Aegis School of Business, Data Science, Cyber Security & Telecommunication
| Application fee: | 20.56 USD |
| GST: | 18 % |
Certificate of Proficiency in Statistics for Data Science & AI
Details
| Certification Body: | Aegis School of Data Science |
|---|---|
| Location: | Online |
| GST: | 18% |
| Total course fee: | 0 USD |
| Rating: |
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.
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Sl. No. |
Topic/Subtopics |
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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. |
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2. |
Descriptive Statistics I Measures of Central Tendency: (AM, Weighted Mean, GM, HM, Median, Mode) Measures of Partition: (Quartiles, Deciles, Percentiles) |
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3. |
Descriptive Statistics II Measures of Variation: (Range, Mean Deviation, Quartile Deviation, Standard Deviation, Coefficient of Variation) Skewness and Kurtosis. |
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4. |
Probability Permutation and combination, Definitions, Addition and Multiplication Theorem, Conditional probability, Bayes Theorem and its application. |
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5. |
Random Variables and Mathematical Expectation Probability Mass Function (PMF), Probability Density Function (PDF), Cumulative Distribution Function (CDF). |
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6. |
Probability Distributions Bernoulli Distribution, Binomial Distribution, Poisson Distribution Normal Distribution |
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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. |
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8. |
Inferential Statistics: Estimation Point Estimation, Interval Estimation: (one parameter, two parameter) |
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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. |
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10. |
Correlation and Regression Karl Pearson Correlation Coefficient, Spearman’s Rank Correlation, Regression Analysis: (Linear Regression, Multiple Linear Regression) |