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

# Missing values
During data collection and entry, it is possible that some values are missed, or data was not available for some entries. Hence, missing data is very common among data science applications.

Pandas makes it very easy to work with missing data. It does not include missing values in all of its different calculations such as sum, mean, etc. by default. 

Pandas writes the value `NaN`(Not a Number) when it finds a missing value. 

## Detecting missing values
We can detect missing values using the function `isnull`. It returns `True` wherever there is a missing value, and `False`, otherwise.

import pandas as pd 
df = pd.read_csv('housing.csv')

# check which columns have how many missing values
print("Missing values in every column : \n" ,df.isnull().sum())
print("\n\n Missing values total : ",df.isnull().sum().sum())

# Display rows that have missing values
missing = df['total_bedrooms'].isnull()
print(df[missing])

In **line 5**, we use the function `isnull` and then use `sum` on it. This gives us a list of all columns with the number of missing values in them. From the list, we see that `total_bedrooms` has $207$ missing values. If we take another `sum` then we have the total number of missing values in the whole dataset, as done in **line 6**.
 
In **line 9**, we use `isnull` on `total_bedrooms` column and get a list of `True` and `False`. In the next line, we index using this list and display all the rows that have missing values in the total_bedrooms column.  

This lesson will elaborate on how to deal with data that has missing values. 

What is Data Science

Python Basics

Handling Tabular Data in Python

Data Cleaning

Exploratory Data Analysis

Statistical Inference

Predictive Models

Machine Learning

Missing Values

Missing values

Detecting missing values