Olga Berezovsky

Predictive modeling (or machine learning) can be challenging to understand and use because there is no one algorithm that would work the best for every problem. In this article, I go over some use cases for logistic regression and walk through the steps of applying this classification algorithm for data forecasting.

Pandas Python library is becoming more and more popular between data scientists and analysts. It allows you quickly to load, process, transform, analyze, and visualize the data. In this article, I use some common commands for exploratory data analysis using Pandas and SQL for a public sample of random Reddit posts.

SQLite is a favorite, go-to SQL language when you have limited time and a lot of data to go through. We're sharing some useful tips and tricks for using SQLite because while the language has big advantages, it also has some limitations.

Quantitative user profiling is one approach to segmenting and defining user categories based on the data you collect. It helps to divide your user base into cohorts based on user activity level and your top marketing/product metrics: MAU/DAU (monthly/daily active users), user engagement, retention.

More and more companies are using PostgreSQL, one of the world's most popular relational database management systems (RDBMs). Personally, I am a big fan for two reasons. PostgreSQL allows you to easily distribute queries across multiple nodes running in parallel and to create custom procedures, and it also supports advanced SQL and numerous window functions, aggregates, table samplings, and other cost-based query optimizations.
This post will focus on PostgreSQL joins and show you a way to… Show more

More and more companies are using PostgreSQL, one of the world's most popular relational database management systems (RDBMs). Personally, I am a big fan for two reasons. PostgreSQL allows you to easily distribute queries across multiple nodes running in parallel and to create custom procedures, and it also supports advanced SQL and numerous window functions, aggregates, table samplings, and other cost-based query optimizations.
This post will focus on PostgreSQL joins and show you a way to connect two tables that are not related by a foreign key relationship. Show less

Working with big data you often have to apply different sampling techniques for your analysis — select a representative subset of data to identify patterns and trends in the larger data set. While there are many sampling techniques, in the article I am describing one of the simplest ways to get a randomly distributed data set from RedShift using PostgreSQL.

Building predictions and model forecasts is one of the most common challenges in data analytics. Below I am going to simulate a time series analysis and projection based on the chronological raw data.

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