Data Science has become a hot topic these days. But what exactly is data science? Let’s discuss what is data science, what is its origin and what are its main applications. In addition, we will also discuss what are the available career paths for Data Science field.
The Revolution of Data
With technological innovation, there are lots of applications we use in our day today life. Every day, every second, those applications generate millions of data. If we can collect, store, analyze and transform those data to information, we can accomplish revolutionary innovations and advancements in any field or industry.
Throughout human history, the landmarks of our civilization have been characterized by advances in our ability to observe and collect data. Our distant ancestors developed practical tools and methods to measure distance, weight, volume, temperature, time and location.
Throughout modern history, even small amounts of data have provided us with important information in the search for solutions to some of our biggest challenges. We started recording of information on stone, and then evolved it to printed books. Later, the invention of computers became the main motivation for the humans to dig in to data and find new possibilities.
The beginning of data science
The main reason that lead to the existence of data science is the increase in unstructured data, available with the digitalization of information. This large volume of unstructured data is commonly known as Big Data.
The second important factor was the advancement in cloud computingwhich gave massive processing power through horizontal processing with clusters. Without this increase in processing capacity, data science would certainly not exist. This is because traditional vertical processing is expensive and inefficient for processing large amounts of data.
This problem was solved, mainly, through the specialization of computational capacity made available by cloud computing vendors, such as GoogleCloud Platform, Amazon Web Services and Microsoft Azure. With the possibility of renting hardware on demand and its redistribution to achieve maximum efficiency, many projects have become viable with cloud computing.
Data-driven future
In this century, data scientists benefit from the rapid acceleration of this whole process and with greater abundance of data and reduced storage and processing costs to reveal valuable insights. In the past, most of the data was unprocessed, that is, it was not transformed into information. Today, with cloud processing capabilities, companies are looking forward to turn data into information to interpret it and generate insights that are important and helpful for their businesses.
With advanced data analyzing tools, data science professionals can make predictions that solve big problems and improve our daily lives. Today, many industries such as healthcare, transportation, logistics, finance etc. have advanced and transformed with the help of data science. Recommendation systems we find in YouTube, online stores, social media etc. are also developed with data science.
What is a Insight?
Businesses have problems that need solutions. In turn, solutions need decisions made based on data. Insight is the solution or the conclusion about something. From a business point of view, every decision-making process should be based on data. That’s where the importance of insights comes in.
What is data science?
With all the things we have discussed so far, now you may have a small clue about data science. Data science is the collection of data in large quantities from different sources to analyze and support decision making, in a predictive way, and generate useful insights.
Today, the profession of data science is the fastest growing in the world. Much of this is caused by the need for companies to analyze unstructured data and turn it into useful information.
It is important to remember that prediction does not guarantee the future, it is just a tool to improve the decision-making process. Data science, is the process that extracts data from different sources, at different speeds, processing large quantities (big data) and generate valuable insights. Generally, the data science process consists of defining problems or issues, preparing, exploring, concluding and communicating.
Skills needed for Data Science
The main pillars of data science are mathematics, statistics, business, data mining and visualization, programming and computing. However, statistics and mathematics are the basis of data science, because data analysis models or algorithms, are built with the help of statistics and mathematics.
What is the difference between Data Science and BI?
This is a common question that many people get confused. Data Science and Business Intelligence (BI) looks same, but their approaches, technologies and functions differ in several ways.
Business intelligence uses structured data to provides a historical report of the data (dashboards, reports) to compare historical data to current data to identify trends. The goal of business intelligence is to convert raw data into business insights so that business leaders can make decisions. The BI professional, the business analyst, uses tools to create dashboards and reports.
In Data science, uses both structured and unstructured data to perform an in-depth statistical analysis using machine learning (ML) to predict future performance or insights. Within data science, machine learning is used as a tool to automate the transformation of data into information.
The main difference between the two is that business intelligence deals with data from the past to find trends while data science will deal with the future, based on predictive analysis.
Data Science Applications
Data science has several practical applications. Some of them are the recommendation of products in online retail stores, voice recognition (deep learning), the treatment of diseases based on data correlations and facial recognition.
Today, several technology giants are investing heavily in deep learning technologies for speech recognition. Voice assistants like Google Assistant, Alexa (Amazon), Cortana (Microsoft) and Siri (Apple) are some examples of conversational technologies, which allow the user to interact with an artificial intelligence assistant through voice commands. This technology is developed by transformation between unstructured data (voice) into useful information (computational commands).
Data Science Careers
The data science career is one of the most demanding career path these days. According to the U.S. Bureau of Labor Statistics the rise of data science needs will create 11.5 million jobs by 2026.
- Data scientist: Professional with a strong background in computer science, mathematics and statistics. A data scientist can analyze large amounts of data and come to conclusions (insights) or generate predictions. It is certainly a more complete profile, which mixes business knowledge.
- Data Engineer / Machine Learning Engineer: The Data Science area also needs a professional with a technological and infrastructure profile. Due to the large amount of data that this professional will work with, the administration of clusters is necessary for parallel processing of the data, whether structured or unstructured. The data engineer must be able to prepare the data, creating data lakes and data warehouses for consumption by data scientists.
- Business Analyst: As in the BI area, the Data Science area also needs a professional with a business profile, who can understand the heart of the company, suggesting new practices or businesses to generate more value.
The Future of Data Science
With the cost of data storage and processing falling and with the increase in the number of ways that capture more and more data, the amount of data available will be increasing which can give us answers to some of the greatest challenges in the world. It is up to professionals in this new field of science to create models to enhance the productivity of all areas. There is no restriction in any area for the work of data scientists, which is a great opportunity to make human effort more and more efficient.
