even though you can learn most of Data Science and Machine learning concepts through online courses like Machine Learning by Andrew Ng, which solves the Maths problem for you; and, allow you to entirely focus on deep learning theories; you would still need to refresh your mathematics and statistics concepts which you may have studied before in school or colleges.
When you get into a real job solving real problems, not knowing Statistics, Mathematics, and Probability will not work as an excuse in a real job where you need to come up with your own adaptions to solve the unique problem you have in your hand, and that's why I suggest you brush your statistics and mathematics skills once you get hold of Machine learning fundamentals.
I have been postponing to learn Maths and Statistics for a long time, but last weekend I thought to let's start and see how it goes. I already had a couple of courses recommended to me by some knowledgeable chaps gone through this path before, and I also had my own shortlisted classes, which I am going to share with you today.
I am still learning, but these are some of the best online courses to learn Statistics and Maths for Data Science-based upon recommendations and reviews, and you should definitely check them if you need to brush up on your Mathematics, Probability or Statistics skill. It also helps in Data Science interviews; here many interviewers check your grip on these subjects.
5 Best Online Course to learn Statistics and Mathematics for Data Science, Machine Learning, and Deep Learning in 2024
Without wasting any more of your time, here is my list of some of the best courses to learn Statistics and Mathematics for Data Science and Machine Learning.1. Statistics for Data Science and Business Analysis
This is one of the best courses to learn the fundamentals of Statistics, not just for Data scientists but for anyone who needs to use statistics for data analysis. In this course, you will learn to efficiently analyze data, formulate hypotheses, and generally reason about what the big set of data is telling you.The course will also teach you how to plot different types of data and fundamentals like calculating correlation and covariance and calculating measures of central tendency, asymmetry, and variability, etc.
You will also learn how to work with different types of data and distributions, understand the mechanics of regression analysis, and learn the concepts needed for data science, even with Python and R.
Overall, a perfect statistics course for a beginner Data Scientist. The course is also trusted by more than 34,298 students enrolled and has, on average, a 4.4 rating, which says a lot about its quality. I strongly recommend this statistics course to every data scientist.
The animation used in the course really makes it easy to understand complex Statistics and Mathematics concepts like probability.
2. Mathematics for Machine Learning Specialization [Coursera Best Course]
For a lot of higher-level courses in Machine Learning and Deep Learning, you will find a need to refresh the basics in mathematics and statistics like probability. These are the concepts you may have studied before in school or university, but which were taught in another context, or not very intuitively, such that you struggle to relate it to how it's used in Computer Science.This Specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.
The Specialization is a collection of 3 courses that will teach you Mathematics from the Machine learning point of view. You will refresh your knowledge of Linear Algebra and Calculus, along with learning other mathematical concepts that are important in Machine learning.
At the end of this Specialization, you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning and data science. Like other Coursera specializations, these are free courses if you just want to learn, but you need to pay a subscription fee if you need a certification or wish to do quizzes, assignments, and assessments.
3. Become a Probability and Statistics Master
This is one of the most focused courses on Probability and Statistics together. You will learn everything from Probability and Statistics like Data distribution like mean, variance, and standard deviation, and normal distributions and z-scores, Data Visualization including bar graphs, pie charts, Venn diagrams, histograms, and dot plots, and more.You will also learn some about analyzing data, including mean, median, and mode, plus range and IQR and box-and-whisker plots and Hypothesis testing like inferential statistics, significance level, type I and II errors, test statistics, and p-values.
Overall, one of the most comprehensive courses to learn Probability and Statistics in a short time. The course contains more than 11 hours of watching material and also comes with 400+ practice questions to test your knowledge.
Talking about social proof, it's one of the best statistics courses in Udemy and already trusted by more than 16,462 students enrolled. It also has, on average, 4.6 ratings from 1,888 rating participants, which is fantastic. Big thanks to instructor Krista King for creating this awesome course.
4. Statistics with R Specialization [Coursera]
This is another awesome resource for Data scientists on Coursera. In this Specialization, you will learn about how to analyze and visualize data in the R programming language and create reproducible data analysis reports.You will also learn about statistical inference like Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions and, more importantly, communicate statistical results correctly. If you love R and want to be great at data analysis, this course can help you out.
If you are using R for Data Science, then this course is really great for you, but if you a Python guy like me, there are better choices available.
By the way, if you find Coursera specialization and certifications useful then I suggest you join the Coursera Plus, a great subscription plan from Coursera which gives you unlimited access to their most popular courses, specialization, professional certificate, and guided projects. It cost around $399/year but it's completely worth your money as you get unlimited certificates.
5. Statistics Foundations: Understanding Probability and Distributions
This is an excellent online course to learn to sample and explore data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods and discuss how such practices can impact the scope of inference.The course will also teach you many exploratory data analysis techniques like numeric summary statistics and basic data visualization.
You will also learn how to install R and RStudio (free statistical software) and use these tools for Data analysis on lab exercises and a final project. Overall a great course to learn the basics of statistics and probability.
Btw, you would need a Pluralsight membership to get access to this course, which costs around $29 per month or $299 annually (14% discount). It's more like Netflix for Software Developers, and Since learning is an essential part of our job, Pluralsight membership is a great way to stay ahead of your competition.
They also provide a 10-day free trial without any commitment, which is a great way to not just access this course for free but also to check the quality of courses before joining Pluralsight.
That's all about some of the best online courses to learn Statistics, Mathematics, and Probability for Data Science and Machine Learning. Good knowledge in these areas goes a long way in analyzing and making sense of Big data you will need to do as part of your job. A small effort building these foundations or revising it goes a long way to become the successful Data Scientist or Data Engineer you always wanted to be.
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Thanks for reading this article so far. If you like these best Mathematics and Statistics courses, then please share them with your friends and colleagues. If you have any questions or feedback, then please drop a note.
P.S. - If you are keen to learn more about Data Science and Machine Learning and just want to do one thing at this moment, go join the Data Science A-Z: Real-life Data Science course by Kirill Eremenko on Udemy. You won't regret your decision.
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