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In Career Last updated: January 27, 2023
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Data science facilitates the profitable use of petabytes of data by smart, businesses, financial institutions, healthcare centers, and more. And data science is powered by the mathematical discipline, statistics. Hence, learn statistics for data science to become a successful data scientist.

### Why Should You Learn Statistics for Data Science?

Websites and apps are collecting enormous volumes of data each second. But they do not make any sense until there is a pattern. Statistics help you to make sense of raw data by finding a pattern.

Once data scientists get big datasets, they apply descriptive statistics to transcribe the surveys or observations into something that provides insight.

Then, data scientists use inferential statistics to analyze small parts of the entire dataset to relate the findings with the dataset’s source, like a population in a country.

Thus, you need to learn statistics to answer data science questions like:

• The vital features of any dataset or survey data
• Ways to design product development strategy
• Setting up the performance metrics and their tables
• Predicting expected or common outcomes from a project
• Retaining valid data and discarding noise

### Importance of Statistics in Data Science

#### Data Cleansing

Statistics are powerful to validate if the data was collected according to the survey plan. Statistical methods also help data scientists to eliminate noise, falsified data, irrelevant data, and redundant data. Thus, that structured data becomes ready as an input for any machine learning program.

#### Analyzing Data

In data analysis, you must apply statistical functions like mean, median, mode, variance, and distributions. Also, for forecasting, statistics help to predict specific outcomes from a data model.

Statistics is the key to understanding data, improving the data model, and why the dataset has generated specific values.

#### Classification Methods

Logistic regression is one such method that data scientists use excessively. They apply this statistical function to forecast qualitative responses based on patterns observed in the data model.

#### Clustering

Yet another important statistical function helps data scientists segregate a population. For example, data scientists can apply clustering to segregate different age groups of customers and run targeted ads to minimize cost and maximize the conversion rate.

Now, find below some essential learning resources for data science.

### Free Courses and Video Resources

The followings are some free courses that are available on YouTube. Also, you will find some top edTech platforms offering free learning content.

## Great Learning

Start learning about the need for statistics in data science by watching this Great Learning YouTube video course. The video spans 7 hours and 12 minutes, explaining various vital functions of statistics for data science.

For example, it explains the relation between machine learning and statistics, types of datasets, correlation, probability theory, binomial distribution, and more.

## CrashCourse

CrashCourse Statistics from the YouTube channel CrashCourse is an excellent source for data science aspirants to learn statistics. There is 44 video content explaining all the statistical functions exclusive to data science and machine learning.

You need to watch the videos in order of their appearance to learn the lessons in an organized way. You may want to sit with pen and paper to practice the statistical problems discussed in the videos.

## Free Code Camp

Want to know what a university course on statistics for data science looks like? Watch this quality statistics course video on YouTube made available by Free Code Camp

Once you go through the lesson diligently, you will learn the skills to collect, summarize, organize, and interpret data. You will also be able to conclude gig datasets.

Yet another elaborate online learning content on statistics is this YouTube video from Khan Academy

It is an organized list of video lectures on various topics of statistics. There are 67 video lectures freely available to access as much as you want.

## Statistics by Marin

Marin goes by the YouTube channel MarinStatsLectures-R Programming & Statistics and offers an exhaustive lecture series on statistics for data science.

There are 50 lecture videos covering essential statistics functions like study designs, distributions, Z-Scores, etc.

## 365 Data Science

This 365 Data Science YouTube video on Introduction to Statistics covers the required functions of statistics that are needed for data scientists.

Skewness, variance, levels of measurement, numerical variables, etc., are some notable statistical topics the lecture will cover.

## StatQuest

Learn machine learning by applying statistical functions side by side by watching this free YouTube lecture on ML from StatQuest

There are 84 video lectures in this playlist. You will learn interesting statistical functions like bias, variance, multiple regression, and logistic regression.

## Udacity

It is a smart step to start learning a new skill by going through some free resources. It helps you get a glimpse of the skill and know the efforts needed to acquire it successfully. To learn statistics for data science, you can use this Udacity course the same way.

You will learn the required statistical functions for data science like:

• Probability
• Estimation
• Discovering relationships in data
• Regression analysis
• Inference
• Normal distribution and outliers

The course is open to everyone. Basic knowledge of algebra will be helpful in performing the practice tasks.

## Introduction to Bayesian statistics: Udemy

Bayesian statistics is a statistical inference method to explore the probability of a hypothesis. Data scientists use this statistical function in many ways. You can learn the entire concept free by checking out this Udemy course.

You will learn Bayesian statistics in 4 succinct sections containing 14 lectures. It will take about 1 hour and 18 minutes to complete the course. You can go over the course as often as you want to memorize and understand the concepts.

## Introduction to Statistics: Coursera

It is a Stanford University course taught by a faculty of the same university and delivered online via Coursera. This free-of-charge course is also self-paced training material so that you can change the deadlines according to your schedule.

Key course content is:

• Descriptive statistics for data exploration
• Collecting and sampling data
• Probability theory
• Binomial distribution
• Regression analysis

It will take about 15 hours to complete all the lessons. Finally, you will earn a certificate for successful completion.

## Statistics and probability: Khan Academy

Want to learn statistics and probability for data science for free? You must try out this gamified learning content from Khan Academy. The course content includes the fundamentals of probability and statistics for data science.

There are 16 lessons in this content. In the end, there is a course challenge to test your skills and knowledge of the lessons taught. Furthermore, the course delivers lessons via video lectures. Thus, it is a self-paced course suitable for on-the-job professionals.

## Statistics for Data Science with Python: Coursera

This Coursera course has been made available by IBM. It is a highly objective course to learn the building block principles of statistics for data science. Notable course topics are:

• Data gathering
• Descriptive statistics for data summarization
• Visualizing and displaying data
• Probability distributions
• hypothesis testing
• Analysis of variance or ANOVA
• Correlation and regression analysis

The estimated course completion time is 14 hours. Not to worry if you are a working professional since it is a complete online and self-paced course.

## Mathematics for Machine Learning Specialization: Coursera

Mathematics is inseparable from machine learning, artificial intelligence, and data science. You can learn exactly what you need to become a successful professional in the above niches by signing up for this Coursera course

The Imperial College of London is offering this course through Coursera, the leading online courses platform. It is a 3 training course delivered by four veteran instructors. At 4 hours per week, you can complete the training in 4 months.

### Paid Online Courses

If you are also looking for exhaustive learning content covering the entire discipline, here are some paid learning resources for you:

## Statistics & Mathematics for Data Science & Data Analytics: Udemy

If you want to learn probability theory and statistics to apply business analysis and data science functions, you must check out this Udemy course. Some notable lessons are:

• Root mean square deviation (RMSE)
• Mean absolute error (MAE)
• Hypothesis testing
• Null-hypothesis significance testing or p-value
• Type I & type II error
• Descriptive statistics
• Probability theory
• Multiple Linear Regression

It is a self-paced online training course with 91 lectures spanning nine sections. The estimated course content length is 11 hours and 24 minutes.

## Become a Probability & Statistics Master: Udemy

Learning the theories is not enough. You need to practice sample problems and questions to test your confidence. Hence, you can check out this Udemy course to get both ideas and sample questions. Some of the key course topics are:

• Essential data visualization tools like pie charts, bar graphs, Venn diagrams, dot plots, histograms, and more
• Statistical distribution of data using Z-Score, standard deviation, normal distribution, variance, and mean
• Regression analysis
• Data sampling
• Hypothesis testing

The course consists of 10 sections and 141 lecture videos. At the end of each section, there is also a practice test. At the end of the overall course, there is a final exam.

## Statistics Fundamentals with Python: DataCamp

Python is the vital programming language for data science. Hence, you need to learn how to implement statistics using Python coding. This DataCamp skill track can help you learn statistics from Python’s perspective. Amazing course content:

• Summary statistics and probability
• Statistical models such as logistics and linear regression
• Data sampling techniques
• Conclude from an extensive dataset by performing a hypothesis test

The entire skill track consists of 5 courses. Each course is of 4 hours in length. Hence, it would take 20 hours to complete the skill track.

## Statistics Fundamentals with R: DataCamp

Yet another skill track from DataCamp helps you to learn statistics for data science using the R language. R is the most popular programming language for data visualization graphics and statistical computing. Key skill track topics are:

• Introduction to statistics in R
• Introduction to regression analysis in R
• Data sampling in R
• Intermediate regression in R
• Hypothesis testing in R

The 5 courses on this skill track are 4 hours each, and the total completion time is 20.

## Essential Math for Data Science: Amazon

This book is an excellent source to find all the required mathematics topics like linear algebra, calculus, probability, and not to mention statistics. The book explains and shows the application of neural networks, linear regression, and logistic regression in data science projects.

You will also learn to derive statistical significance and interpret p-values from an extensive dataset by applying hypothesis testing and descriptive statistics. The book is available as an eBook for Kindle devices and paperback for those who like physical books.

## Practical Statistics for Data Scientists: Amazon

Learn practical statistics for data science and its implementation using Python and R programming language effortlessly from this Amazon book. The author explicitly describes which part of statistics is necessary for data scientists and which part is not.

The book will cover key statistics functions like random sampling, regression analysis, classification techniques, and machine learning methods. You can own this handy book as a paperback copy, spiral-bound copy, or digital copy for Kindle.

## Naked Statistics: Amazon

This book teaches you the indispensable tools of statistics for data science. You will get a brief and easy-to-understand clarification of statistical concepts like regression analysis, correlation, inference, and more.

By studying and understanding various needs of the learners, Amazon has made this book available in formats like Kindle, hardcover, MP3 compact disk, paperback, and Audiobook.

### Conclusion

If you are a mid-level or expert data scientist, you already know the importance of statistics for data science. Fresh graduates can learn that as outlined above in this article.

Knowing which statistics lessons are required for data science, you will invest a lot of months learning the whole of statistics. You can find this valuable knowledge by exploring any or all of the above resources to become a data scientist.

You may also be interested in reinforcement learning for your ML models.

• Tamal is a freelance writer at Geekflare. After completing his MS in Science, he joined reputed IT consultancy companies to acquire hands-on knowledge of IT technologies and business management. Now, he’s a professional freelance content… read more