SQL for Data Science
SQL for Data Science


In order to work on any data projects, you need data. And in today's era of serverless architecture where all the data is stored in Data Lake and Data warehouses, being able to write good SQL has become a necessity for every data scientist. You need to be able to write fast and efficient SQL to be able to retrieve, clean, and massage your data to do basic Exploratory Data Analysis(EDA).

Session Outline:
Hence, in this tutorial, I will walk you through the:
1. Basics like selecting a subset of data
2. Intermediate like Aggregations using group by clauses
3. Advanced where we will learn a few window/analytical functions

Session Prerequisites:


Graduating with a degree in Information Technology my love of working with data landed me my first job. Spanning three continents, my projects saw me architect Data Warehouses and Data Analytics platforms across banking, retail industries and startups. Currently, I am building the data platform and strategy at a construction tech B2B company called Assignar. Data, being a relatively new field is still shackled by the archaic ways of the old technology days with women still being far and few. Being a woman in this emerging area brings with it a sense of pride, but also a sense of responsibility. Empowering women to challenge the status quo and consider a career in Data is also on my agenda. I also enjoy keeping myself involved in academia by teaching in the Master of Data Science and Innovation(MDSI) program as a lecturer for Data Science Practices where I get to teach and mentor budding Data enthusiasts. One of my areas of interest is deep learning (neural networks and AI). I am fascinated with the possibilities of its application across industries and am keen to realise its potential in mainstream businesses. Hence I further aim to continue my quest for knowledge and discovery with an Industry PhD in the field of Neural Networks and Project Management.

Open Data Science




Open Data Science
One Broadway
Cambridge, MA 02142

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