BluePes Blog: Insights & Trends

What is Data Science?

In recent years, data science has become increasingly prominent in the common consciousness. Since 2010, its popularity as a field has exploded. Between 2010 and 2012, the number of data scientist job postings increased by 15 000%. In terms of education, there are now academic programs that train specialists in data science. You can even complete a PhD degree in this field of study. Dozens of conferences are held annually on the topics of data science, big data and AI. There are several contributing factors to the growing level of interest in this field, namely: 1. The need to analyze a growing volume of data collected by corporations and governments 2. Price reductions in computational hardware 3. Improvements in computational software 4. The emergence of new data science methods. With the increasing popularity of social networks, online services discovered the unlimited potential for monetization to be unlocked through (a) developing new products and (b) having greater information and data insights than their competitors. Big companies started to form teams of people responsible for analyzing collected data.

• Mykola Lavrskyi
• 6 min

Real Life Data Science Applications in Healthcare

Due to healthcare's importance to humanity and the amount of money concentrated in the industry, its representatives were among the first to see the immense benefits to be gained from innovative data science solutions. For healthcare providers, it’s not just about lower costs and faster decisions. Data science also helps provide better services to patients and makes doctors' work easier. But that’s theory, and today we’re looking at specifics.

• Mykola Lavrskyi
• 5 min

Predictive Analysis in Business

Decision-making in business is often based on assumptions about the future. Many companies aspire to develop and deploy an effective process for understanding trends and relationships in their activity in order to gain forward-looking insight to drive business decisions and actions. This is called predictive analytics. We can define predictive analytics as a process that uses data and a set of sophisticated analytic tools to develop models and estimations of an environment's behavior in the future. In predictive analysis, the first step is to collect data. Depending on your target, varied sources are using, such as web archives, transaction data, CRM data, customer service data, digital marketing and advertising data, demographic data, machine-generated data (for example, telemetric data or data from sensors), and geographical data, among other options. It is important to have accurate and up to date information. Most of the time, you will have information from multiple sources and, quite often, it will be in a raw state. Some of it will be structured in tables, while the rest will be semi-structured or even unstructured, like social media comments. The next important step is to clean and organize the data - this is called data preprocessing. Preprocessing usually takes up 80% of the time and effort involved in all analysis. After this stage, we produce a model using already existing tools for predictive analytics. It is important to note that we use collected data to validate the model. Such an approach is based on the main assumption of predictive analytics, which claims that patterns in the future will be similar to the ones in the past. You must ensure that your model makes business sense and deploy the analytics results into your production system, software programs or devices, web apps, and so on. The model can only be valid for a certain time period, since reality is not static and an environment can change significantly. For example, the preferences of customers may change so fast that previous expectations become outdated. So, it is important to monitor a model periodically. There are plenty of applications for business based on predictive analytics. To conclude this article, we will briefly consider some of them.

• Mykola Lavrskyi
• 5 min

Predictive Analytics Workflow

Many companies use predictive models in their activity to provide better customer service, sell more products and services to customers, manage risk from fraudulent activity, and better plan the use of their human resources (to list a few important examples). How does predictive analysis offer all of these benefits? In this article we will consider the process of predictive analytics, and its related advantages.

• Mykola Lavrskyi
• 4 min