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Analysis of Mental health in IT and Tech Companies

Mental health refers to cognitive, behavioral, and emotional well-being. It is all about how people think, feel, and behave.
Mental health problems have an impact on employers and businesses directly through increased absenteeism, negative impact on productivity and profits, as well as an increase in costs to deal with the issue.[4] In addition, they impact employee morale adversely.

This dataset is from a 2014 survey that measures attitudes towards mental health and frequency of mental health disorders in the tech workplace.The original dataset is from Open Sourcing Mental Illness. I found this dataset from Kaggle and I'm trying to analyse the effect of mental health illnesses on career and companies. It is increasingly being recognized that the mental health of employees is a crucial determinant in their overall health and that poor mental health and stressors at the workplace can be a contributory factor to a range of physical illnesses like hypertension, diabetes and cardiovascular conditions, amongst others. In addition, poor mental health can also lead to burn-out amongst employees, seriously affecting their ability to contribute meaningfully in both their personal and professional lives.

The tools and techniques I gained from the course Data Analysis with Python such as NumPy, Pandas, MatPlotLib, SeaBorn as well some others such as Squarify, pyWaffle played a major role in helping me analyse the data

Downloading the Dataset

Using a helper library e.g. opendatasets, which contains a collection of curated datasets and provides a function for directly download, we download the files
Using opendatasets, the mental-health in tech dataset is downloaded in this Jupyter Notebook.

!pip install jovian opendatasets --upgrade --quiet

Let's begin by downloading the data, and listing the files within the dataset.

# Change this
dataset_url = 'https://www.kaggle.com/osmi/mental-health-in-tech-survey'