Statistics Mathematics for Data Science in Python

Statistics & Mathematics for Data Science in Python, available at $49.99, has an average rating of 4.2, with 73 lectures, 8 quizzes, based on 33 reviews, and has 587 subscribers.

You will learn about Learn the foundational concepts of statistics and mathematics using Python Learn how data science and machine learning work under the hood Learn by implementing the abstract concepts This course is ideal for individuals who are Any one who wants to master the statistics and mathematics of Data science will find this course very useful It is particularly useful for Any one who wants to master the statistics and mathematics of Data science will find this course very useful.

Enroll now: Statistics & Mathematics for Data Science in Python

Summary

Title: Statistics & Mathematics for Data Science in Python

Price: $49.99

Average Rating: 4.2

Number of Lectures: 73

Number of Quizzes: 8

Number of Published Lectures: 73

Number of Published Quizzes: 8

Number of Curriculum Items: 81

Number of Published Curriculum Objects: 81

Original Price: $49.99

Quality Status: approved

Status: Live

What You Will Learn

Learn the foundational concepts of statistics and mathematics using Python

Learn how data science and machine learning work under the hood

Learn by implementing the abstract concepts

Who Should Attend

Any one who wants to master the statistics and mathematics of Data science will find this course very useful

Target Audiences

Any one who wants to master the statistics and mathematics of Data science will find this course very useful

Master the Statistics & mathematics that powers Data Science!!

“Data Scientist is a person who is better at statistics than any programmer and better at programming than any statistician.” – Josh Wills

Data science is all about leveraging data to draw meaningful insights. And undoubtedly, converting raw and quantitative data into an organized form requires a lot of knowledge & hard work. When it comes to data science, mathematics & statistics are the 2 important pillars around which the majority of the concepts revolve.

Though expecting everyone to become the Aryabhatta can be wrong, but one can definitely dedicate some time to learn all the important concepts of Mathematics & Statistics to master Data Science, one of the most trending fields of this digital economy.

Considering the high demand for data scientists & all-time high skill gaps, we have curated this online course entirely dedicated to Statistics & Mathematics behind Data Science. All the covered concepts will aid you in identifying patterns from the data and help you to create algorithms.

Why you should learn Mathematics & Statistics for Data Science?

Maths & stats are the building blocks of data science

You will be able to create various algorithms

You can easily interpret data effectively

Helps in identifying & solving complex real-world problems

Model Selection based on their inherent limitations

Why you should take this course?

This course on statistics & mathematics is a perfect way of learning & understanding the important concepts involved in data science. You will learn all the maths & stats behind data science through its handcrafted sections in the most interactive way possible.

It covers everything from Vocabulary & Descriptive statistics to NLP along with all the important tools. In the end, a project is also included on data visualization & optimization to ensure complete learning.

This course includes:

Working with Google Colab

Vocabulary & descriptive statistics

Distribution types- Uniform, binomial, Poisson, normal & fitting

Inferential statistics with visualizations

Course Curriculum

Chapter 1: Google Colab for Data Science

Lecture 1: Course Overview

Lecture 2: Introduction

Lecture 3: Google Drive & Colab Introduction

Lecture 4: Documentation Exploration

Lecture 5: Importing Data from Google Drive to Pandas DataFrame

Lecture 6: Importing Data from OneDrive to Pandas DataFrame

Lecture 7: Sharing a Colab Notebook

Lecture 8: Summary

Chapter 2: Vocabulary & Descriptive Statistics

Lecture 1: Introduction

Lecture 2: Introduction to General Statistical Vocabulary

Lecture 3: Variable Types within Data

Lecture 4: Summarizing Data with Counts

Lecture 5: Measures of Center, Essential Analytics

Lecture 6: Correlation Coefficient

Lecture 7: Summary

Chapter 3: Distribution Types

Lecture 1: Introduction

Lecture 2: Introduction to Probability Distributions

Lecture 3: Uniform Distribution

Lecture 4: Binomial Distribution

Lecture 5: Poisson Distribution

Lecture 6: Normal Distribution

Lecture 7: Fitting Distributions – Advanced

Lecture 8: Summary

Chapter 4: Inferential Statistics with Visualizations

Lecture 1: Introduction

Lecture 2: Bar Charts

Lecture 3: Histograms

Lecture 4: Box Plots

Lecture 5: Scatter Plots

Lecture 6: Advanced Visualizations

Lecture 7: Summary

Chapter 5: Confidence Intervals & Hypothesis Testing

Lecture 1: Introduction

Lecture 2: Seaborn Sample Data & Fitting

Lecture 3: Introduction to Confidence Intervals & Tests

Lecture 4: Assuming Normality

Lecture 5: Normal Data:Probability Plots with Means

Lecture 6: Normal Data: Categorical Confidence Intervals

Lecture 7: Normal Data: Quantitative Confidence Intervals

Lecture 8: ANOVA

Lecture 9: Non-Normal Data & Bootstrap

Lecture 10: Summary

Chapter 6: Regression & Predictions

Lecture 1: Introduction

Lecture 2: Preparation Part 1: Loading & Exploring Diamonds Data

Lecture 3: Preparation Part 2: Categorical Coding & Data Splitting

Lecture 4: Linear Regression

Lecture 5: Polynomial Regression

Lecture 6: Ridge Regression

Lecture 7: Lasso Regression

Lecture 8: ElasticNet Regression

Lecture 9: Random Forest Regression

Lecture 10: Model Comparison Tool

Lecture 11: Model Hyper Tuning & Optimization

Lecture 12: Summary

Chapter 7: Classification Modeling

Lecture 1: Introduction

Lecture 2: Preparation Part 1: Loading & Exploring Penguins Data

Lecture 3: Preparation Part 2: Cleaning & Preparing Penguins Data

Lecture 4: Naive Bayes

Lecture 5: Logistic Regression

Lecture 6: K-Nearest Neighbors

Lecture 7: SVM

Lecture 8: Random Forest

Lecture 9: Model Comparison Tool

Lecture 10: Model Hyper Tuning & Optimization

Lecture 11: Summary

Chapter 8: Natural Language Processing

Lecture 1: Introduction

Lecture 2: Data Loading & Exploration

Lecture 3: NLTK to Examine Text

Lecture 4: For Loop Creation of 8.2

Lecture 5: Movie Reviews Text Analysis & Frequency

Lecture 6: Finding Features of Textual Data

Lecture 7: Naive Bayes with NLTK

Lecture 8: Cosine Similarity Between Texts

Lecture 9: Summary

Chapter 9: Project

Lecture 1: Project Resource File

Instructors

Eduonix Learning Solutions
1+ Million Students Worldwide | 200+ Courses

Eduonix-Tech .

Rating Distribution

1 stars: 0 votes

2 stars: 1 votes

3 stars: 4 votes

4 stars: 11 votes

5 stars: 17 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!