A Data Scientist’s Tools to Boost Productivity

There are several tools or software that I use to carry out my daily tasks and data operations. Data science tools can be categorized into programming tools (includes cloud technologies, development software, programming languages or libraries) or business tools (such as SAS, excel, datarobot etc).

For this article, I will be focusing mostly on the tools that have made me more productive and efficient. I will be highlighting 10 of them that I usually use on a day to day basis.

Oracle SQL Developer

I use Oracle SQL developer for my database development tasks. I’ve used other tools such as MySQL Workbench, Toad or PyCharm for querying but I prefer this tool because of it’s minimalistic interface. It makes it easy and quick for me to navigate around the software.

PyCharm

I like how I can use PyCharm for different things. I can create a python scripts, notebooks and even connect to a database and run queries. One thing that I like about PyCharm is that it makes installation of libraries or packages easy across multiple projects. It has all the tools I need and has inspection with advanced debugger.

Tableau

Tableau is by far one of the most powerful data visualization tools that I’ve used. I tried Data Studio, PowerBI, Mixpanel all of which are also great for doing simple visuals but I find doing complex aggregations easier on Tableau and it has more visualization options — not to mention having the ability to connect to several database servers.

Trifacta

Probably one of the most underrated data wrangling tools but it’s actually very powerful in terms of speed and its ability to clean, structure raw data and transfer from one source to another. I like its minimalistic look but it has all the features I need on my day to day work.

Azure Databricks

Databricks is an analytics platform optimized for the Microsoft Azure cloud services platform. It has 3 different environments Databricks SQL, Databricks Data Science & Engineering, and Databricks Machine Learning. It supports Python, Scala, R, Java, SQL, and a few data science frameworks and libraries which makes it easier for data scientists and…