Abirami R works as a Data Scientist in Flutura Decision Sciences and Analytics for over 2.5 years. Being fond of Mathematics since childhood made her pursue pure Science. She holds a Master’s Degree in Statistics from Bangalore University and a triple major Bachelor’s Degree in Computer Science, Mathematics and Statistics. Abirami’s interest and passion for finding meaningful patterns and insights from data grew strong during her Bachelor degree days and made her choose Statistics to follow her interest. She is glad as she could secure a gold medal in her Master’s Degree at the University level.
Meaningful insights combined with technology is a powerful combination and can improve business through the awareness it creates. Data Science is that perfect blend of insights and Technology.
How did you get into Data Analytics? What interested you in learning Data Analytics?
Abirami R: My first learning on Data Analytics was from my educational background where I learned how information could help you make better decisions with the right statistic involved. At work, I learned that the value that insights from data can create is immense and it’s a cascading effect where your customer and your customer’s customer get benefitted. This fuelled my interest in learning this field.
What was the first data set you remember working with? What did you do with it?
Abirami R: The first data I worked on was customer data of a Retail Energy Provider in the USA. I helped build models that would predict customer churn.
Was there a specific “aha” moment when you realized the power of data?
Abirami R: Yes, I was extremely excited to know the new products recommended by us were actually bought by the customer at the outlets. This is when I saw the value of Data and success of patiently iterating.
What is your typical day-in-a-life in your current job. Where do you spend most of your time?
Abirami R: My day is mostly involved in finding patterns in the data, in other words, Exploratory Data Analysis (EDA). EDA is the most important part of data analysis as it helps the predictive model to be accurate. Apart from EDA, I work on building a predictive model. This involves data cleaning, feature engineering and model training and testing.
How do you stay updated on the latest trends in Data Analytics? Which are the Data Analytics resources (i.e. blogs/websites/apps) you visit regularly?
Abirami R: I read blogs and articles written by folks from the data science community on a regular basis. I also attend conferences and summits to keep myself updated on the new technologies and theories. I read articles from Kaggle and Medium.
Share the names of 3 people that you follow in the field of Data Science.
- Andrew Ng
- Jason Brownlee
- DJ Patil
Team, Skills and Tools
Which are your favourite Data Analytics Tools that you use to perform in your job, and what are the other tools used widely in your team?
Abirami R: I use R and Python to do my job on a daily basis for most of the tasks like EDA, data cleaning and model building. Depending on the volume of the data we connect to data sources. For moderate datasets we use RDBMS or files as data source whereas for large datasets we connect to a big data source.
What are the different roles and skills within your data team?
Abirami R: There are folks-
- who work on the data pipeline – Data Engineers
- who work on data analytics and insights – Data Scientists
- who work on data representation and visualisations – UI/UX Engineer
Help describe some examples of the kind of problems your team is solving in this year?
Abirami R: Using the sensor data on ship engine to predict the engine shutdown before its occurrence.
How do you measure the performance of your team?
- Ability to make measured data to meaningful insights.
- Using the retrospective learning to predict the future.
These are the two things through which we measure our performances.
Advice to Aspiring Data Scientists
According to you, what are the top skills, both technical and soft-skills that are needed for Data Analysts and Data Scientists?
- Good in DBMS
- EDA and Visualisations
- Math behind the models
- Unbiased thinking
- Out of box thinking
How much focus should aspiring data practitioners do in working with messy, noisy data? What are the other areas that they must build their expertise in?
Abirami R: Firstly analysts should identify the noisy data from the normal data. This changes from the problem we are trying to solve. Sometimes the data points that we assume to be noisy might actually be the data point that makes sense to business. So its purely domain knowledge along with evidence to support the data makes it good or noisy. Discarding or retaining them is a call to be taken by the analysts.
What is your advice for newbies, Data Science students or practitioners who are looking at building a career in Data Analytics industry?
- Database Management System – (like MySQL, PostgreSQL)
- Finding patterns in the data (EDA) – Tableau
- Strong quantitative skills (Mathematics and Statistics)
- Programming Skills –(like R, Python)
- Machine Learning
The above skills in the same order of precedence.
What are the changing trends that you foresee in the field of Data Science and what do you recommend the current crop of data analysts do to keep pace?
Abirami R: Deep Learning and Artificial Intelligence.
Deep Learning in Data Science is going to groom. By end of 2018 we will see a lot of improvement and approach in image/audio/video analysis and classification. Many mundane tasks would get automated – like surveying a site would be automated by drone taking images and an AI product analysing and publishing result. There will be enhancements in hardware capability also.
To know more about Abirami R, you can check out her LinkedIn profile.
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