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.

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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

# Introduction to Data types
Data is basically raw information. It can be in any form. We will look at data from the perspective of a data scientist who is going to clean and analyze the data. We need to know in what form the data is present to analyze it properly and apply different statistical methods on it. To a data scientist, data can take two basic forms:
* **Numerical** 
* **Categorical**

## Numerical data
Numerical data is data that has some meaning as a measurement, such as the height of a person, the price of a product, the IQ of a person, the number of lessons in this course, etc. It is also known as *Quantitative Data*. It can be broken down into two types.

### 1. Discrete data
Discrete data is data that can take separate and distinct values. It can take only a certain number of values. It cannot be divided into smaller meaningful parts. For instance, the number of heads in 100 tosses of a coin flip, the number of students in a classroom, the number of cars in a showroom, etc.

### 2. Continuous data
Continuous data cannot be counted, but it can be measured. It represents measurements. It includes quantities that do not have an end to them such as money, the height of a person, the amount of rainfall, the speed of a car, etc. It can be divided into further meaningful parts.

We can use statistical methods such as mean, median, quartiles, Box plots, and Histograms to describe numerical data. 

This lesson will introduce the learner to the different data types in which data can exist.

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Data Types

Introduction to Data types

Numerical data

1. Discrete data