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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
What you learn in Statistics for Data Science ?
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.
Course Outline
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.
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.
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.
This section begins by explaining the need for sampling. It discusses the population, sample statistics, population parameters, and sample distribution concepts.
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.
With this course, you get
Free lifetime access
Learn anytime, anywhere
1.0 Hours
of self-paced video lectures
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.
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