Google is a company that uses a lot of data science. However, what does a big company like Google use for this? In a bid to answer this question, we studied the requirements for a job as a data scientist at Google. This gave us insight into some of the most frequent tools, languages, and packages they use;
The Languages
The most important thing to consider with data science is programming. This requires specific certain programming languages, and Google has a few that kept popping up during our search. These include:
- Python
- R
- SQL
- MATLAB
All the above-listed languages are popular in this field, with Python being one of the most popular languages. It is easy to see why Google uses these programming languages for their data science needs.
Machine Learning Packages
In addition to programming language, there are the machine learning packages- we only saw 3 of these on the requirement list for a data science position at Google. These machine learning packages were;
- TensorFlow
- Scikit-learn
- PyTorch
Google built TensorFlow as a machine learning package; therefore, it’s a no-brainer that they use it for data science. On the other hand, PyTorch was developed by Facebook; however, it was mentioned less frequently than the other two.
The Tools
Finally, the tools we saw that Google uses for Data science projects include the following;
- Hadoop
- Spark
- Apache Hive
Hadoop as a tool makes the managing of big data more straightforward and smoother, and it is one of the essential tools in this field. In addition, Apache Hive works with SQL-like interface and is similar to Spark- they both make handling big data more accessible.
There you have it, some tools, languages, and packages Google uses for big data analytics. Do you know or use any of these? We will love to see if you do, as well as your thoughts and contributions.
Image Credit: Great Learning
