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Statistics & Probability for Data Science & Machine Learning
16 Students (2 Reviews)
Statistics & Probability for Data Science & Machine Learning course builds strong foundations in data analysis, probability, and statistical modelling for careers in AI and data science.
GET THIS COURSE AND 3000+ OTHERS FOR ONLY £49 PER YEAR. FIND OUT MORE
Statistics and probability are the foundation of data science and machine learning, enabling professionals to analyze data, identify patterns, and make informed predictions. This course provides a comprehensive introduction to essential statistical concepts and probability theory used in modern data-driven applications.
Throughout the course, learners will explore key topics such as descriptive statistics, probability distributions, hypothesis testing, correlation, regression analysis, and data interpretation. The course also explains how these concepts are applied in machine learning models to improve accuracy and decision-making. Practical examples and case studies ensure learners gain hands-on understanding.
By the end of this course, participants will develop strong analytical and quantitative skills required for data science and machine learning roles. This course is ideal for individuals aiming to build a career in data analysis, artificial intelligence, and predictive modelling.
Learning Outcomes
- Understand core statistics and probability concepts
- Analyze data using descriptive and inferential statistics
- Apply probability distributions in data analysis
- Perform hypothesis testing and statistical inference
- Understand correlation and regression techniques
- Interpret data for machine learning applications
- Build a foundation for data science and AI
Why choose this course
- Affordable, Accessible, informative and well-structured study materials
- Earn a globally recognised accredited qualification
- Study in your own time, at your own pace, through any device
- Benefit from feedback through mock exams and multiple-choice assessments
- Excellent career advancement/career switch opportunities
- Get 24/7 help or advice from our email and live chat teams
Certification
Certificate of Achievement
After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post.
Who is this course for
This course is suitable for:
- Aspiring data scientists learning statistical foundations
- Machine learning beginners building analytical skills
- Data analysts working with statistical modelling techniques
- Students pursuing careers in data science fields
Entry Requirements
- Basic knowledge of mathematics
- Understanding of algebra fundamentals
- Minimum secondary or equivalent qualification
- Interest in data science and analytics
Career Prospects
Completing Statistics & Probability for Data Science & Machine Learning can enhance employability and workplace performance. It supports career progression in roles such as:
- Data Analyst
- Data Scientist
- Machine Learning Engineer
- Business Intelligence Analyst
- Statistical Analyst
- AI Specialist
This qualification strengthens soft skills highly valued by UK employers, including adaptability, emotional intelligence, leadership potential, and stress management.
Course Curriculum
| Section 01: Let's get started | |||
| Welcome! | 00:02:00 | ||
| What will you learn in this course? | 00:06:00 | ||
| How can you get the most out of it? | 00:06:00 | ||
| Section 02: Descriptive statistics | |||
| Intro | 00:03:00 | ||
| Mean | 00:06:00 | ||
| Median | 00:05:00 | ||
| Mode | 00:04:00 | ||
| Mean or Median? | 00:08:00 | ||
| Skewness | 00:08:00 | ||
| Practice: Skewness | 00:01:00 | ||
| Solution: Skewness | 00:03:00 | ||
| Range & IQR | 00:10:00 | ||
| Sample vs. Population | 00:05:00 | ||
| Variance & Standard deviation | 00:11:00 | ||
| Impact of Scaling & Shifting | 00:19:00 | ||
| Statistical moments | 00:06:00 | ||
| Section 03: Distributions | |||
| What is a distribution? | 00:10:00 | ||
| Normal distribution | 00:09:00 | ||
| Z-Scores | 00:13:00 | ||
| Practice: Normal distribution | 00:04:00 | ||
| Solution: Normal distribution | 00:07:00 | ||
| Section 04: Probability theory | |||
| Intro | 00:01:00 | ||
| Probability Basics | 00:10:00 | ||
| Calculating simple Probabilities | 00:05:00 | ||
| Practice: Simple Probabilities | 00:01:00 | ||
| Quick solution: Simple Probabilities | 00:01:00 | ||
| Detailed solution: Simple Probabilities | 00:06:00 | ||
| Rule of addition | 00:13:00 | ||
| Practice: Rule of addition | 00:02:00 | ||
| Quick solution: Rule of addition | 00:01:00 | ||
| Detailed solution: Rule of addition | 00:07:00 | ||
| Rule of multiplication | 00:11:00 | ||
| Practice: Rule of multiplication | 00:01:00 | ||
| Solution: Rule of multiplication | 00:03:00 | ||
| Bayes Theorem | 00:10:00 | ||
| Bayes Theorem – Practical example | 00:07:00 | ||
| Expected value | 00:11:00 | ||
| Practice: Expected value | 00:01:00 | ||
| Solution: Expected value | 00:03:00 | ||
| Law of Large Numbers | 00:08:00 | ||
| Central Limit Theorem – Theory | 00:10:00 | ||
| Central Limit Theorem – Intuition | 00:08:00 | ||
| Central Limit Theorem – Challenge | 00:11:00 | ||
| Central Limit Theorem – Exercise | 00:02:00 | ||
| Central Limit Theorem – Solution | 00:14:00 | ||
| Binomial distribution | 00:16:00 | ||
| Poisson distribution | 00:17:00 | ||
| Real life problems | 00:15:00 | ||
| Section 05: Hypothesis testing | |||
| Intro | 00:01:00 | ||
| What is a hypothesis? | 00:19:00 | ||
| Significance level and p-value | 00:06:00 | ||
| Type I and Type II errors | 00:05:00 | ||
| Confidence intervals and margin of error | 00:15:00 | ||
| Excursion: Calculating sample size & power | 00:11:00 | ||
| Performing the hypothesis test | 00:20:00 | ||
| Practice: Hypothesis test | 00:01:00 | ||
| Solution: Hypothesis test | 00:06:00 | ||
| T-test and t-distribution | 00:13:00 | ||
| Proportion testing | 00:10:00 | ||
| Important p-z pairs | 00:08:00 | ||
| Section 06: Regressions | |||
| Intro | 00:02:00 | ||
| Linear Regression | 00:11:00 | ||
| Correlation coefficient | 00:10:00 | ||
| Practice: Correlation | 00:02:00 | ||
| Solution: Correlation | 00:08:00 | ||
| Practice: Linear Regression | 00:01:00 | ||
| Solution: Linear Regression | 00:07:00 | ||
| Residual, MSE & MAE | 00:08:00 | ||
| Practice: MSE & MAE | 00:01:00 | ||
| Solution: MSE & MAE | 00:03:00 | ||
| Coefficient of determination | 00:12:00 | ||
| Root Mean Square Error | 00:06:00 | ||
| Practice: RMSE | 00:01:00 | ||
| Solution: RMSE | 00:02:00 | ||
| Section 07: Advanced regression & machine learning algorithms | |||
| Multiple Linear Regression | 00:16:00 | ||
| Overfitting | 00:05:00 | ||
| Polynomial Regression | 00:13:00 | ||
| Logistic Regression | 00:09:00 | ||
| Decision Trees | 00:21:00 | ||
| Regression Trees | 00:14:00 | ||
| Random Forests | 00:13:00 | ||
| Dealing with missing data | 00:10:00 | ||
| Section 08: ANOVA (Analysis of Variance) | |||
| ANOVA – Basics & Assumptions | 00:06:00 | ||
| One-way ANOVA | 00:12:00 | ||
| F-Distribution | 00:10:00 | ||
| Two-way ANOVA – Sum of Squares | 00:16:00 | ||
| Two-way ANOVA – F-ratio & conclusions | 00:11:00 | ||
| Section 09: Wrap up | |||
| Wrap up | 00:01:00 | ||
| Order Your Certificate & Transcripts | |||
| Order your Certificates & Transcripts | 00:00:00 | ||
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Frequently asked questions
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They help analyze data, build models, and make accurate predictions in machine learning.
Yes, it is designed for beginners with basic math knowledge.
You will learn statistical analysis, probability, and data interpretation techniques.
No, programming is not mandatory but can be helpful.
It provides the mathematical foundation required to understand ML models.
Yes, it includes real-world examples and case studies.
Yes, a certificate is awarded upon completion.
For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime.
You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience.
We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase.
Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course.