Data Science and Databases

Microsoft’s Power BI is one of the most popular software solutions used to perform social network analysis. Here’s how to create custom Power BI visuals in R for compelling and flexible results.

Understanding total spend is a common challenge for cloud users, especially on projects with complex pricing models. This article explores the top AWS cost optimizations that will help you scale your platform effectively.

Centrality and text analysis allow users to get more out of their social network data. Here’s how you can leverage them using R and Gephi.

Colloquial language doesn’t lend itself to computation. That’s where natural language processing steps in. Learn how topic modeling helps computers understand human speech.

Thanks to rapid advances in technology, large amounts of data generated on social networks can be analyzed with relative ease, especially for those who use the R programming language and Gephi.

Two heads are better than one. This proverb describes the concept behind ensemble methods in machine learning. Let’s examine why ensembles dominate ML competitions and what makes them so powerful.

Data inundates us like never before—how can we hope to analyze it? Graphs (networks, not bar graphs) provide an elegant approach. Find out how to start with the Python NetworkX library to describe, visualize, and analyze “graph theory” datasets.

How source code becomes a running program is often opaque: “Just run the compiler” is all that developers normally need to know.

Writing an interpreter from scratch—including its lexer and parser—is an illuminating challenge.

Indexes and partitioning can help with SQL performance, but they’re not cure-alls. Through everyday examples of date range and LIKE queries, find out how to “think like an RDBMS” to make yours run faster.

Harnessing the potential of machine learning for computer vision is not a new concept but recent advances and the availability of new tools and datasets have made it more accessible to developers.

In this article, Toptal Software Developer Teimur Gasanov demonstrates how you can create an app capable of identifying handwritten digits in under 30 minutes, including the API and UI.

Data quality is a crucial element of any successful data warehouse solution. As the complexity of data warehouses increases, so does the need for data quality processes.

In this article, Toptal Data Quality Developer Alexander Hauskrecht outlines how you can ensure a high degree of data quality and why this process is so important.

Sorting a table can make some queries faster—but the maintenance cost is untenable. Enter real database indexes and their most common implementation structure: the B-tree.

The Tanagra.js library is designed to be simple and lightweight, and it currently supports Node.js and ES6 classes. The main implementation supports JSON, and an experimental version supports Google Protocol Buffers.

Retailers often face supply and demand issues that cause them to miss out on potential sales or tie up a lot of money in overstocked products.

In this article, Toptal Data Scientist Ahmed Khaled explains how retailers can boost revenues and cut costs with sales forecasts backed by artificial intelligence.

Working with non-numerical data can be challenging, even for seasoned data scientists. To make good use of such data, it needs to be transformed. But how?

In this article, Toptal Data Scientist Yaroslav Kopotilov will introduce you to embeddings and demonstrate how they can be used to visualize complex data and make it usable.

TensorFlow is one of the leading tools for training deep learning models. Outside that space, it may seem intimidating and unnecessary, but it has many creative uses—like producing highly effective adversarial input for black-box AI systems.