What is data science?

Only consistent data strategy and analytics pave the way for data-driven business models. Data science plays an essential role here: it enables companies to use their business data to reduce costs, open new business opportunities, or improve customer experience. Here’s what you should know about data science.

Data science is a way to gain insights from both structured and unstructured data. Various methods are used – from statistical analysis to machine learning. Most companies use data science to turn data into value in the form of:

Data science gives purpose to the data an organization collects.

Data science is generally a collegiate discipline. Data scientists are at the core of most teams in this field – but the journey from data to analysis to value production requires the integration of different skills and roles. For example, data analysts must be on board to maintain data models and examine data before it is presented to the team. Data engineers are needed to create the necessary pipelines to enrich data sets and make information available across the enterprise.

The goal of data science is to develop the means to derive business-oriented insights from data. This requires an understanding of how value and information flow within the organization – and the ability to use that to identify business opportunities. While these can be one-off projects, data science teams typically attempt to identify key data assets that can be turned into data pipelines that feed maintainable tools and solutions. Examples include solutions that banks use to prevent credit card fraud or tools that assist in the placement of wind turbines.

The business value of data science depends on the needs of the company: data science, for example, can help a company develop tools that predict hardware failure. This made it possible to avoid unplanned downtime and to better plan maintenance work.

Although closely related, data analysis is a component of data science used to understand what a company’s data looks like. data science Uses results Data AnalyticsTo solve the problems.

The difference between data analysis and data science also lies in the time scale: data analytics describes the current state of reality, while data science uses this data to make predictions about or better understand the future.

Production engineering teams work on sprint cycles with specific schedules. This is often difficult for data science teams, since a significant amount of time can usually be spent up front deciding if a project is feasible at all, but before the team can answer this question, the data must first be collected and cleaned.

Ideally, data science should follow a scientific method, even if this is not always the case or is not possible. The principle applies: science takes time. You spend some time confirming your hypothesis and then a lot of time disproving yourself. However, in the business world, time is of the essence. For data science, this often means accepting a result that is ‘good enough’ but not ‘perfect’. However, there is a risk that the results will fall victim to confirmation bias or overfitting.

Data science teams use a wide range of tools including SQL, Python, R, Java, and a large number of open source projects such as Hive, oozie, and TensorFlow. These tools are used in a variety of data-related tasks – from data mining and data cleaning to computational analysis of data using statistical methods or machine learning. Popular data science tools include:

• SAS: This special statistical tool is used for data mining, statistical analysis, business intelligence, clinical trial analysis, and time series analysis.

• face: This popular data visualization tool is now part of Salesforce.

• TensorFlow: The Machine Learning Software Library was originally developed by Google and licensed under the Apache 2.0 License. TensorFlow is used, among other things, to train deep neural networks.

• Data Robot: The automated ML platform is used to build, deploy, and maintain AI instances.

• BigML: This machine learning platform focuses on creating and sharing data sets and models.

• a knife: An open source platform for data analysis, reporting and integration tasks.

• Apache Spark: This unified analysis engine is designed to handle large amounts of data and support data cleansing, transformation, modeling, and evaluation.

• RapidMiner: The data science platform is designed to help teams prepare data, machine learning projects, and predictive analytics models.

• Matplotlib: The open source Python library provides tools for creating static, animated, and interactive visualizations.

• Excel: Microsoft spreadsheet software is perhaps the most widely used business intelligence tool. However, Excel is also useful for data scientists who work with smaller data sets.

• youth: This JavaScript library is used to create interactive visualizations in web browsers.

• ggplot2: The Advanced Data Visualization Package for R allows data scientists to transform analyzed data into visualizations.

• Jupiter: This open source Python-based tool is used for live code execution, visualizations, and presentations.

While the number of data science majors is growing rapidly, their graduate degrees are not necessarily what data science companies are looking for. For example, companies with a background in statistics are popular, especially if they have the experience and ability to communicate results to business users.

Many companies are also looking specifically for applicants with a PhD – particularly in physics, mathematics, computer science, economics, or social sciences. Many see a PhD as proof that a candidate is able to research a particular topic thoroughly and pass on information about it to others.

Many in-demand data scientists or data science team leaders come from non-traditional backgrounds, and in some cases even those who have nothing to do with computer science. In many cases, an enterprise data scientist’s primary skill is the ability to look at and understand relationships from unconventional perspectives.

We’ve summarized some of the most popular data science job roles and the corresponding average salary (for Germany) for you. The data basis for this is provided by PayScale’s recruitment portal:

• Data Analyst: €46,300

• Data scientist: 55,400 euros

• Data engineer: 57,800 euros

• Junior Data Analyst: €40,100

• Senior Data Analyst: 63,400 €

• Chief Data Scientist: €73,400

• Principal data scientist: €81,600

• Senior Data Engineer: 72,200 Euro

• Data manager: €67,200

• Data engineer: 76,200 euros

• Data science manager: €90,800

• Analytics Director: 66,800 €

• Analytics Manager: €107,500

• Business Intelligence Analyst: €45,900

• Research scientist: 57,300 euros

• Research Analyst: €38,600

This post is based on an article from our sister publication CIO.com in the US.