Overview What You Will Learn Curriculum Instructor

Course Overview

One thing that damps the excitement of learning data science is the requirement of a deep understanding of mathematics and/or the ability to teach oneself advanced level calculus, linear algebra, and statistics. Good knowledge of computational mathematics is necessary if you want to become a Data Scientist or a Data Analyst. 

This course equips you with the actual knowledge you need in statistics if you work with data. This course gives you the chance to systematically master the core concepts in statistics and probability, descriptive statistics, hypothesis testing, regression analysis, analysis of variance and some advanced regression/machine learning methods such as logistics regressions, polynomial regressions, decision trees and more. You will be taken through both theoretical and practical real-life examples of using mathematics in data science.

By the end of this course, you will have everything you need to master the fundamentals in statistics and probability required in data science or data analysis.

What You Will Learn

  • Master the fundamentals of statistics for data science and data analytics.
  • Master descriptive statistics and probability theory.
  • Learn machine learning methods such as decision trees and decision forests.
  • Understand probability distributions such as normal distribution
  • Poisson distribution
  • and more.
  • Learn about hypothesis testing
  • p value
  • type I
  • and type II error.
  • Get equipped with logistic regression
  • multiple linear regression
  • and regression trees.

Program Curriculum

  • Introduction
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  • Introduction
  • Mean
  • Median
  • Mode
  • Which Central Frequency to Use? Mean, Median or Mode?
  • Skewness
  • Range & Interquartile Range
  • Population and Sample
  • Variance and Standard Deviation
  • Impact of Scaling & Shifting
  • Chapter 2 Quiz

  • Distribution of Data
  • The Normal Distribution
  • The Z-score
  • Statistical Distribution
  • Chapter 3 Quiz

  • Basic and Simple Probability
  • Rule of Addition
  • Law of Multiplication
  • Bayes Theorem and Expected Value
  • Central Limit Theorem
  • Binomial Distribution
  • Poisson Distribution
  • Chapter 4 Quiz

  • What and Why?
  • Significance Level and p Value with Type Error
  • Confidence Interval and Margin of Error
  • Hypothesis Test, T Test, and T Distribution
  • Law of Large Numbers
  • Chapter 6 Quiz

  • Linear Regression
  • Correlation Coefficient
  • Residual, MSE, and MAE
  • Coefficient of Determination
  • RMS Error
  • Chapter 6 Quiz

  • Multiple Linear Regression
  • Overfitting
  • Polynomial Regression
  • Decision Tree
  • Regression Tree
  • Random Forest
  • Chapter 7 Quiz

  • Basics of ANOVA and Assumption
  • One-way ANOVA
  • F Distribution
  • Two-way ANOVA and Sum of Squares
  • Chapter 8 Quiz
Load more modules

Instructor

Aashish Dixit

Aashish has spent a lot of his life studying and teaching. He holds a master’s and bachelor’s degree in Engineering. He has also spent a lot of his life working as a senior Network Data engineer at GE & HP in Gurgaon (& San Jose), then as a Manager Data N/W, first with JP Morgan Chase, Ohio and then on his own, and finally at Flipkart and Cisco in Bangalore. In all these roles, he has written a lot of code and built a lot of models. He has spent around two decades teaching high-quality, in-depth courses on advanced Mathematics, Data Science & Technology. I have helped hundreds of learners become champions in subjects and enabled them to change their lives, with his students working at companies such as Amazon, Google, Cisco, and Facebook.

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