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 on n-dimensional data
For clustering n-dimensional data, we can use the same procedure. The only difference is that we first need to reduce the number of dimensions to 2. For reducing dimensions, we will use a technique called **Principal Component Analysis (PCA)**. We do not need to go into the details of how this technique works since the details are out of the scope of this course. We can reduce data to 2 dimensions by using the `PCA` class available in `sklearn.decomposition`. We will cluster the reduced data. 

We will be using the [Default of Credit Card Dataset](https://www.kaggle.com/uciml/default-of-credit-card-clients-dataset). This dataset originally had two classes. Naturally, if we do not have the class labels, we would make two clusters for this example. 

import pandas as pd 
import matplotlib.pyplot as plt 
from sklearn.decomposition import PCA

# Make data
df = pd.read_csv('credit_card_cleaned.csv')
Y = df['default.payment.next.month']
df = df.drop(columns = ['default.payment.next.month','GENDER','MARRIAGE','ID'])
print('shape of original data set : ',df.shape)

# PCA 
pca = PCA(n_components=2)
X = pca.fit_transform(df)
X = pd.DataFrame(X)
X.columns = ['feature_1','feature_2']
print('shape of transformed data set : ',X.shape)

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

We import the `PCA` class from `sklearn.decomposition` in **line 3**. After reading the data in **line 6**, we separate the labels in `Y` in **line 7**. We drop the categorical columns in the next line. We initialize the `PCA` class in **line 12** with `n_components=2`, which means we need to reduce dimensions to 2. The `PCA` class has the function `fit_transform`, which gives us the transformed data. We use `fit_transform` in **line 13** and store the reduced data in `X`. At this time, the returned data is not a dataframe, rather it is a 2-dimensional list. So we convert it into a dataframe in **line 14** by giving the list to `pd.DataFrame`. We name the columns `feature_1` and `feature_2` in **line 15**, so they are easy to access later.  From the outputs of **lines 9 and 16**, we can see that the data has been reduced to 2 columns. 

In the end, we just plot the reduced data as we did for the above simple example. 

This lesson will focus on K-Means on n-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 n-Dimensional Data