We call Data Science a set of tools that allow us to extract valuable information from raw data. It is a field that covers multiple disciplines, such as statistics, mathematics or programming, as well as business knowledge in general and the sector to which it is applied in particular.

Data Science, Data Analysis, Data Mining and Big Data are some of the terms that we hear more and more in our environment and that are already part of everyday life in the business world. However, for many they are words typical of engineering or statistics.

Two big questions arise that are difficult to answer concretely. What is their meaning? and how can they help us? The reality is that the amount of data available today is immense and according to the IBM Marketing Cloud, 90% of the current data has been generated in the last ten years.

Defining various terms related to data science

If we investigate within Data Science, there are several disciplines that we need to know:

• Data Mining: Data mining is the process of data extraction, obtaining potentially useful information from where there seemed to be only chaos. Normally, in the first step what we get is unstructured data and, after going through the entire process, we generate valuable information for the brand.
• Big Data: A very popular concept in recent times. This discipline works with large amounts of data, to the point that it may be necessary to have several computers to process it. Typically, data science projects handle high-volume data, so the use of this term is justified. In addition, it is a trend with great prospects for the future, because the information we generate daily from our interactions with devices and systems continues to multiply. Big Data includes structured data, semi-structured data, and unstructured data:
  • Unstructured data: digital images, audio or video files, mobile data, sensor data, web pages, social networks, emails, blogs, etc.
  • Semi-structured: XML files, system log files, text files, etc.
  • Structured data: transaction data, databases, etc.
• Artificial intelligence: In the context of data mining, we talk about artificial intelligence when we apply machine learning algorithms, such as decision trees or neural networks. We could say that data science uses artificial intelligence, but the applications of artificial intelligence go far beyond data science. In general, we call artificial intelligence the set of systems and tools that seek to simulate the logical reasoning of humans.

How Data Science can help companies?

Analyzing all this data and obtaining high-level business intelligence from it is one of the great challenges of today’s companies. And it is that with a good application of data science, we can obtain crucial information for brands such as:

1. Predict future user behaviors to make more informed decisions and reduce business risk.
2. Detect anomalies such as cyberattacks or fraud, avoiding losses for the company that can be very large.
3. Anticipate the needs of the user to send them highly personalized offers and content with greater possibilities of converting (as is already happening with companies such as Netflix or Amazon).
4. Establish patterns and trends that allow the design of new products with greater possibilities of success.
5. And in general, achieve levels of marketing segmentation and user interaction that until now we could only dream of.

The rise of Data Science

The rise of Big Data and technologies related to Artificial Intelligence are representing a radical change within the discipline of Data Science. In fact, according to IDC, the estimated number of revenues generated by Big Data and business analytics will grow from $ 130 billion, which was reached in 2016, to $ 203 billion in 2020.

By the end of this year, a third of the world’s leading companies will have already invested twice as much in Data Science solutions as in any other business tool or application.

According to the report «The Quant Crunch: Demand for data science skills is disrupting the job market«, published by IBM, the demand for engineers and data analysts will have increased by 39% by 2020.

Companies are looking for data experts

In recent years, new professional profiles have appeared that did not exist previously and that are increasingly important for companies, such as those related to Big Data and Business Analytics.

The importance of collecting and analyzing the large amounts of data currently being generated means that some of the most needed professions in the market belong to this sector. Below we are going to review some of the professional opportunities most demanded by companies in the Big Data and Business Analytics sector:

• Data analysts enable companies to maximize the value of their data assets using tools like Microsoft Power BI. As subject matter experts, data analysts are responsible for designing and building scalable data models, cleaning and transforming data, and enabling advanced analytical capabilities that deliver significant business value through easy-to-understand data visualizations.
• Data Scientist: The data scientist goes one step beyond the analyst and must have a global vision of the entire process of extracting information from data, in order to solve any problem that arises in its development. Regarding your knowledge, you will have skills in mathematics, statistics and programming, which will help you in the construction of analytical models and in the development of mathematical algorithms.
• Chief Data Officer: Above the two previous profiles is the Chief Data Officer, being the main responsible for the data of a company and in charge of defining the company’s strategy. Its functions range from the validation of the technologies used in data collection and analysis, to the security policies that are carried out for the management, storage and use of data.
• Data Architect: With extensive knowledge of programming and cybersecurity, the Data Architect is in charge of the design and implementation of software architectures in Big Data projects. In addition, you must be able to handle both structured and unstructured data, have knowledge of relational (SQL) and non-relational (No-SWL) databases and be qualified to use different tools.
• Business Data Analyst: Lastly, there is the Business Data Analyst, which will be the professional in charge of collecting the needs of business users to later transmit this data to the Data Scientists. Also, you must find a balance between the budget for data analysis and the technologies applied to improve the efficiency and productivity of the company.

Boost your career with a Microsoft certification

Microsoft exams and specially Azure certifications help you differentiate yourself and validate your knowledge and skills. Browse MeasureUp’s online training options to learn new skills that may better prepare you for your career and lay the foundation for success on Microsoft certification exams. In fact, you can choose between several paths:

• Implementing an Azure Data Solution: Candidates for this exam are Microsoft Azure data engineers who collaborate with stakeholders to identify and meet data requirements to implement data solutions that use Azure data services. Azure data engineers are responsible for data-related implementation tasks that include providing data warehousing services, streaming and batch data, transforming data, implementing security requirements, implementing data retention policies, identifying data breaches. performance bottle and access external data sources.
• Designing an Azure Data Solution: Candidates for this exam are Microsoft Azure data engineers who collaborate with business stakeholders to identify and meet the data requirements to design data solutions that use Azure data services. Azure data engineers are responsible for data-related design tasks that include designing Azure data storage solutions that use relational and non-relational data stores, batch and real-time data processing solutions, and data security and compliance solutions.

Do you want to know if you are ready for the exam day? Test your knowledge with Microsoft Official Practice Tests.

P.S. If you find this article interesting, then you should have a look at the top 10 benefits of automation testing.