AZ Maths for Data Science
Date: 31 August 2025 | Posted by: AD-TEAM
A-Z Maths for Data Science
Last updated 1/2025
Duration: 23h | .MP4 1920x1080, 30 fps(r) | AAC, 44100 Hz, 2ch | 9.84 GB
Genre: eLearning | Language: English
Learn about Linear Algebra, Probability, Statistics and more through solved examples and intuition.
What you'll learn
- Basics of Linear Algebra - What is a point, Line, Distance of a point from a line.
- What is a Vector and Vector Operations
- What is a Matrix and Matrix Operations
- Visualizing data, including bar graphs, pie charts, histograms
- Data distributions, including mean, variance, and standard deviation, and normal distributions and z-scores
- Analyzing data, including mean, median, and mode, plus range and IQR and box plots
- Data Distributions like Normal and Chi Square
- Probability, including union vs. intersection and independent and dependent events and Bayes' theorem
- Permutation with examples
- Combination with examples
- Central Limit Theorem
- Hypothesis Testing
Requirements
- Foundational Mathematics
Description
A-Z MATHS FOR DATA SCIENCE IS SET UP TO MAKE LEARNING FUN AND EASY
This 100+ lesson course includes 23+ hours of high-quality video and text explanations of everything from Linear Algebra, Probability, Statistics, Permutation and Combination. Topic is organized into the following sections:
Linear Algebra - Understanding what is a point and equation of a line.
What is a Vector and Vector operations.
What is a Matrix and Matrix operations
Data Type - Random variable, discrete, continuous, categorical, numerical, nominal, ordinal, qualitative and quantitative data types
Visualizing data, including bar graphs, pie charts, histograms, and box plots
Analyzing data, including mean, median, and mode, IQR and box-and-whisker plots
Data distributions, including standard deviation, variance, coefficient of variation, Covariance and Normal distributions and z-scores.
Different types of distributions - Uniform, Log Normal, Pareto, Normal, Binomial, Bernoulli
Chi Square distribution and Goodness of Fit
Central Limit Theorem
Hypothesis Testing
Probability, including union vs. intersection and independent and dependent events and Bayes' theorem, Total Law of Probability
Hypothesis testing, including inferential statistics, significance levels, test statistics, and p-values.
Permutation with examples
Combination with examples
Expected Value.
AND HERE'S WHAT YOU GET INSIDE OF EVERY SECTION:
We will start with basics and understand the intuition behind each topic.
Video lecture explaining the concept with many real-life examples so that the concept is drilled in.
Walkthrough of worked out examples to see different ways of asking question and solving them.
Logically connected concepts which slowly builds up.
Enroll today! Can't wait to see you guys on the other side and go through this carefully crafted course which will be fun and easy.
YOU'LL ALSO GET:
Lifetime access to the course
Friendly support in the Q&A section
Udemy Certificate of Completion available for download
30-day money back guarantee
Who this course is for:
- Students currently studying probability and statistics or students about to start probability and statistics
- Anyone who wants to study math for fun
- Anyone wanting to learn foundational Maths for Data Science
- Anyone who wants to understand what goes behind the popular packages
More Info
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