Reshika Chilakapati

Reshika graduated from Queen Mary University with BSc in Physics. She truly discovered the value of data analytics whilst doing her dissertation on barocaloric materials, where the impact of her work helped improve the environmental impact of fridges. Seeing the real-life outcomes that analyzing this data had, inspired her to pursue a career in this [data]. Using Origin Pro and Python to complete the data analysis. During her time at university, she also completed a student consultancy project - which involved working with a client to conduct market research for their new business plan. This intrigued Reshika to pursue a career in the field of consultancy but wanted to utilize her data analytical skills. After university, Reshika spent her time working in customer service which allowed her to develop her time management and communication skills by answering customer queries.

When Reshika came across Tableau and the Information Lab, she enjoyed the creative aspect of designing dashboards and representing data in an easy format. Now, she is a part of DS 39. At the Data School, Reshika has worked with many different companies – ranging from wealth management firms to sports teams. She has consistently demonstrated that she can solve complex problems, adapt to new challenges and communicate ideas clearly. She enjoys using her creativity to design dashboards and deciding the best way to visualize the data depending on the audience. Completing these projects involved creating different dashboards which were aligned to the user's needs and experience with Tableau and data cleaning with Alteryx and Tableau Prep. Reshika also used SQL to extract data from an API and parse JSON. She also has completed a project management task where she regularly communicated with the client to understand their needs and delegate her cohort for the final product.

In her personal development time, Reshika has completed Alteryx and Data Preppin' challenges to solidify her data cleaning skills which could then be used to visualize in Tableau. This has allowed her to complete her micro and core credentials in Alteryx. She also takes on feedback from her Makeover Mondays and improves her dashboards by creating wireframes and implementing different techniques into Tableau.

In her free time Reshika enjoys playing video games and digital art, where she completed commissions by understanding what her customer's wants and needs are.

Blog Posts

Avatar
Wed 10 Jan 2024 | Reshika Chilakapati
HOW TO: Create a Calendar Chart
Calendar charts are great for identifying key dates within your data, whether it is so mark big days or visualize a trend over a month! This blog post will go through a step by step guide on how to build this chart. Step 1
Avatar
Fri 28 Jul 2023 | Reshika Chilakapati
Dashboard Week: Day 5 Timeline
Today was a simple task, similar to Makeover Monday
Avatar
Thu 27 Jul 2023 | Reshika Chilakapati
Dashboard Week: Day 4 Timeline
Today, we were required to use PowerBi to analyze Eurovision data! Unfortunately, I had no clue what Eurovision was and have never watched it in my life
Avatar
Wed 26 Jul 2023 | Reshika Chilakapati
Dashboard Week: Day 3 Timeline
Today we were required to use BallDon'tLie API to extract NBA data and visualize within Tableau. This required the use of Alteryx which is not my strongest skillset but this challenge has been a big lesson to me in terms of rearranging my plans! Here is a timeline of how I went about this task
Avatar
Tue 25 Jul 2023 | Reshika Chilakapati
Dashboard Week: Day 2 Timeline
Dashboard week day 2 involved analyzing IMDB ratings and only using Tableau Prep to clean the data. The data came in the form of zipped TSV files - which was very new to me
Avatar
Fri 21 Jul 2023 | Reshika Chilakapati
Improving My Dashboards Pt. 1
For my personal project, I decided to take one of the dashboards I have hidden on Tableau Public and improve on the design and analysis. This was a Makeover Monday I completed before joining the Data School and it was about internet speeds across Europe
Avatar
Tue 13 Jun 2023 | Reshika Chilakapati
Explaining Granularity
After completing my Learn What The Data School Learns session, I came to the realization that I am very bad at explaining what granularity means. Typically because I keep getting Level of Detail mixed up with Granularity
Avatar
Wed 03 May 2023 | Reshika Chilakapati
Difference in Sales using LOD's vs Table Calculations
A session with Erica Hughes on advanced Table and Level of Detail Calculations showed us how to build the same chart using two different techniques. Before learning these, remember to read my blog on the difference between Table Calculations and LOD's
Avatar
Fri 28 Apr 2023 | Reshika Chilakapati
Small Changes That Make Big Impacts
This week's project involved recreating an old dashboard from Andy Kriebel and making small changes to it. Only until this week, I have never used containers or even the tiled option to design my dashboard
Avatar
Tue 25 Apr 2023 | Reshika Chilakapati
Cool Things Set Actions Can Do in Tableau
Sets are a finite list of values within a field and refer to a Boolean (values chosen are either in or out of the field). They can only be created from discrete dimensions however, not from measures
Avatar
Mon 24 Apr 2023 | Reshika Chilakapati
Table Calculations vs Level of Detail Calculations
DS 39 are beginning to dive into the behind the scenes of calculations in Tableau. Last week, we looked at calculated fields and how different functions come into play. This week, we started exploring Table calculations and Level of Detail calculations
Avatar
Fri 21 Apr 2023 | Reshika Chilakapati
Upgrading My Application Viz
Our project this week for DS 39 is to apply all the new skills we have learnt in Tableau Desktop to our first viz completed for the Data School application
Avatar
Wed 19 Apr 2023 | Reshika Chilakapati
Alteryx Made Easy
This past week, DS 39 have been learning how to use Alteryx. Unlike the rest of my cohort, I struggled with picking up on how to use the software compared to Tableau Prep. This blog will review the fundamentals of Alteryx and simplify things for easier understanding
Avatar
Mon 17 Apr 2023 | Reshika Chilakapati
String Functions 101
Today, we began exploring calculated fields and all the functions that we may use in our daily lives. String functions were my main focus, which was brand new to me as I only have ever used to the number functions
Avatar
Thu 13 Apr 2023 | Reshika Chilakapati
Color Palettes in Tableau
Another Tableau training session is complete thanks to Andy Kriebel! Learning more about sorting, quadrant charts and sets led us to adding our own color palettes into Tableau. For my application, I used Google to generate a random color palette and copied each individual HEX code into Tableau
Avatar
Wed 12 Apr 2023 | Reshika Chilakapati
Understanding Tableau Terminology
DS 39 had another training session with Carl Allchin in which, he stressed the importance of knowing our terminology to improve our understanding on how to use the software. So, in this blog post - I am going to recap everything we learnt so far today. Saving different file types: * Workbooks (
Avatar
Tue 11 Apr 2023 | Reshika Chilakapati
Makeover Monday using the DougScore
Today's Makeover Monday task involves using a dataset which is based on Doug Demuro rating different cars in various categories such as; acceleration, value, comfort, etc
Avatar
Thu 06 Apr 2023 | Reshika Chilakapati
The Ins and Outs of Inputs and Outputs in Tableau Prep
Tableau Prep is a tool used to clean and reshape any data that is considered messy. This allows the data to then be ready for visual analysis through Tableau Desktop to communicate said data
Avatar
Wed 05 Apr 2023 | Reshika Chilakapati
Planning and Prepping
Today was the first time I ever opened Tableau Prep ever, it was intimidating at first but Carl's amazing tangents helped me wrap my head around the new concepts! Tableau Prep: Tableau Prep is used to clean and reshape data so we can analyze and communicate it
Avatar
Tue 04 Apr 2023 | Reshika Chilakapati
Data Analytics Pipeline explained using Tesco
Our second day in training in DS 39 was spent learning about different business approaches in extracting, storing, preparing and reporting data. The six stages that we looked at were; raw data, ingestion, central storage, prepared data, trusted data and visualized data
Powered by The Information Lab
1st Floor, 25 Watling Street, London, EC4M 9BR
Subscribe
to our Newsletter
Get the lastest news about The Data School and application tips
Subscribe now
© 2025 The Information Lab