Marija Ilic

Marija works as a data scientist in the banking industry. She specializes in big data platforms (Cloudera and Hadoop) with software and technologies such as Hive/Impala, Python and PySpark, Kafka, and R. Marija has an extensive background in DWH/ETL development in the banking industry. Her main interests are predictive modeling, real-time decision-making, and social network analysis. Outside of work, Marija enjoys listening to her favorite LPs on her old gramophone—and never grows tired of its soothing crackle.

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10 Apr 2020

Marija Ilic

Analyze Time Series COVID-19 Data with Window Functions

The COVID-19 pandemic really changed the way we live. No matter where you are, you're being impacted by this unpredictable virus. We do not know what the future holds, but we do believe in science and in better days to come. One thing that makes me as a data analyst particularly happy is that we have publicly available data. We can analyze that data and learn something from it. If you want to explore COVID-19 with the help of modern SQL constructions, keep reading — this article is intended for you.

4 Mar 2020

Marija Ilic

Should I Learn SQL as a Data Analyst?

So, you are a data analyst—someone who relies on data. You like to gather, clean, and transform data and come to conclusions based on data. You are a data detective. Which tool do you choose to do your job in an elegant, reproducible, and simple way? Excel? R? Python? Or should you learn SQL? Stay tuned! In this article, I will explain why SQL is a must-have skill for data analysts.

4 Sep 2017

Marija Ilic

How to Track Down Duplicate Values in a Table

When it comes to information management, duplicates present one of the most common challenges to data quality. In this article, I'll explain how it is possible to find and distinguish duplicate names with the help of the SQL data programming language. I really like my maiden name. The reason I like it so much is because it's rare. My maiden name (first with last) provided a unique identifier on platforms such as LinkedIn, Facebook, Twitter and similar.

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