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Statistics for Data Science

Join this free course to be knowledgeable of the key concepts of Statistics for Data Science, Machine Learning, and Business Intelligence. Know the need for normal distribution, sampling, and hypothesis in data analytics practices

4.49
average rating

Ratings

Beginner

Level

1.5 Hrs

Learning hours

6.4K+
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Skills you’ll Learn

About this Course

The first lesson in this online course introduces you to the fundamental terminologies of statistics, including probability, distribution, hypotheses, and CLT (Central Limit Theorem), the basic statistics concepts for Data Science. The course addresses the hypothesis used to support or refute the statements for distribution after explaining the Normal distribution with examples. Using the Central Limit Theorem, you will subsequently gain a complete understanding of the Sampling Distribution concept. The instructor concludes by illustrating the theorem with the hypotheses. Enroll in this Statistics course for Data Science to learn the various theories, hypotheses, and theories and earn a certificate of completion.

 

Continue to learn in the Data Science domain after this free, beginner-level Statistics for Data Science course. The Great Learning platform provides advanced-level Data Science courses covering all the concepts in depth to benefit your career. 

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Course Outline

Probability - Meaning and Concepts

You will learn what probability means and its concepts in this module through some examples. The instructor will discuss what an experiment is with an example and define and thoroughly explain the formula to determine the likelihood of an event. Later, a diagram will assist you in comprehending the extreme probability values and mutually exclusive events.
 

Normal Distribution and its Example

This section explains normal distribution for continuous functions and the association between mean, median, and mode in a normal distribution with solved examples and also discusses its properties and practical applications. You will also understand density function and standard distribution with solved example problems.

Hypothesis Testing Outline

This section outlines a technique followed in inferential statistics called Hypothesis testing. It highlights confidence intervals, types of errors, specific hypothesis, and types of statistical procedures used. 
 

Concepts of Sampling Distribution

This section begins by explaining the need for sampling. It discusses the population, sample statistics, population parameters, and sample distribution concepts. 
 

Sampling Distribution- CLT(Central Limit Theorem)

This section begins by highlighting the technique to connect the sample with the population. It describes CLT, its properties, and concepts, like standard deviation, sampling distribution, standard error, and z-score, by formulating to solve for an example problem. It also analyzes various assumptions of the solution of CLT. 
 

Hypothesis

This section defines the hypothesis and discusses the null and alternative hypotheses and their application in Machine Learning and Business Intelligence. 
 

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4.49
Course Rating
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Ratings & Reviews of this Course

Reviewer Profile

5.0

I enjoy learning Statistics for Data Science for free; it is great indeed
I really enjoyed learning at Great Learning's Statistics for Data Science. It was great indeed, more practical and easy to follow. The skills I gained from this course will be displayed when executing my duties. I really appreciate the great work for this development from this platform.
Reviewer Profile

5.0

Amazing and informative course. Do try!
I gained depth in topics, and the instructor made it easy to understand. Thank you very much.
Reviewer Profile

5.0

I gained fundamentals in Statistics for Data Science
I enjoyed the course and learned a lot about Statistics for Data Science.
Reviewer Profile

5.0

Always curious about the real essence of statistics applicable in Data Analytics
I was always curious to know about the statistics that can be applied to Data Analysis. This has been a perfect course for me to get an overview of the best applications of statistics in the data science field.
Reviewer Profile

5.0

The topic is really interesting and will help me in my future career
I gained a lot of knowledge and skills in this topic for my future career.
Reviewer Profile

5.0

I learned the basics of statistics and probability
I have gained enough knowledge and skills in probability and statistics for solving real-world problems.
Reviewer Profile

5.0

This course is a very good introduction to statistics
This course is a very good introduction to statistics for data science.

Statistics for Data Science

4.49
average rating

Ratings

1.5 Hrs

Learning hours

Beginner

Level

6.4K+
local_fire_department

Learners

10 Million+ learners

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Frequently Asked Questions

What prerequisites are required to learn this Statistics for Data Science course?

The free Statistics for Data Science course doesn’t require any prerequisites. Anyone can take this course and learn from it without prior knowledge. 
 

How long does it take to complete this free Statistics for Data Science course?

The course contains one hour of video content you can finish at your convenience. Great Learning Academy courses are self-paced and can be finished whenever you get time. 

Will I have lifetime access to the free course?

Yes, the free course comes with lifetime access. Any learner who wants to brush up on their skills can revisit and retake the course. 
 

What are my next learning options after this Statistics for Data Science course?

Enthusiasts of the Data Science field can opt for Great Learning’s professional Master in Data Science course covering all the essential skills to build a promising career. 

Is it worth learning Statistics for Data Science?

Yes, statistics is an essential part of Data Science. Learners looking to build a solid career in the Machine Learning domain also need an understanding of Statistics. So, it provides great worth learning Statistics for both Data Science and Machine Learning enthusiasts. 
 

What is Statistics in Data Science used for?

Statistics is the core of Data Science, advanced ML algorithms, and Business Intelligence that gather and translate data insights into actionable events. Data Scientists use statistics to analyze data patterns and derive conclusions from them. This provides strategies for businesses to grow in the market. 
 

Why is Statistics for Data Science so popular?

Statistics and probability are prevalent terms in data science as they allow businesses to find loopholes in the system with the help of data and their solutions. That’s why it became essential for data scientists and analysts. 

What jobs demand that you learn Statistics for Data Science?

  • Data Science is the fastest-growing field, and there are several jobs that require learning Statistics concepts for Data Science. Some of these jobs are:
  • Data Administrator
  • Data Engineer
  • Data Scientist
  • ML Engineer
  • Business IT Analyst
  • Data Architect
  • Market Analyst
     

Will I get a certificate after learning the Statistics course for Data Science?

Yes, once you finish the course modules, you can take the quiz that will reward you with a course completion certificate. The certificate showcases your skills in your professional career. 
 

What knowledge and skills will I gain upon completing this Statistics for Data Science course?

This free course will help you to understand the basic Statistics concepts used in Data Science, including probability, mean, median, standard deviation, normal distribution, hypothesis, Central Limit Theorem, and sampling distribution. The course is beneficial for developing Statistical skills for the learner. 

How much does this Statistics for Data Science course cost?

The course is entirely free of cost. So any aspirant can enroll in this course and start learning basic statistics concepts in Data Science. 
 

Is there a limit on how many times I can take this Data Science course?

No, the course does not set any limit to learning it. Learning enthusiasts can take this course as many times as they want. Hence, you can revisit the course and start learning again whenever you feel like revising your learnings. 

Can I sign up for multiple courses from Great Learning Academy at the same time?

Yes, there are several courses that you can enroll yourself in and start learning. You can sign up for multiple courses simultaneously with Great Learning Academy. 
 

Why choose Great Learning Academy for this Statistics for Data Science course?

Great Learning Academy is a huge platform that provides self-paced courses in various domains. Learners take benefit from these courses in their professional careers. There are more than 5 million learners worldwide who are benefitted from these courses. This Statistics for Data Science course is helpful for enthusiasts looking forward to building a career in the Data domain. This course will help you familiarize yourself with data science's statistics, probability, sampling distribution, Central Limit Theorem, and hypothesis. 

Who is eligible to take this Data Science course?

The free course does not require any eligibility to enroll. Hence, any learner intending to learn basic Statistics concepts for Data Science can take the course without any nudge. 
 

What are the steps to enroll in this course?

Follow the steps below to enroll in this course:

1. Visit the Great Learning Academy homepage.

2. Search for the course in ‘Statistics for Data Science course.’

3. Click on the ‘Enroll for Free button.

4. Register with Great Learning Academy to enroll in any course and start learning the course for free.

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