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And data science is powered by the mathematical discipline, statistics.
Hence, learn statistics for data science to become a successful data scientist.
Read on to move a step ahead in your data science journey.
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.
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.
Also, for forecasting, statistics help to predict specific outcomes from a data model.
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.
Yet another important statistical function helps data scientists segregate a population.
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.
The video spans 7 hours and 12 minutes, explaining various vital functions of statistics for data science.
CrashCourse
CrashCourse Statistics from the YouTube channelCrashCourseis 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 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 byFree Code Camp.
You will also be able to conclude gig datasets.
Khan Academy
Yet another elaborate online learning content on statistics is this YouTube video fromKhan 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.
There are 50 lecture videos covering essential statistics functions like study designs, distributions, Z-Scores, etc.
There are 84 video lectures in this playlist.
You will learn interesting statistical functions like bias, variance, multiple regression, and logistic regression.
It helps you get a glimpse of the skill and know the efforts needed to acquire it successfully.
To learn statistics for data science, it’s possible for you to use thisUdacity coursethe same way.
You will learn the required statistical functions for data science like:
The course is open to everyone.
Basic knowledge of algebra will be helpful in performing the practice tasks.
Data scientists use this statistical function in many ways.
you’re able to learn the entire concept free by checking out thisUdemy 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 could go over the course as often as you want to memorize and understand the concepts.
This free-of-charge course is also self-paced training material so that you might change the deadlines according to your schedule.
Key course content is:
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 fromKhan 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
ThisCoursera coursehas 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:
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.
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 might complete the training in 4 months.
Some notable lessons are:
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’re gonna wanna practice sample problems and questions to test your confidence.
Hence, it’s possible for you to check out thisUdemy courseto get both ideas and sample questions.
Some of the key course topics are:
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 oughta learn how to implement statistics using Python coding.
ThisDataCampskill track can help you learn statistics from Pythons perspective.
Amazing course content:
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.
R is the most popular programming language for data visualization graphics and statistical computing.
The book is available as an eBook for Kindle devices and paperback for those who like physical books.
The author explicitly describes which part of statistics is necessary for data scientists and which part is not.
Naked Statistics: Amazon
This book teaches you the indispensable tools of statistics for data science.
Fresh graduates can learn that as outlined above in this article.
