Maths and Stats Foundation - eLearning
450,00 EUR
- 3 hours
Build a strong analytical mindset with the Maths & Statistics Foundation Training, designed to simplify core mathematical and statistical concepts for real-world application. This course helps you develop confidence in working with data by mastering essential topics such as descriptive statistics, probability, distributions, and fundamental mathematical techniques used in analytics and data-driven decision making.
Key Features
Language
Course and material in English
Level
Beginner - Intermediate level
Access
1 Year access to the learning platform
3 Hours of On-Demand Videos
with 10+ hours recommended study time
18 Guided Hands-On Exercises
4 Auto-Graded Assessments
33 Recall Quizzes
1 Comprehensive Assignments
Certificate
Program completion certification included
Learning Outcomes
At the end of this Course, you will be able to understand:
Fundamental
Begin with fundamental concepts such as mean, median, and mode, and explore how scaling and shifting affect data.
Regression
Gain an understanding of regression analysis and the concept of Root Mean Square Error (RMSE).
Data science
Discover how mathematics and statistics are applied in data science, machine learning, and business intelligence.
ANOVA
Get introduced to Analysis of Variance (ANOVA) and its practical applications.
Hypothesis
Learn the principles of hypothesis testing, including T-Test and T-Distribution.
Course timeline
Descriptive Statistics
Lesson 01
- Mean, Median, Mode
- Mean vs Median
- Skewness
- Skewness Practice
- Skewness Solution
- Range and IQR
- Sample vs Population
- Variance and Standard Deviation
- Impact of Scaling and Shifting
- Statistical Moments
Distribution
Lesson 02
- What is a Distribution?
- Normal Distribution
- Z-Scores
- Exercise - Normal Distribution
- Solution - Normal Distribution
Probability Theory
Lesson 03
- Probability basics and foundational concepts
- Addition and multiplication rules with exercises and solutions
- Bayes’ Theorem and applied examples
- Expected value with practice problems
- Law of Large Numbers
- Central Limit Theorem (theory, intuition, challenges, and exercises)
- Binomial and Poisson distributions
- Real-life probability applications
Hypothesis Testing
Lesson 04
- Introduction to hypothesis testing and its role in data science
- Understanding hypotheses, significance level, and p-values
- Type I & Type II errors
- Confidence intervals and margin of error
- Sample size estimation and statistical power
- Steps to perform hypothesis testing
- Practice exercise and solution
- T-test and T-distribution
- Proportion testing
- Key P–Z value relationships
Regression
Lesson 05
- Introduction to regression analysis
- Linear regression and correlation coefficient
- Exercises and solutions on correlation and regression
- Residuals, MSE, and MAE with practice problems
- Coefficient of determination (R²)
- Root Mean Square Error (RMSE) with exercises and solutions
- Multiple linear regression concepts
Advanced Regression and ML Algorithm
Lesson 06
- Multiple linear regression
- Polynomial and logistic regression
- Decision trees and regression trees
- Random forests
- Overfitting and model performance issues
- Strategies for handling missing dataimplement the cross-cutting concerns in your application or program.
- implement aspect-orientation to avoid cross-cutting concerns
ANOVA
Lesson 07
- Basics of ANOVA and key assumptions
- One-way ANOVA
- F-distribution
- Two-way ANOVA (sum of squares)
- F-ratio and interpretation of results
Who Should Enroll in This Program?
Aspiring data scientists and data analysts
Software engineers transitioning into data/AI roles
Business and finance professionals working with data
Anyone who wants to improve data interpretation and quantitative thinking skills
Prerequisites
No advanced math background is required. However, learners will benefit from:
- Basic high school-level mathematics (algebra and arithmetic)
- Familiarity with everyday data concepts (charts, averages, percentages)
- Basic computer literacy
- No prior experience in statistics, programming, or data science is needed.
Statements
Licensing and accreditation
This course is offered according to Partner Program Agreement and complies with the License Agreement requirements
Equity Policy
Candidates are encouraged to reach out to AVC for guidance and support throughout the accommodation process.
Frequently Asked Questions
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