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

# K-means in Python
We do not need to code the above algorithm because it is available in `sklearn.cluster`. We will be clustering on a dummy dataset first. The dummy dataset has three columns, `feature_1`, `feature_2`, and `label`. The dataset has 4 classes, which mean each row of the data set can have a label from `0`, `1`, `2`, or `3`. First, we will plot a scatter plot of the two features. 

import pandas as pd 
import matplotlib.pyplot as plt 

df = pd.read_csv('dummy.csv')
print(df.head())
X = df.drop(columns = ['label'])
Y = df['label']

plt.scatter(x= X['feature_1'],y = X['feature_2'])

We drop the `label` column in **line 6** because in reality, we do not have labels for unsupervised learning. We plot a scatter plot between the two columns in **line 9**. By looking at the plot we observe that this data can be categorized into clusters. 

Let's make these clusters below. 

This lesson will focus on K-Means on two-dimensional data in Python.

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

K-Means on Two-Dimensional Data

K-means in Python