There’s no doubt that the field of data science is booming. The emergence of data science has changed many industries, including agriculture, healthcare, education, and marketing. In addition, there are specialties within the field that have shown to be lucrative as well.
Data science is nothing more complicated than scientists extracting data and information from big data and analyzing it. That data comes from multiple sources – search engines, social media sites, surveys, e-commerce sites and many more places. Every day, our access to data increases. This is mostly down to how technology and techniques in collecting it have advanced and continue to advance. These days, just about anything can be monitored, including buying patterns, customer behaviour, health care, plant behaviour and so on, and many companies use this to make predictions about future. Nowadays, diseases can be predicted at the earliest stage with the help of data science in healthcare (human, animals, plants and soil) that too remotely with innovative appliances powered by Machine learning. Mobile applications and smart devices constantly collect data about heartbeat rates, blood pressure, sugar, or soil moisture, soil nutrients, plant parameters so on, transferring this data to the experts as real-time updates, who can then devise treatments accordingly. These decisions can help them plan better. However, as fast as this data grows, the more unstructured it becomes and, in that format, data is no good and it must be parsed to get any real information from us. That is where data science came into the picture, using machine learning and big data to interpret the data, enabling effective decision-making.
So, how does data science work? It uses tools from various disciplines to gather in the data, process it, and gain insights from that data, extracting the meaningful data and using it to make the right decisions. It’s that simple. Data science comprises several fields, including programming, machine learning, analytics, statistics, and data mining. With data mining, algorithms are applied to the data set, revealing patterns used to extract meaningful data. Predictive analytics or statistical measures use the data extracted to predict what might happen in the future based on what happened previously. Machine learning is part of the artificial intelligence family. It is used for processing huge amounts of data, quantities that humans simply couldn’t even comprehend, let alone process in their entire lifetime. Until recently, implementing data science skills into practical life was next to impossible, but in the last decade, new upcoming scientists did their best to implement the statistical knowledge into practical life.
The career scope in data science is more than most other job scopes globally. Data science is a field of knowledge that just keeps on growing, with no boundaries. Data science is a wise career choice, not just for the salary but the many benefits it offers. Although data science is relatively new, thanks to the positivity that surrounds it, it is now in much greater demand than ever before. You will have to complete the basic bachelor’s degree from a university in one of the quantitative streams. Learning computing languages like Python during your college days is a requirement. Python is one of the highest-level languages of all time. It is used to code and decodes other programs without much effort in no time. The higher the level of the computing language, the easier it is for the machine to understand. However, most people would agree that it’s worth pursuing a master’s degree in data science if your background meets. If you’re pursuing a master’s in data science with the goal of becoming a data scientist, you should be prepared for a long journey and lots of hard work. However, if your goal is to gainful employment with a stable salary, then it’s worth the time and effort.
Here is a list of positions in data science jobs that might be useful to you:
Machine learning scientists: One of the most critical career options in data analysis or data science is machine learning. Machine level language and information continuously modify and a highly skilled machine learning scientist helps by discovering new data and creating algorithms to get new ideas easily.
Data Engineer: Engineers are the gods of creation. They build for the people. Similarly, data engineers give an appropriate structure to the machine learning scientist’s raw data. Later, the software engineer uses the data frame from the data engineer and completes the package.
Data Analyst: Data analysts also play a pivotal role in the respective companies. They analyze the data from external sources and filter out the only necessary information for the company’s needs.
Data Consultant: The data consultants and data analysts are dependent on each other. The data consultant looks into the data analyst’s information and uses it to make the best possible business decisions.
Data Architect: To bring out the best from a company, the data architects apply useful data and information from the raw source and optimize them to apply in the best way possible for the company.
Applications Architects: These people track down the various information used in the company. The higher authorities decide on how to deal with the information based on the application architects’ report. The impact of the above aspects and positions equally matters as none of them will work well without one another. The usable information from data scientists helps bring out the best of a company, while the algorithms and new software from the data engineers work in new ways and open up new career-making aspects. With such valuable information, you can do a lot more with the perks of data sciences.
Imran Khan, Associate Professor Statistics, Global Vice president for Education LISA 2020, Div. of Statistics, SKUAST-K, Shalimar.