DATA SCIENCE: FIND OUT WHAT YOU NEED TO KNOW ABOUT THE SUBJECT
Data science is one of the most promising careers on the market. Today, professionals in this field understand that they must progress beyond the usual skills of analyzing large amounts of data, data mining and programming skills. Anyone who works in the IT area has probably heard a lot about Data Science. It is a relatively new technological aspect, but it has aroused the interest of large organizations and proved to be a good investment for IT professionals looking for a place in the market.
But what exactly is Data Science and what are its main features?
I will explain the concept and clarify the main doubts about the topic. Let’s start!
What is Data Science?
Currently, there are numerous ways to produce and collect Data. Corporate spreadsheets and reports, social media, blogs, websites and wearable devices are just some of the ways data is increasingly collected and stored. Transforming this raw data into information that has meaning for organizations, providing the solution to problems or the acquisition of competitive advantages, is the challenge that presents itself.
This is the goal of Data Science. In general, it allows the extraction of knowledge from large databases, structured or not. With this, it is possible to obtain specific answers and insights that, in a traditional way, would not be achieved in a timely manner.
What is the difference between Data Science and Business Intelligence?
It is important to start with some basic definitions of the two terms, studying more carefully the two distinct yet closely related fields in Data Analysis.
Data science, as used in business, is data-driven, where a lot of interdisciplinary knowledge is applied together to extract meaning and understandings from available business data, which is typically large and complex.
While Business Intelligence (BI) helps to monitor the current state of business data to understand the historical performance of a business. BI helps to interpret past data, Data Science, on the other hand can analyze preceding data (trends or patterns) to make future predictions.
Therefore, BI is mostly used for reporting or descriptive analytics, whereas Data Science is mostly used for predictive analytics or prescriptive analytics.
What are the areas of knowledge encompassed?
Due to the need to manage data from different sources, quickly and focused on specific objectives, Data Science involves direct action in technical areas, such as traditional computing, mathematics, statistics and engineering.
However, since its result also has an impact on sectors such as the digital economy, health and finance, Data Science still works with concepts related to competitive intelligence, administration and business. Finally, given the versatility it demonstrates, this technique is also widely used in academia, especially in the development of machine learning, in applications such as search engines, automatic translation and voice recognition.
Below is a list of the main areas covered by data science:
data mining;
cloud computing;
database management;
business intelligence;
deep learning;
data visualization.
Who needs a data scientist?
Considered by the Harvard Business Review as the most coveted professional of the 21st century, the data scientist is the professional who manages to integrate the areas of knowledge necessary to extract relevant information from the available databases. This characteristic alone justifies the search by companies of all sizes and segments for people capable of coordinating complex processes of data cleaning, analysis and modeling, as well as improving algorithms based on business needs. For several years in a row, Glassdoor has ranked data scientist the best job in the United States. In Brazil, the profession is also growing, offering professionals very attractive salaries. The constant demand for data science professionals in all fields, large and small, is being challenged by the shortage of qualified candidates available to fill available positions.
What does a data scientist do?
It is no wonder that large organizations in the health and technology area seek the best professionals in this area. It is a relatively new profession that combines computer science, engineering and statistics skills, as well as insight into the business and ability to collaborate and creativity.
These professionals are empowered, data-driven people with high-level technical skills. They are prepared to build complex quantitative algorithms, whose objective is to structure and synthesize vast amounts of information used to answer questions and drive strategies in your organization. This is combined with the communication and leadership expertise indispensable to deliver concrete results to multiple stakeholders in an organization or business. As a result, a multidisciplinary and specialized training is the recommendation for those who want to follow this promising career and stand out in the market.
What to do to become a data scientist?
Analyze your profile
Analyzing your affinities and qualifications is a good idea. Self-assessments are resources used by professionals all over the world to help them identify what is the next step in their careers — and doing them is quite simple. With a list of the talents, you already have and those you are most likely to develop, you will be able to get a good idea if a career in Data Science is right for you. There are two types of skills needed to become a good data scientist, they are: hard skills and soft skills. The main differences between these two competencies are how they are acquired and used in performing tasks. Hard Skills are often acquired through technical courses or specific training. They include skills related to how to use a particular machine, software or other application.
Some other examples of hard skills are:
familiarity with programming languages — those who already know some programming languages, even if they are not used in Data Science, will be able to learn SAS, Python or R in a shorter time;
logical thinking — ideal for understanding how data are related and what you can learn from them;
Number Skills — Mathematics and statistics are tightly integrated into the data scientist’s routine.
Soft skills are more seen as personality traits that you may have developed during your lifetime. They are put to the test when you manage your time, communicate with others, or tackle a complex problem for the first time.
Here are some examples of soft skills:
Communication: Effective communication skills are useful during the interview process and in your professional career. This competence involves knowing how to talk to other people in different situations or environments;
ability to solve problems: this ability is highly valued by companies. Using creative methods to solve difficult problems can greatly help business development;
adaptability: if you work in technology or a startup, adaptability is very important. Transformations in processes, tools or customers with which the company works can happen quickly.
Upgrade your computer settings
To learn you need the right equipment. If you are unable to process the software that is part of the work, you will have difficulty understanding how data science works in practice and you will drop out of studies in a short time.
A computer with at least 8 GB of memory and a processor starting from the i5 should be your priority so that you can continue your specialization.
look for qualification
SAS, Big Data, Python or R and Machine Learning are some of the skills you need to develop to become a data scientist. The fundamentals of Big Data are critical for you to understand where the data you will analyze originates. Tools like Hadoop, Spark and Data Warehouses will be part of your routine, so it is necessary to choose at least one of these technologies to master.
Languages like SAS, Python and R are best suited for analyzing large groups of data. Python and R free, have an active community of professionals who help each other over the internet and are adopted by the market to make Data Science possible. Learning them before entering a Data Science course will put you one step ahead of the competition. Lastly, there are Machine Learning algorithms. Machine learning is an essential resource to benefit from the full potential of data and you need to learn how it works and what kind of tasks it can automate. Mining data with Machine Learning is much easier and good data scientists know this. Starting by understanding what differentiates supervised from unsupervised learning is a good idea to define which one is most relevant to the role you want to have.
learn about business
Most Data Science professionals are self-employed, serving clients around the world. Therefore, from developing the ability to prospect and win customers is essential. Convincing customers that their problems can be solved with effective data analysis, for example, is something you must be able to do to stand out in the market.
Hunt for constant update
Never forget one thing: Data Science is a rapidly advancing field that, year after year, evolves and requires different skills from professionals working in the area. Keeping up with technology, taking new courses whenever possible, and updating your certifications is what will make you a coveted professional. Investing in a career in Data Science is a great decision. In the coming years, the demand for these professionals will only increase, and entering the area as soon as possible can help you to conquer a good space in the job market.
