Learn Statistics for Data Science Online
Statistics for Data Science
Join this free online course to get familiarized with the key concepts of Statistics for Data Science, probability, and hypotheses. Know how normal distribution and sampling play a very important role in data analytics practices.
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11.25 Hrs
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About this course
This free course will first introduce you to some basic terms of statistics, such as probability, distribution, hypotheses, and CLT (Central Limit Theorem) theorem, the essentials to learning Statistics for Data Science. Next, you will comprehend the Normal distribution with the help of examples. The examples of the normal distribution are very helpful in understanding statistics better. Moving forward with the course, you will be introduced to the hypothesis, which is used to prove or disprove the statements for distribution. Later, you will learn the concept of Sampling distribution briefly with the help of the Central Limit Theorem. Lastly, the tutor will help you to understand the theorem with the help of hypotheses. Enroll in this course to understand the concept of statistics basics for Data Science practices and gain a course completion certificate at the end. Are you looking forward to upskilling yourself in Data Science? Look no further! 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
Basics of statistics
This section covers the basic definition of Statistics and its association with data. It also covers the role of a statistician within an organization and the essential concepts to work with data including formulating problems, deriving data, and solving for a solution with a real-time example.
Descriptive statistics
This section explains how data is described without having pre-conditioned assumptions or pre-built models into it. It explains descriptive analysis with various medical-line examples.
Case study
This section explains the Cardio Good Fitness case study and demonstrates a solution to help you understand descriptive statistics using the Jupyter notebook.
Measures of Central Tendency
This section describes measures of central tendency by formulating to solve for the previously mentioned example. It also analyzes various metrics of the solution through graphs.
Measures of Dispersion
This section describes the standard deviation by formulating to solve for it. It explains the relative tendency towards the most accurate solution through the derived observation. You will also learn to work with code in the Jupyter notebook to understand this better. You will also learn to graphically represent the observation, about data, and metadata in the later part of this section.
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