Pre-MBA Statistics
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What you'll learn
Explore the types of data and the basics of probability.
Describe how a relatively small sample of data can help to infer about a large population.
Justify arguments about a population based on limited data.
Skills you'll gain
- Probability
- Estimation
- Statistical Inference
- Probability Distribution
- Data Collection
- Statistical Modeling
- Data Literacy
- Probability & Statistics
- Data Presentation
- Sampling (Statistics)
- Statistics
- Statistical Methods
- Data Analysis
- Data Science
- Descriptive Statistics
- Statistical Visualization
- Sample Size Determination
- Statistical Analysis
- Data Visualization
- Statistical Hypothesis Testing
Details to know
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There are 6 modules in this course
Welcome to the Pre-MBA Statistics course! By the end of this course, you will be able to describe how statistics can be used to summarize, analyze, and interpret data. This course introduces you to some aspects of descriptive and inferential statistics. You will learn to distinguish between various data types and describe the operations that you can execute with each type of data and the right tools to use. The course also discusses the concepts of probability, which form the backbone of statistical analysis. In particular, the course explores how data behaves and provides insight into its analysis. Further, it discusses how data can be sampled and the pros and cons of these methods. The course also delves deeper into the behavior of large data sets based on well-established statistical results. This also enables you to identify the pitfalls of incorrectly using statistical laws. Lastly, you will learn how to estimate population parameters based on limited data and check the correctness of hypotheses about populations from limited data.
This course is open to students from all disciplines holding a bachelorβs degree. A rudimentary knowledge of Mathematics would help grasp the concepts better.
In this module, you will learn about various types of data. You will gain insight into the types of data based on how they can be organized and the amount of inference possible from each of them. The module also analyzes the unique characteristics of diverse types of data. Lastly, you will also learn operations with usability and interpretability of various kinds of data.
What's included
15 videos5 readings3 assignments1 discussion prompt
15 videosβ’Total 51 minutes
- Course Introductionβ’3 minutes
- Meet Your Instructor (Prof. Sriram)β’1 minute
- Meet Your Instructor (Prof. Diptesh)β’1 minute
- Week 1: Introductionβ’2 minutes
- Introduction to Numerical Dataβ’1 minute
- Meanβ’5 minutes
- Medianβ’4 minutes
- Measures of Variabilityβ’3 minutes
- Percentileβ’3 minutes
- Categorical: Cardinal Dataβ’7 minutes
- Categorical: Ordinal Dataβ’4 minutes
- Nominal Dataβ’3 minutes
- Visualizing Numerical Dataβ’5 minutes
- Visualizing Categorical Dataβ’7 minutes
- Week 1: Summaryβ’1 minute
5 readingsβ’Total 110 minutes
- Course Overviewβ’10 minutes
- Recommended Reading: Types of Dataβ’60 minutes
- Essential Reading: Statistical Data Types: All You Need to Knowβ’15 minutes
- Recommended Reading: Types of Data & Measurement Scales: Nominal, Ordinal, Interval and Ratioβ’15 minutes
- Recommended Reading: Engaging Thoughtfully with Different Perspectivesβ’10 minutes
3 assignmentsβ’Total 97 minutes
- Graded Quiz: Types of Data β’40 minutes
- Practice Assignmentβ’30 minutes
- Data Types and Visualizationβ’27 minutes
1 discussion promptβ’Total 30 minutes
- Types of Dataβ’30 minutes
In this module, you will learn about the basics of probability and the concept of random variables. This provides a relatively more formal approach to how data behaves and how uncertainties are modeled mathematically. Finally, the module discusses random variables and the special mathematical entities that model numerical data well and help in inferences.
What's included
16 videos3 readings3 assignments1 discussion prompt
16 videosβ’Total 77 minutes
- Week 2: Introductionβ’1 minute
- Random Experimentsβ’4 minutes
- Eventsβ’4 minutes
- Relations Between Eventsβ’6 minutes
- The Axiomsβ’4 minutes
- Probabilities of Unions of Eventsβ’6 minutes
- Probabilities of Events in a Partitionβ’3 minutes
- Problemβ’6 minutes
- Conditional Probabilityβ’9 minutes
- Independenceβ’3 minutes
- Problemβ’4 minutes
- Introduction to Random Variablesβ’10 minutes
- Probability Mass and Density Functionsβ’7 minutes
- Uniform and Normal Random Variablesβ’5 minutes
- Problemβ’4 minutes
- Week 2: Summaryβ’2 minutes
3 readingsβ’Total 90 minutes
- Essential Reading: An Introduction to Probability & Statisticsβ’15 minutes
- Recommended Reading: A Blog on Probability and Statisticsβ’15 minutes
- Recommended Reading: Random Variablesβ’60 minutes
3 assignmentsβ’Total 82 minutes
- Graded Quiz: Probabilityβ’40 minutes
- Basics of Probabilityβ’30 minutes
- Random Variablesβ’12 minutes
1 discussion promptβ’Total 30 minutes
- Conditional Probability in Real Life β’30 minutes
In this module, you will learn about different types of sampling methods used in surveys. Such sampling can be completely randomized or non-randomized. You will learn the pros and cons of these techniques and identify the right method to use in the situation you have in hand. You will also analyze the presentation of two important results: the law of large numbers and the central limit theorems.
What's included
11 videos4 readings3 assignments1 discussion prompt
11 videosβ’Total 74 minutes
- Week 3: Introductionβ’1 minute
- Introduction to Samplingβ’8 minutes
- Simple Random Samplingβ’2 minutes
- Stratified and Convenience Samplingβ’6 minutes
- Voluntary and Snowball Samplingβ’6 minutes
- Problemβ’10 minutes
- Expectationsβ’10 minutes
- Law of Large Numbersβ’12 minutes
- Independent and Identically Distributed Random Variablesβ’5 minutes
- Central Limit Theoremβ’12 minutes
- Week 3: Summaryβ’2 minutes
4 readingsβ’Total 75 minutes
- Essential Reading: Types of Sampling Methods (With Examples)β’15 minutes
- Recommended Reading: Samplingβ’30 minutes
- Recommended Reading: 8 Types of Sampling Techniquesβ’15 minutes
- Recommended Reading: Sampling Methods Reviewβ’15 minutes
3 assignmentsβ’Total 67 minutes
- Graded Quiz: Samplingβ’40 minutes
- Types of Samplingβ’12 minutes
- Probability Lawsβ’15 minutes
1 discussion promptβ’Total 45 minutes
- Sampling Techniques and Systematic Errorsβ’45 minutes
The task of collecting data from all members of a population is often expensive and sometimes impossible. You can, however, easily collect sample data from a population. In this module, you will learn to make inferences about the characteristics of the population from which you have collected sample data. In this module, you will learn about point estimation and then be able to construct a point estimate of the mean and standard deviation of data in the population. If the data you are interested in is expressed as a proportion, you can construct a point estimate of that proportion. The module also discusses interval estimation. You will learn how to build a confidence interval or a range around a point estimate so that you are appropriately confident that the population parameter will fall within that interval regardless of the sample from which the point estimate was obtained.
What's included
10 videos3 readings3 assignments1 discussion prompt
10 videosβ’Total 69 minutes
- Week 4: Introductionβ’1 minute
- Introduction to Some Key Terminologyβ’8 minutes
- Distribution of Estimatorsβ’8 minutes
- Properties of Estimatorsβ’11 minutes
- Examples of Calculations for Point Estimationβ’8 minutes
- What Is Interval Estimation?β’9 minutes
- How Confident Are We About the Intervals We Create?β’8 minutes
- How Do We Find the Width of a Confidence Interval?β’7 minutes
- Examples of Calculations for Interval Estimationβ’7 minutes
- Week 4: Summaryβ’1 minute
3 readingsβ’Total 105 minutes
- Essential Reading: What is a Point Estimate in Statistics?β’15 minutes
- Recommended Reading: Sampling, Sampling Distributions, and Interval Estimationβ’60 minutes
- Recommended Reading: Interval Estimationβ’30 minutes
3 assignmentsβ’Total 76 minutes
- Graded Quiz: Point and Interval Estimationβ’40 minutes
- Point Estimationβ’24 minutes
- Interval Estimationβ’12 minutes
1 discussion promptβ’Total 30 minutes
- Stratified Sampling vs. Simple Random Samplingβ’30 minutes
Given a sample of values and a claim that the sample comes from a population with certain characteristics, after going through this module, you will be able to construct tests that will justify or reject such a claim. You will learn the logic behind constructing and executing tests for means and proportions. You will also learn about tests to compare the properties of two populations based on samples from both populations.
What's included
11 videos3 readings3 assignments1 discussion prompt
11 videosβ’Total 60 minutes
- Week 5: Introductionβ’1 minute
- Testing Hypothesesβ’5 minutes
- Null and Alternate Hypothesesβ’4 minutes
- The Logic Behind Hypothesis Testingβ’4 minutes
- Errors in Testing and Their Consequencesβ’6 minutes
- Choosing Null and Alternate Hypotheses β’4 minutes
- One Sample Test for Means When the Population Standard Deviation Is Knownβ’10 minutes
- One Sample Test for Means When the Population Standard Deviation Is Unknownβ’7 minutes
- One Sample Test for Proportionsβ’8 minutes
- Two Sample Testsβ’11 minutes
- Week 5: Summaryβ’1 minute
3 readingsβ’Total 75 minutes
- Recommended Reading: Testing Hypothesesβ’30 minutes
- Essential Reading: Introduction to Hypothesis Testingβ’15 minutes
- Recommended Reading: Hypothesis Testingβ’30 minutes
3 assignmentsβ’Total 79 minutes
- Graded Quiz: Hypothesis Testingβ’40 minutes
- Introduction to Hypothesis Testingβ’15 minutes
- Performing Hypothesis Testsβ’24 minutes
1 discussion promptβ’Total 15 minutes
- Hypotheses Testing and the Related Errorsβ’15 minutes
This is a peer-review assignment based on the concepts taught in the Pre-MBA Statistics course. In this assignment, you will be able to apply the skills learned in the course in a realistic situation.
What's included
1 video1 peer review
1 videoβ’Total 2 minutes
- Course Wrap up Videoβ’2 minutes
1 peer reviewβ’Total 120 minutes
- Analyzing Student Performanceβ’120 minutes
Instructors
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University of London
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Emory University
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Reviewed on Jun 11, 2023
ACCORDING TO ME,THIS IS REALLY A VERY NICE COURSE BASED ON THE TOPICS OF STATASTICS AND EVERY MBA ASPIRANT SHOULD BE FAMILIER WITH THIS COURSE,BEACAUSE THIS IS GOING TO HELP YOU DURING MBA A LOT.
Reviewed on Oct 7, 2024
good learning experience but need clarity explanation about topic and short notes about topic
Reviewed on May 22, 2023
The course was good and informational but ,the peer review system wasn't impressive.A bit disheartening when points are concerned.
Frequently asked questions
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