State of Data Science & Machine Learning in 2018

State of Data Science & Machine Learning in 2018

The results of this Kaggle survey were published recently. The questions addressed include:

Survey Methodology
Survey Participants- Basic Profile
Educational profile of participants
Job profile of Particpants
What do they do at work?
What tools are used for data analysis
Do you consider yourself a Data Scientist?
Coding Exposure
Programming Language Used
Data Visualization Libraries Used
What is more important -Academic Acheivements or independent projects?
Time spent on activities in Data Science projects
Types of Data handled
Do you use Machine learning methods at work?
Experience in using Machine Learning methods at work
Machine Learning Frameworks Used
Metrics for Model Success
ML algorithms -Importance of topics
Difficultes in projects involved in exploring unfair bias in datasets/ algorithms
Projects involving exploring model insights
Confidence in Explaining ML models
Methods used for explaining ML Models
Integrated Development tools(IDE) usage
Hosted NoteBook Usage
Cloud Computing services usage
Cloud Computing Products Usage
Machine Learning Products Usage
Relational databaseProducts Usage
Big Data and Analytics Products Usage
Training on Machine Learning/Data Science
Online Training
Source of Public datasets
Media Sources
Summary of Findings

One of the numerous charts from this survey (click on picture to zoom in)
Kaggle conducted an industry-wide global survey that presents a truly comprehensive view of the state of data science and machine learning. The survey attempts to understand broadly the profile , work activities, nature of projects undertaken , programming languages used, machine learning methods usage at work and machine learning frameworks used by participants from about 147 countries globally. It also provides information about the usage of various products and services like cloud computing, hosted notebooks etc in the field of data science and machine learning. It also throws light into the status of training and training methods, public data sets and media sources that the participants depend on to enhance their knowledge in the area.
The survey was live for one week in October, and after cleaning the data finished with responses from 23,859 participants globally.
Read the results here. 
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