A-Z Maths for Data Science

Learn about Linear Algebra, Probability, Statistics and more through solved examples and intuition.

Ratings 3.91 / 5.00
A-Z Maths for Data Science

What You Will 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

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 Should Attend!

  • 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

TAKE THIS COURSE

Tags

  • Data Science
  • Linear Algebra
  • Probability
  • Statistics

Subscribers

149

Lectures

131

TAKE THIS COURSE



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