Data Analysis: Leveraging Python in Tableau

Amrit Sreekumar (~amrit95)


17

Votes

Description:

With the advent of Tableau and languages like Python and R, converting raw data into meaningful insights is much easier and convenient than before. Tableau is a tool used to visually represent data and is powerful enough to analyze the given data at any required level. At an industry perspective, the tool comes handy in finding the trends in marketing and sales with a click of a button.

Introducing Python to Tableau using TabPy can help define calculated fields in Python, thereby giving it the power to leverage a large number of Machine-learning libraries right from the visualizations. This widens the scope of its applications to any field that deals with big data and its analytics. Optimisation and cross-sharing of data models facilitated by TabPy immensely enhance the efficiency and usability of the tool. With just a few lines of code, we can churn out predictive models and increase the accuracy of future predictions.

The talk will primarily focus on:

  • An introduction to data manipulation and visualization using Tableau.
  • An overview of the steps to leverage TabPy in Tableau.
  • The impact and advantages of Tableau-TabPy combination in the real world.

Prerequisites:

A rudimentary understanding of Data Science and Python scripting.

Speaker Info:

I am a sophomore undergrad in computer science from Amrita School of Engineering, India of which I am a part of an intra-college FOSS initiative called FOSS@Amrita. Developing small but useful things that improve lives of the common and affects the open-source community has always been my passion. I believe that with the right technology applied, it can do wonders for the lives of people.

Furthermore, I have completed the Google Summer of Code’17 with The Wikimedia Foundation and was also a Google Code-In mentor for the same community. Worked on the project that aimed at the improvement and enhancement of the ProofreadPage Extension and Wikisource, through important bug fixes that are left as backlog and implementation of significant features that would make it more user-friendly. This was done so that the extension and Wikisource become easier to use and are raised to the contemporary Mediawiki standards. Apart from this, I'd love to ​express​ ​views​ on​ ​contemporary​ ​world issues,​ ​get​ to know​ ​the​ ​different dimensions​ of​ ​it and analyze the​ ​multiple​​ ways​ ​in​​ which​ ​the​ ​problems​ ​could be rectified.

Speaker Links:

Linkedin

Blog

Gerrit

GitHub

Id: 763
Section: Data science
Type: Talks
Target Audience: Beginner
Last Updated: