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

# Plotting
A **Plot** is a way of representing data visually. It can describe relationships between variables. Plots can give insight and information about the data that may not have been easily derived by looking at the data as a table. 

Almost every tool that works with spreadsheets can plot data. Python has a complete package dedicated for plotting data known as *matplotlib*. We will be using this package with pandas to plot data. In this lesson, we will discuss **univariate plots**. These are plots where a single variable is plotted. 

Let's look at an example of this using the [Sample Sales Data](https://www.kaggle.com/kyanyoga/sample-sales-data). The data is in *sales_data_sample.csv* file. Have a look at the data.

import pandas as pd 
# Read data
df = pd.read_csv('sales_data.csv')

# Print Information
print(df.info())

## Plotting individual columns
We can easily plot distributions of a single individual column for our dataframe. Let's look at an example below. Run the code below and click on the output graph to view it in full screen. 

### Histograms
**Histograms** are plots that inform us of the underlying *frequency distribution* of the data. They group numbers into ranges and tells us how many values of a variable lie in every range. 

import pandas as pd 
import matplotlib.pyplot as plt 

df = pd.read_csv('sales_data.csv')

# plot YEAR ID
df['QUANTITYORDERED'].plot(kind = 'hist',bins = [0,5,15,25,35,45,55,65],rwidth = 0.8)

# display the plot
plt.show()


We import `matplotlib.pyplot` package in **line 2** as `plt`. We would use the keyword `plt` wherever we want to use it in the code. 

We have plotted the histogram of `QUANTITYORDERED` in **line 7**. We have set `kind` to `hist`. We can provide two more inputs with `hist` to the function; `bins` to specify our ranges and `rwidth` to specify the space between each bar in the graph. If we do not provide these inputs, the function automatically decides some values for the `bins` and `rwidth`. Alternatively, we can provide the number of bins to this function instead of a list and it makes the provided number of bins automatically.  

From the graph, we can see that the firm received more than $800$ orders in which the quantity ordered was in the range of $25-35$.

### Density plots
A **density plot** is a representation of the distribution of a numeric variable. It uses a density estimate to show the probability density function of the variable. It is a smoothed version of the histogram and is used in the same concept. 

This lesson will describe the process of plotting univariate plots of the data from a csv file in Python.

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Plotting Data 1: Univariate Plots

Plotting

Plotting individual columns

Histograms