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Statistics Fundamentals

2h 4m 10s
English
Paid

Statistics Fundamentals is a 12-lesson 2 hours 4 minutes self-paced course by LunarTech. Statistics underpins most of data science, yet it's often the part candidates feel shakiest about.

Course facts

Lessons
12
Duration
2 hours 4 minutes
Level
All levels
Language
English
Updated
Instructor
LunarTech
Price
Premium

Statistics underpins most of data science, yet it's often the part candidates feel shakiest about. This course builds that foundation directly, aimed at the kind of statistical thinking data-driven roles actually require.

The material connects theory to practice: applying statistical concepts to real-world problems and being ready to explain them clearly when they come up in interviews.

Who teaches Statistics Fundamentals? LunarTech

LunarTech thumbnail

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.

What lessons are included in Statistics Fundamentals?

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#1: Introduction
All Course Lessons (12)
#Lesson TitleDurationAccess
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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Books

Read Book Statistics Fundamentals

#TitleTypeOpen
1Fundamentals of Statistics PDF

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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.