“Probability for Statistics and Data Science” is designed to teach the fundamental skill of probability, which is crucial for success in business. This course focuses on probability concepts such as conditional probability, Bayesian probability, and probability distributions, which will help individuals advance their careers in fields such as data science, business intelligence, statistics, finance, and pure probability. The course has been specifically designed to reflect the most in-demand competencies that will help learners understand and calculate challenging probabilistic topics. It is simple to understand, comprehensive, practical, and animated with amazing video quality, full of exercises, and resources.
The course covers the fundamentals of probability such as the event, sample space, complement, expected value, variance, and probability distribution function. Additionally, the course covers combinatorics, which is the study of combinations, permutations, and variants, and Bayesian probability, which is a more advanced probability theory. Currently, udemy is offering the Probability for Statistics and Data Science course for up to 87 % off i.e. INR 449 (INR 3,500).
Who can Opt for this Course?
- Those seeking careers in data science
- People who are considering careers in business intelligence
- Enterprise analysts
- Business leaders
- Those with a love for statistics and quantitative analysis
- Anyone who is interested in learning about the nuances of probability and how it is applied in business
- Individuals seeking to begin learning probability
- Those who wish to understand probability’s foundations
- People who want to understand scholarly publications by drawing conclusions from simplified statistics
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 03 Hours |
| Rating | 4.7/5 |
| Student Enrollment | 12,287 students |
| Instructor | 365 Careers https://www.linkedin.com/in/365careers |
| Topics Covered |
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| Course Level | N.A |
| Total Student Reviews | 977 |
Learning Outcomes
- Knowledge of probability theory Learn about combinatorics
- Discover how to apply and understand Bayesian Notation
- Variables from several sorts of distributions can be followed
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Introduction to Probability (26 minutes) | What does the course cover? |
| What is the probability formula? | ||
| What is the probability formula? | ||
| How to compute expected values? | ||
| How to compute expected values? | ||
| What is a probability frequency distribution? | ||
| What is a probability frequency distribution? | ||
| What is a complement? | ||
| What is a compliment? | ||
| 2. | Combinatorics (42 minutes) | Why are combinatorics useful? |
| Why are combinatorics useful? | ||
| When do we use Permutations? | ||
| When do we use Permutations? | ||
| Solving Factorials | ||
| Solving Factorials | ||
| Why can we use certain values more than once? | ||
| Why can we use certain values more than once? | ||
| What if we couldn’t use certain values more than once? | ||
| Computing Variations without Repetition | ||
| What are combinations and how are they similar to variations? | ||
| What are combinations and how are they similar to variations? | ||
| What is “symmetry” in Combinations? | ||
| What is “symmetry” in Combinations? | ||
| How do we combine combinations of events with separate sample spaces? | ||
| How do we combine combinations of events with separate sample spaces? | ||
| What is the chance of a single ticket winning the lottery? | ||
| What is the chance of winning the lottery? | ||
| A Summary of Combinatorics | ||
| Practical Example: Combinatorics | ||
| 3. | Bayesian Inference (54 minutes) | What is a set? |
| What is a set? | ||
| What are the different ways two events can interact with one another? | ||
| What are the different ways two events can interact with one another? | ||
| What is the intersection of sets A and B? | ||
| What is the intersection of sets A and B? | ||
| What is the union of sets A and B? | ||
| What is the union of sets A and B? | ||
| Are all complements mutually exclusive? | ||
| Are all complements mutually exclusive? | ||
| What does it mean to for two events to be dependent? | ||
| What does it mean to for two events to be dependent? | ||
| What is the difference between P(A|B) and P(B|A)? | ||
| What is the difference between P(A|B) and P(B|A)? | ||
| Conditional Probability in Real-Life | ||
| How do we apply the additive rule? | ||
| How do we apply the additive rule? | ||
| How do we derive the Multiplication Rule formula? | ||
| How do we interpret the Multiplication Rule Formula? | ||
| When do we use Bayes’ Theorem in Real Life? | ||
| Bayes’ Theorem | ||
| Practical Example: Bayesian Inference | ||
| 4. | Distributions (01 hour 17 minutes) | What is a probability distribution? |
| What is a probability distribution? | ||
| What are the two main types of distributions based on the type of data we have? | ||
| What are the two main types of distributions based on the type of data we have? | ||
| Discrete Distributions and their characteristics. | ||
| Discrete Distributions and Their Characteristics. | ||
| What is the Discrete Uniform Distribution? | ||
| What is the Discrete Uniform Distribution? | ||
| What is the Bernoulli Distribution? | ||
| What is the Bernoulli Distribution? | ||
| What is the Binomial Distribution? | ||
| What is the Binomial Distribution? | ||
| What is the Poisson Distribution? | ||
| What is the Poisson Distribution? | ||
| What is a Continuous Distribution? | ||
| What is a Continuous Distribution? | ||
| What is a Normal Distribution? | ||
| What is a Normal Distribution? | ||
| Standardizing a Normal Distribution | ||
| How do we Standardize a Normal Distribution? | ||
| What is a Student’s T Distribution? | ||
| What is a Student’s T Distribution? | ||
| What is a Chi Squared Distribution? | ||
| What is a Chi-Squared Distribution? | ||
| What is an Exponential Distribution? | ||
| What is an Exponential Distribution? | ||
| What is the Logistic Distribution? | ||
| What is a Logistic Distribution? | ||
| Practical Example: Distributions | ||
| 5. | Tie-ins to Other Fields (18 minutes) | Tie-ins to Finance |
| Tie-ins to Statistics | ||
| Tie-ins to Data Science |
Resources Required
- There is no requirement for prior experience
- Beginning with the fundamentals, we will steadily increase your knowledge
- A readiness to practice and learn
Featured Review
Erika Hooper (5/5): The videos are wonderful and help to make connections to the information.
Pros
- Majed Hamad (5/5): It is also great for whoever is working in the market research industry.
- Cem Bura ALKAN (5/5): Great course to learn or to remember what you learned about probability.
- Mohan Bisunke (5/5): Absolutely great designed courses in Probability for statistics and data science.
- Petar Agovski (5/5): Great course!! Warm recommendation to take this course and to get a good understanding of statistics and probability.
Cons
- Lukasz D. (2/5): A little bit too simplistic. My 13-year-old son was able to watch it with me and understand most, which is good and bad at the same time.
- Rahul A. (2/5): The course is overall good but it doesn’t cover Bayes theorem properly only two videos with a simple example. I bought it only for Bayes..but this course is good who is new to stats
- Athanasios P. (2/5): 4.30 example is not well explained, you mention it too and move forward without clarifying what you meant
- Diana A. (2/5): I would expect a better explanation for the lectures starting from “Distributions”. I felt it was going at a fast pace and it lacks explaining the background or the main concept of each topic. It was just as giving highlights on each topic. This should be mentioned clearly in the course description to help us pick the right course for us that serves our needs. Less than what I expected for this part of the course since it didn’t help me much.
About the Author
The instructor of this course is 365 Careers is a Creating opportunity for Data Science and Finance students. With a 4.6 Instructor Rating and 632,611 Reviews on Udemy, he/she offers 91 Courses and has taught 2,204,468 Students so far.
- On Udemy, 365 Careers is the top-selling provider of courses in business, finance, and data science
- In 210 different countries, more than 2,000,000 students have taken the company’s courses
- People who have finished 365 Careers training now work at renowned companies like Apple, PayPal, and Citibank
- On Udemy right now, 365 focuses on the following subjects: Finance – Financial modeling in Excel, valuation, capital budgeting, financial statement analysis (FSA), investment banking (IB), leveraged buyout (LBO), corporate budgeting, using Python for finance, Tesla valuation case study, CFA, ACCA, and CPA
- The courses offered by 365 Careers are the ideal place to start whether you want to work as a financial analyst, data scientist, business analyst, data analyst, business intelligence analyst, business executive, finance manager, FP&A analyst, investment banker, or entrepreneur
Comparison Table
| Parameters | Probability for Statistics and Data Science | Binomial, Normal Distribution, and Matrices for Data Science | Master Statistics & Probability 2020: Beginner-To-Advanced |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 3.5 hours | 16.5 hours | 13.5 hours |
| Rating | 4.7 /5 | 4.3 /5 | 4.2 /5 |
| Student Enrollments | 12,287 | 46,412 | 16,009 |
| Instructors | 365 Careers | Sai Acuity Institute of Learning Pvt Ltd Enabling Learning Through Insight! | Kody Amour |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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