Gain insights into conducting meaningful data analysis in Python without prior programming experience. Discover Python fundamentals, data preprocessing, EDA, and predictive analytics for solving business problems.

env.tar.gz

Python3

Ready to move past Excel for complex business analysis? Then you’ll find this course very helpful.

This hands-on introductory Data Science course is aimed at professionals and students who don't have any experience with programming. It will help you advance your career by preparing you to conduct meaningful data analysis in Python on any dataset — large or small.

You’ll begin with the fundamentals of Python, with focus on CSV files in Python, covering concepts like data preprocessing and Exploratory Data Analysis (EDA). In the second half, you'll focus on predictive and inferential analysis using statistical and machine learning techniques, and learn how these techniques can help solve business problems.

Data Science for Non-Programmers

## Random Forests
**Random Forest**, as the name implies, consists of a large number of [decision trees](https://www.educative.io/courses/data-science-for-non-programmers/decision-trees) that operate as an *ensemble* for classification. An **ensemble** is a collection of different predictive models that collectively decide the predicted output. In random forests, each individual tree gives a class as an output. The class with the most votes gets chosen as the final output of the model. 

# Random Forests
**Random Forest**, as the name implies, consists of a large number of [decision trees](https://www.educative.io/courses/data-science-for-non-programmers/decision-trees) that operate as an *ensemble* for classification. An **ensemble** is a collection of different predictive models that collectively decide the predicted output. In random forests, each individual tree gives a class as an output. The class with the most votes gets chosen as the final output of the model. 

This lesson will focus on training random forest models in Python. 

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Random Forests

Random Forests