Certificate of Proficiency in Statistics for Data Science & AI

Application fee : 20.39 USD

Details

Certification Body: Aegis School of Data Science
Location: Online
GST: 18%
Total course fee: 0 USD
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Course Details

Certification Exam Description

This Certification designed to test your knowledge in statistics for Data Science and AI at different levels such as Theoretical Knowledge, Outcome Communication and Applied expertise. The exam is closed book and no outside reference materials are allowed.

Brief about Topic

Data Science is a sunrise discipline for which statistics is the spine. Statistics perform a significant role in predictive modelling as well as it deals with uncertainty in data.

You may belong to any streams such as Social sciences, Life Sciences, Chemical Sciences, Medical Sciences, Financial Expert, Engineer; you must refer to past experience (i.e. numeric and nonnumeric facts called as DATA) to make right decisions. Basically, it turns data into information, and information into insights.

 

Certification Exam topics

Note: The certification will be based on a given curriculum, but certainly not limited only to mentioned subtopics.

Sl. No.

Topic/Subtopics

1.

Introduction to Statistics

What is Statistics?  Branches of Statistics, Population and Sample, Types of measurements & Data, Classification and Tabulation, Frequency Distribution, Diagrammatic and Graphical representation.

2.

Descriptive Statistics I

Measures of Central Tendency: (AM, Weighted Mean, GM, HM, Median, Mode)

Measures of Partition: (Quartiles, Deciles, Percentiles)

3.

Descriptive Statistics II

Measures of Variation: (Range, Mean Deviation, Quartile Deviation, Standard Deviation, Coefficient of Variation)

Skewness and Kurtosis.

4.

Probability

Permutation and combination, Definitions, Addition and Multiplication Theorem, Conditional probability, Bayes Theorem and its application.

5.

Random Variables and Mathematical Expectation

Probability Mass Function (PMF), Probability Density Function (PDF), Cumulative Distribution Function (CDF).

6.

Probability Distributions

Bernoulli Distribution, Binomial Distribution, Poisson Distribution

Normal Distribution

7.

Sampling and Sampling distribution:

Types of Sampling,

Sampling Methods: (SRS, Stratified, Systematic, 2-Stage, multi-stage and Cluster) Sampling Distribution, Sample size, Standard Error, Test Statistic, Parameters, Central Limit Theorem.

8.

Inferential Statistics: Estimation

Point Estimation, Interval Estimation: (one parameter, two parameter)

9.

Inferential Statistics: Testing of Hypothesis

Statistical Hypothesis, Null and Alternative hypothesis, Testing procedure, Critical value, Type I and type II error, p-value,

Z tests, t tests, Chi square tests, F tests

Analysis of Variance.

10.

Correlation and Regression

Karl Pearson Correlation Coefficient, Spearman’s Rank Correlation,

Regression Analysis: (Linear Regression, Multiple Linear Regression)