Statistics for Data Science and Business Analysis Course is a beginner-level course that teaches descriptive and inferential statistics, Hypothesis testing, Regression analysis, and more. Currently, Udemy is offering the course for up to 87% off i.e. INR 455 (INR 3,499).
This Statistics Course on Udemy is apt for candidates who have a keen interest in Business Analysis and Data Science. It helps candidates learn the fundamentals of statistical analysis and how to plot different types of data. The course helps kickstart a student’s career as a Business Intelligence Analyst, Marketing Analyst, Data Analyst, or Data Scientist.
Learning Outcomes
- Learn the fundamentals of statistics
- Learn how to carry out regression analysis
- Learn the basics of Data Science with Python and R
- Perform hypothesis testing
- Understanding dummy variables, correlation, and covariance
Course Highlights
| Key Highlights | Details |
|---|---|
| Course Name | Statistics for Data Science and Business Analysis |
| Duration | 5 Hours |
| Rating | 4.6/5 |
| Student Enrollment | 1.64 lakhs |
| Instructor | 365 Careers |
| Course Level (Resources Required) | Beginner |
| Coding Exercises | No |
| Projects | No |
| Total Student Reviews | 0.36 lakhs |
| Merits |
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| Shortcomings |
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Course Content
| S.No | Module (Duration) | Topics |
|---|---|---|
| 1. | Sample or Population Data (4 minutes) | Understanding the difference between a population and a sample |
| 2. | The Fundamentals of Descriptive Statistics (23 minutes) | The various types of data we can work with, levels of measurement, Categorical variables. Visualization techniques for categorical variables |
| Numerical variables. Using a frequency distribution table, Histogram charts, Cross tables and scatter plots. | ||
| 3. | Measure of Central Tendency, Asymmetry, and Variability (25 minutes) | The main measures of central tendency: mean, median and mode, Measuring skewness |
| Measuring how data is spread out: calculating variance, Standard deviation and coefficient of variation. | ||
| Calculating and understanding covariance, The correlation coefficient. | ||
| 4. | Practical Example: Descriptive Statistics (16 minutes) | Practical example |
| 5. | Distributions (19 minutes) | Introduction to inferential statistics, What is distribution, The Normal distribution |
| The standard normal distribution, Understanding the central limit theorem, Standard error. | ||
| 6. | Estimators and Estimates (31minutes) | Working with estimators and estimates, Confidence intervals – an invaluable tool for decision making |
| Calculating confidence intervals within a population with a known variance | ||
| Confidence interval clarifications, Calculating confidence intervals within a population with an unknown variance | ||
| What is a margin of error and why is it important in Statistics? | ||
| 7. | Confidence Intervals: Advanced Topics (16 minutes) | Calculating confidence intervals for two means with dependent samples, Calculating confidence intervals for two means with independent samples (part 1) |
| Calculating confidence intervals for two means with independent samples (part 2), Calculating confidence intervals for two means with independent samples (part 3) | ||
| 8. | Practical Example: Inferential Statistics (10 minutes) | Practical example: inferential statistics |
| 9. | Hypothesis Testing: Introduction (18 minutes) | The null and the alternative hypothesis, Establishing a rejection region and a significance level, Type I error vs Type II error |
| 10. | Hypothesis Testing: Let’s Start Testing! (30 minutes) | Test for the mean. Population variance known, What is the p-value and why is it one of the most useful tools for statisticians |
| Test for the mean. Population variance unknown, Test for the mean. Dependent samples, | ||
| Test for the mean. Independent samples (Part 1), Test for the mean. Independent samples (Part 2) | ||
| 11. | Practical Example: Hypothesis Testing (7 minutes) | Practical example: hypothesis testing |
| 12. | The Fundamental of Regression Analysis (20 minutes) | Introduction to regression analysis, Correlation and causation, The linear regression model made easy |
| What is the difference between correlation and regression, A geometrical representation of the linear regression model, A practical example – Reinforced learning | ||
| 13. | Subtleties of Regression Analysis (27 minutes) | Decomposing the linear regression model – understanding its nuts and bolts, What is R-squared and how does it help us? |
| The ordinary least squares setting and its practical applications, Studying regression tables, The multiple linear regression model. | ||
| The adjusted R-squared, What does the F-statistic show us and why do we need to understand it? | ||
| 14. | Assumption for Linear Regression Analysis (21 minutes) | OLS assumptions, A1. Linearity, A2. No endogeneity, A3. Normality and homoscedasticity |
| A4. No autocorrelation, A5. No multicollinearity | ||
| 15. | Dealing with Categorical Data (5 minutes) | Dummy variables |
| 16. | Practical Example: Regression Analysis (14 minutes) | Practical example: regression analysis |
Resources Required
- Stable Internet Connection.
- Latest Version of Windows or macOS.
- MS Excel
Comparison Table
| Parameters | Statistics for Data Science and Business Analysis | Statistics for Business Analytics and Data Science A-Z | Tableau 20 Advanced Training: Master Tableau in Data Science |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 5 Hours | 6 Hours | 10 Hours |
| Rating | 4.6/5 | 4.5/5 | 4.6/5 |
| Student Enrollments | 1.64 lakhs | 0.53 lakhs | 0.86 lakhs |
| Instructors | 365 Careers | Kirill Eremenko | Kirill Eremenko |
| Level | Beginner | Beginner | Intermediate (Beginner Level Knowledge of Tableau is Required) |
| Topics Covered | Statistics, Data Analytics, Data Science | Central Limit Theorem, hypothesis testing, confidence intervals, statistical significance | Tableau, Calculations, Tableau in Data Science |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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Student Reviews
Check out the student reviews for the course.
- Rakesh J. (5.0/5) “I first came across this course in my Data Science training at Simplilearn. This was the recommended video for the Mathematics basics. Loved these basic mathematics for Data Science. I was crushed when they updated this course with a robotic-sounding PowerPoint presentation. I literally pulled my hair out listening to the lecture. So, I went looking for this video lecture. I found it here in Udemy, Thank God! Suggestion: You can definitely make an advanced version of this course extending it’s reach to Machine Learning, Deep Learning & AI.”
- Yayati K. (5.0/5) “The videos are kept short and up to the mark. No unwanted concepts are included and practical implementation is explained. Very well designed course.”
- Ajay Babu B. (5.0/5) “Course was very brief and easy to understand.”
- Vinay S. (5.0/5) “Crisp explanations and good practical assignments”
- Aryan B. (5.0/5) “The instructor delivery is great and with animations involved it is quite interesting to watch and understand.”
- Jiaping C. (4.0/5) “it would be better to demonstrate out the steps of some formula from the examples would make it more clear.”
- Breno Ingwersen S. (4.0/5) “The course goes through all fundamentals but not as deep as expected and all the examples are a bit too basic and unrealistic. However it’s a good course to get the basic idea to later deepen yourself with other sources.”
- Hiten V. (4.0/5) “Perfect Course for the student or employee who so ever is transitioning or pursuing his/her career towards the data science”
- Gagan S. (3.0/5) “Most of the times i had to look into Q&A section to get my doubts rectified, could have included all the necessary things explained in lesson to reduce time spent on Q&A to understand better”
- Sourabh K. (3.0/5) “the course was bit faster to understand. I think it should be a little slower and more detailed, and the most important key was missing is that was the solution part i.e. the instructor should also explain the solution in video format. It would have been a great help to a student, atleast for me.”
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Statistics for Data Science and Business Analysis: FAQs
Ques. What is the fee for the course?
Ans. The course is originally priced at INR 3,499 but currently, it is available for INR 455.
Ques. What will I learn in the course?
Ans. You will learn all about statistics and its use in Data Science, Data Analysis, and Business Analysis..
Ques. What is the duration of the course?
Ans. The duration of the course is 5 hours.
Ques. Is there a certification from Udemy?
Ans. Yes, you will get a certificate of completion from Udemy.
Ques. What is the rating?
Ans. It is rated 4.6 out of 5.
Ques. Do I have lifetime access to this course?
Ans. Yes, you can access this course for a lifetime on mobile or TV.
Ques. Can I access the course on mobile devices, laptops, and TV?
Ans. Yes, you can access the course on mobile devices, laptops, and TV.
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