Master statistics for data-driven careers. Build a strong statistical foundation for data science, analysis, and decision making. Succeed in interviews and apply your knowledge to real-world problems.
Statistics Fundamentals
2h 4m 10s
English
Paid
About the Author: LunarTech
LunarTech is an online tech academy focused on data science, machine learning, and quantitative analysis — covering both the theoretical foundations (linear algebra, calculus, statistics) and the practical Python / SQL toolchain that working data scientists use. The school operates globally with cohort-based and self-paced tracks.
The CourseFlix listing carries twelve LunarTech courses spanning machine-learning theory, deep learning, applied data-science workflows, and the math fundamentals underlying the field. Material is paid and aimed at engineers and analysts transitioning into formal data-science roles or upskilling within them.
Watch Online 12 lessons
0:00
/ #1: Introduction
All Course Lessons (12)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | Introduction Demo | 04:00 | |
| 2 | 1. Random Variables | 06:26 | |
| 3 | 2. Mean, Variance, Standard Deviation | 02:53 | |
| 4 | 3. Covariance & Correlation | 05:17 | |
| 5 | 4. Probability Distribution Functions | 12:42 | |
| 6 | 5. Conditional Probability & Bayes Theorem | 09:09 | |
| 7 | 6. Introduction to Causal Analysis & Linear Regression | 14:51 | |
| 8 | 7. Hypothesis Testing & Statistical Significance | 10:38 | |
| 9 | 8. P-Values, Type I & Type II Errors, Confidence Intervals | 18:53 | |
| 10 | 9. Statistical Tests (Part 1) | 17:36 | |
| 11 | 10. Statistical Tests (Part 2) | 13:59 | |
| 12 | 11. Inferential Statistics (CLT & LLN) | 07:46 |
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Frequently asked questions
What are the prerequisites for enrolling in this course?
The course does not list specific prerequisites, but a basic understanding of mathematics and introductory statistics could be beneficial. Familiarity with concepts such as probability and basic algebra will help you grasp topics like random variables, probability distributions, and linear regression more effectively.
What statistical concepts will I learn in this course?
You will learn a wide range of statistical concepts, including random variables, mean, variance, standard deviation, covariance, correlation, probability distribution functions, and hypothesis testing. The course also covers conditional probability, Bayes theorem, causal analysis, linear regression, and inferential statistics like the Central Limit Theorem and Law of Large Numbers.
Who is the target audience for this course?
The course is designed for individuals pursuing data-driven careers, such as data science, data analysis, or decision-making roles. It is suitable for learners looking to build a strong foundation in statistics to apply in real-world scenarios or to enhance their performance in job interviews.
How does this course compare in depth and scope to other statistics courses?
This course offers a comprehensive overview of fundamental statistics concepts needed for data science and analysis. While it covers a broad range of topics from probability to inferential statistics, those seeking advanced topics like machine learning or deep statistical modeling may need additional resources.
Does the course cover the use of specific statistical software or tools?
The course description does not specify the use of particular statistical software or tools. The focus is primarily on foundational statistical concepts rather than software application. Learners interested in software-specific training may need to supplement this course with additional resources.
What topics are not covered in this course?
The course does not cover advanced statistical topics such as multivariate analysis, time series analysis, or non-parametric methods. It focuses on building a foundation in core statistical concepts necessary for data-driven decision making and analysis.
How can the skills learned in this course be applied to other fields or courses?
The statistical skills acquired in this course can be applied across various fields that rely on data analysis, such as economics, biology, psychology, and business. The foundational knowledge will also be beneficial for more advanced courses in data science, machine learning, and business analytics, providing a solid base for further study.