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All LessonsFree Lessons (6)

What is Data Science

The Buzzword "Data Science"Data Science Lifecycle Python for Data Science

Python Basics

Hello World Variables and Data Types Operators Conditional Statements Functions Lists Loops Packages and Modules Exercise: Average of a List Solution Review: Average of a List Exercise: Factorial of a Number Solution Review: Factorial of a Number

Handling Tabular Data in Python

Importing Data in CSV Files with Pandas Indexing and Selection Filtering Data Applying Functions to Data Aggregating Data Grouping Data Pivot Tables Plotting Data 1: Univariate Plots Plotting Data 2: Bivariate Plots Test Your Knowledge

Data Cleaning

Introduction to Data Cleaning Data Types Missing Values Duplicates Inconsistent Data Outliers Outlier Detection and Removal Exercise: Cleaning NYC Property Sales Solution Review: Cleaning NYC Property Sales

Exploratory Data Analysis

Introduction Analyzing Individual Quantities Exploring Categorical Quantities Exploring Numerical Quantities Correlation and Heatmaps Exercise: Exploring E-Commerce Solution Review: Exploring E-Commerce Business Example: RFM Analysis in Python

Statistical Inference

The Basics of Statistical Inference

Confidence Intervals

Hypothesis Testing

One Sample t-Test

Two Sample t-Test

Predictive Models

A Simple Model Model Fitting on a Loss Function Gradient Descent Optimization with Gradient Descent Simple Linear Regression Multiple Linear Regression Evaluating Regression Models Logistic Regression Evaluating Logistic Regression Models Exercise: Churn Prediction Solution Review: Churn Prediction

Machine Learning

Why Machine Learning Machine Learning Pipeline Decision Trees Random Forests Support Vector Machines Ensembles: Bagging vs Boosting Clustering for Unsupervised Learning K-Means on Two-Dimensional Data K-Means on n-Dimensional Data Test your Knowledge Conclusion

Project

How to Predict the Traffic Volume Using Machine Learning

Data Science for Non-Programmers/

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One Sample t-Test

One Sample t-Test

This lesson will focus on how to perform one sample t-tests in Python.

We'll cover the following...

One-sample t-test

One-sample t-test

A One sample t-test checks whether a population mean differs from the sample mean or not. We can use the function ttest_1samp from the scipy.stats module.

We will be using the Student Alcohol Consumption Dataset. We will take a sample from the data and find the mean grade. We will check if the population mean grade ( $\mu$ ) differs from the sample mean grade ( $\bar{x}$ ) or not.

Null hypothesis $H_0$ ...

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