Only a consistent data and analytics strategy paves the way to data-driven business models. Data science plays an essential role here: It enables companies to use their business data to reduce costs, open up new business opportunities or optimize the customer experience. Here's what you should know about data science.
Data science is a method to gain insights from structured and unstructured data. Various approaches are used – from statistical analysis to machine learning. Most companies use data science to turn data into value in the form of:
Sales increases,
Cost reductions,
Business agility,
Optimized customer experiences or
Newly developed products.
Data science gives a purpose to the data collected by an organization.
Data science is generally a team discipline. Data scientists form the core of most teams in this field – but the journey from data to analysis to production value requires the integration of different skills and roles. For example, data analysts should be on board to maintain data models and examine the data before presenting it to the team. Data engineers are needed to create the pipelines needed to enrich datasets and make the information available across the enterprise.
The business value of data science depends on the needs of the company: data science can, for example, help a company to develop tools that predict hardware failures. This enabled unplanned downtime to be avoided and maintenance work to be better planned.
Although closely related, data analysis is a component of data science used to understand what a company's data looks like. Data science uses the results of data analytics to solve 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 in sprint cycles with set schedules. This is often difficult for data science teams, since a significant amount of up-front time can usually be spent determining whether 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 not feasible. The principle applies: science takes time. You spend a little time confirming your hypothesis and then a lot of time disproving yourself. In the business world, however, time is of the essence. For data science, this often means settling for a result that is "good enough" but not "optimal". However, there is a risk that the results will fall victim to confirmation bias or overfitting.
SAS: This proprietary statistical tool is used for data mining, statistical analysis, business intelligence, clinical trial analysis, and time series analysis.
Tableau: The 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 License 2.0. TensorFlow is used, among other things, to train deep neural networks.
DataRobot: The automated ML platform is used to build, deploy and maintain AI instances.
BigML: This machine learning platform focuses on creating and sharing datasets and models.
Apache Spark: This unified analytics engine is built to handle big datasets, supporting data cleansing, transformation, modeling, and evaluation.
RapidMiner: The data science platform is designed to support teams with data preparation, ML projects and predictive analytics models.
Excel: Microsoft's spreadsheet software is perhaps the most widely used business intelligence tool. However, Excel is also useful for data scientists working with smaller data sets.
js: This JavaScript library is used to create interactive visualizations in web browsers.
ggplot2: This advanced data visualization package for R enables data scientists to turn analyzed data into visualizations.
While the number of data science majors is growing rapidly, their graduate degrees aren't necessarily what data science companies are looking for. For example, companies with a background in statistics are popular, especially if they have expertise and the ability to communicate results to business users.
Many companies are also specifically looking for applicants with a doctorate – especially in physics, mathematics, computer science, economics or social sciences. Many see the PhD as proof that a candidate is able to research a particular topic thoroughly and to pass on information about it to others.
Many in-demand data scientists or data science team leads come from non-traditional backgrounds, in some cases even one that has very little to do with computer science. In many cases, the key skill of an enterprise data scientist is being able to look at and understand relationships from non-traditional perspectives.
We have summarized some of the most popular job roles in data science and their corresponding average salary (for Germany) for you. The data basis for this is provided by the PayScale career portal:
Data Analyst: 46,300 euros
Data scientist: 55,400 euros
Data engineer: 57,800 euros
Junior Data Analyst: 40,100 euros
Senior Data Analyst: 63,400 euros
Senior Data Scientist: 73,400 euros
Lead data scientist: 81,600 euros
Senior Data Engineer: 72,200 euros
Data manager: 67,200 euros
Data Architect: 76,200 euros
Data Science Manager: 90,800 euros
Analytics Manager: 66,800 euros
Director of Analytics: 107,500 euros
Business Intelligence Analyst: 45,900 euros
Research Scientist: 57,300 euros
Research Analyst: 38,600 euros
This post is based on an article from our US sister publication CIO.com.
