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Overview Of statistics for data science
The Statistics for Data Science course is designed for aspiring data analysts, data scientists, machine learning enthusiasts, and professionals who want to strengthen their statistical foundation. Whether you’re just starting your journey in a data science course or deepening your understanding of pattern recognition and machine learning, this program builds core expertise step by step. In fact, it’s ideal for students, career changers, or working professionals seeking data fluency.
Learners benefit from expertly designed content, structured across accessible modules that start with the basics and gradually move toward advanced topics. Through this statistics for data science course, you’ll explore essential concepts like probability, regression, and hypothesis testing, along with their application in machine learning algorithms. Moreover, being a CPD-accredited course, it strengthens your CV, showcasing a commitment to high-level learning. With 24/7 student support and flexible access, learning happens at your pace—whenever and wherever you choose.
Description Of data science course
In today’s data-driven world, understanding statistics for data science is more than a skill—it’s a necessity. This course fills the gap many learners face when trying to link traditional statistics to practical applications in a data science course or machine learning course. Therefore, whether you’re analyzing trends or building predictive models, this course will help you connect theory with real-world solutions using statistical tools.
You’ll start with descriptive statistics and probability theory before diving into hypothesis testing, distributions, and regressions. Additionally, the course covers advanced regression models, machine learning algorithms, and ANOVA, empowering you to perform meaningful analysis and decision-making. Ultimately, this statistics for data science training prepares you for practical tasks in pattern recognition and machine learning, equipping you with tools to analyze data, draw insights, and build models confidently.
Learning Outcomes Of Machine Learning Course:
After completing statistics for data science course, you will:
- Understand core concepts of statistics for data science
- Apply probability theory in a data science course context
- Interpret and visualize distributions for real-world datasets
- Conduct hypothesis testing for data-driven decisions
- Build linear and logistic regression models
- Explore advanced regression and machine learning methods
- Use ANOVA for variance analysis in experiments
- Link pattern recognition and machine learning to statistical insights
- Apply statistical tools in real machine learning course projects
Why Choose Us?
- Firstly, data science course course is accredited by the CPD Quality Standards.
- Additionally, you get lifetime access to the whole collection of the learning materials.
- Furthermore, there is an online test with immediate results.
- Moreover, enroling in the machine learning course has no additional cost.
- Hence, you can study and complete the course at your own pace.
- Finally, you can study for the course using any internet-connected device, such as a computer, tablet, or mobile device.
Certificate of Achievement
Upon successful completion, you will qualify for the UK and internationally-recognised CPD certificate and you can choose to make your achievement formal by obtaining your PDF Certificate at a cost of £4.99 and Hardcopy Certificate for £14.99.
Who Is This Course For?
The statistics for data science is ideal for:
- Data science students building a foundation in statistics for data science through a practical data science course
- Machine learning engineers linking theory with real machine learning course skills
- Analysts and professionals applying statistics for data science in industry settings
- Researchers exploring pattern recognition and machine learning with statistics
- Career switchers entering tech via a hands-on data science course
Requirements
The data science courserequires no prior degree or experience. Therefore, all you require is English proficiency, numeracy literacy, and a gadget with a stable internet connection. Consequently, you can learn and train for a prosperous career in the thriving and fast-growing industry, without any fuss.
Career Path Of Pattern Recognition and Machine Learning
With the help of machine learning course, you can explore diverse career options, such as:
- Data Scientist
- Machine Learning Engineer
- Data Analyst
- AI Researcher
- Statistical Modeler
Order Your Certificate
To order CPD Quality Standard Certificate, we kindly invite you to visit the following link:
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 | |||
| Order Your Certificate | 00:00:00 | ||
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