Overview
This comprehensive course on Statistics & Probability for Data Science & Machine Learning will deepen your understanding on this topic.
After successful completion of this course you can acquire the required skills in this sector. This Statistics & Probability for Data Science & Machine Learning comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market.
So enrol in this course today to fast track your career ladder.
How will I get my certificate?
You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.
Who is This course for?
There is no experience or previous qualifications required for enrolment on this Statistics & Probability for Data Science & Machine Learning. It is available to all students, of all academic backgrounds.
Requirements
Our Statistics & Probability for Data Science & Machine Learning is fully compatible with PC’s, Mac’s, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.
There is no time limit for completing this course, it can be studied in your own time at your own pace.
Career Path
Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to—
- Open doors of opportunities
- Increase your adaptability
- Keep you relevant
- Boost confidence
And much more!
Course Curriculum
- 10 sections
- 89 lectures
- 11 hours, 27 minutes total length
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Welcome! 00:02:00 -
What will you learn in this course? 00:06:00
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How can you get the most out of it? 00:06:00
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Intro 00:03:00
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Mean 00:06:00
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Median 00:05:00
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Mode 00:04:00
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Mean or Median? 00:08:00
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Skewness 00:08:00
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Practice: Skewness 00:01:00
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Solution: Skewness 00:03:00
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Range & IQR 00:10:00
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Sample vs. Population 00:05:00
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Variance & Standard deviation 00:11:00
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Impact of Scaling & Shifting 00:19:00
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Statistical moments 00:06:00
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What is a distribution? 00:10:00
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Normal distribution 00:09:00
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Z-Scores 00:13:00
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Practice: Normal distribution 00:04:00
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Solution: Normal distribution 00:07:00
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Intro 00:01:00
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Probability Basics 00:10:00
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Calculating simple Probabilities 00:05:00
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Practice: Simple Probabilities 00:01:00
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Quick solution: Simple Probabilities 00:01:00
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Detailed solution: Simple Probabilities 00:06:00
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Rule of addition 00:13:00
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Practice: Rule of addition 00:02:00
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Quick solution: Rule of addition 00:01:00
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Detailed solution: Rule of addition 00:07:00
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Rule of multiplication 00:11:00
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Practice: Rule of multiplication 00:01:00
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Solution: Rule of multiplication 00:03:00
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Bayes Theorem 00:10:00
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Bayes Theorem – Practical example 00:07:00
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Expected value 00:11:00
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Practice: Expected value 00:01:00
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Solution: Expected value 00:03:00
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Law of Large Numbers 00:08:00
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Central Limit Theorem – Theory 00:10:00
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Central Limit Theorem – Intuition 00:08:00
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Central Limit Theorem – Challenge 00:11:00
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Central Limit Theorem – Exercise 00:02:00
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Central Limit Theorem – Solution 00:14:00
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Binomial distribution 00:16:00
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Poisson distribution 00:17:00
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Real life problems 00:15:00
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Intro 00:01:00
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What is a hypothesis? 00:19:00
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Significance level and p-value 00:06:00
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Type I and Type II errors 00:05:00
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Confidence intervals and margin of error 00:15:00
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Excursion: Calculating sample size & power 00:11:00
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Performing the hypothesis test 00:20:00
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Practice: Hypothesis test 00:01:00
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Solution: Hypothesis test 00:06:00
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T-test and t-distribution 00:13:00
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Proportion testing 00:10:00
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Important p-z pairs 00:08:00
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Intro 00:02:00
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Linear Regression 00:11:00
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Correlation coefficient 00:10:00
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Practice: Correlation 00:02:00
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Solution: Correlation 00:08:00
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Practice: Linear Regression 00:01:00
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Solution: Linear Regression 00:07:00
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Residual, MSE & MAE 00:08:00
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Practice: MSE & MAE 00:01:00
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Solution: MSE & MAE 00:03:00
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Coefficient of determination 00:12:00
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Root Mean Square Error 00:06:00
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Practice: RMSE 00:01:00
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Solution: RMSE 00:02:00
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Multiple Linear Regression 00:16:00
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Overfitting 00:05:00
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Polynomial Regression 00:13:00
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Logistic Regression 00:09:00
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Decision Trees 00:21:00
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Regression Trees 00:14:00
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Random Forests 00:13:00
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Dealing with missing data 00:10:00
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ANOVA – Basics & Assumptions 00:06:00
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One-way ANOVA 00:12:00
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F-Distribution 00:10:00
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Two-way ANOVA – Sum of Squares 00:16:00
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Two-way ANOVA – F-ratio & conclusions 00:11:00
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Wrap up 00:01:00
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Assignment – Statistics & Probability for Data Science & Machine Learning
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This course includes:
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Duration:11 hours, 27 minutes
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Access:Lifetime access
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Units:89
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Assignments:Yes
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Quizzes:No
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Certificate:Yes
