What exactly is Data Science?
Data Science is the study of obtaining insights from massive volumes of data using diverse scientific methodologies, algorithms, and processes. It aids in the discovery of hidden patterns in raw data. Because of the evolution of quantitative statistics, data analysis, and large data, the phrase Data Science has evolved.
Data Science is an interdisciplinary field that enables knowledge to be extracted from structured or unstructured data. Data science allows you to turn a business problem into a research project, then back into a practical solution.
In today's world, data is the oil. We can leverage data to create a distinct commercial edge if we have the correct tools, technology, and algorithms.
Using modern machine learning techniques, data science can assist you in detecting fraud.
It allows you to construct intelligence in machines.
It allows you to perform sentiment analysis to evaluate consumer brand loyalty.
It allows you to make better and faster decisions.
It allows you to offer the correct product to the right customer to improve your business.
The demand for data science stems from data-driven decision making. That is the straightforward solution to this question. To be a successful business in the twenty-first century, you must use data to your advantage.
Previously, many people did this by analysing data in Excel, but now anyone can access and use data-crunching tools like:
1. Google Analytics – A cloud-based digital marketing service.
2. Tableau and Power Bi – Business intelligence data visualisation tools
3. Python and R – Programming languages that are used to do complex analyses with a few lines of code.
The world's largest corporations are data science-driven firms. Consider Google, Amazon, and Facebook. Each employs data science to develop algorithms that boost consumer pleasure while increasing earnings.
1. Google — Page ranking to ensure that the top links give a response to any sought question.
2. Amazon – Product recommendations based on historical consumer behaviour and preferences.
3. Facebook – Targeted ads (they know what sports you like, what price range you want, what food you like, and so on) to boost market success.
Finally, the main reason demand remains high is because if your competitors rely on data-driven decision making while you do not, they will surpass you and steal your market share.
As a result, businesses must adapt and adopt data science tools and approaches or risk being thrown out of business.
Data Scientists are in short supply, owing to the fact that the discipline is still in its infancy even in 2021.
You know, 20 years ago, learning data science was impractical due to poor internet connections and low computational primitive programming languages. However, as time passed, computer power increased dramatically, allowing data science to become a reality.
This exponential expansion and interest in the subject were unpredictably high, and traditional education was not prepared to accommodate the needs of those who wanted to learn about this rapidly expanding field.
Few programmes have been developed to educate aspiring Data Scientists. This is supported by studies, which indicate that persons who enter the sector typically come from various fields like business, psychology, and life sciences.
The majority of those who shifted learnt their abilities through self-preparation, such as reading books and taking online courses...Not through the traditional education system.
Statistics: Statistics is the most important unit in Data Science fundamentals. It is the method or science of collecting and analysing massive amounts of numerical data in order to gain relevant insights.
Visualization: This approach allows you to access large volumes of data in easily understandable and digestible visuals.
Machine Learning: This is the study and development of algorithms that learn to make predictions on unforeseen/future data.
Deep Learning: Deep Learning is a revolutionary machine learning research method in which the algorithm chooses the analysis model to use.
Data Science Jobs Roles
The most prominent Data Scientist job titles are:
Data Scientist: A Data Scientist is a professional who manages massive volumes of data in order to create compelling business visions utilising numerous tools, approaches, methodologies, algorithms, and so on.
Data Engineer: A data engineer's job is to work with vast amounts of data. He creates, builds, tests, and maintains designs such as large-scale processing systems and databases.
Data Analyst: A data analyst is in charge of mining massive amounts of data. In data, he or she will look for links, patterns, and trends. Later, he or she will provide engaging reporting and visualisation in order to analyse the data and make the best business decisions.
Statistician: A statistician uses statistical theories and procedures to collect, analyse, and comprehend qualitative and quantitative data.
Data Architect: An information technology (IT) specialist who designs and manages data systems, establishes policies for how data is stored and accessed, coordinates various data sources within an organisation, and integrates new data technologies into existing IT infrastructures is known as a data architect.
Data Administrator: The data administrator is responsible for ensuring that the database is available to all necessary users. He also ensures that it is operating properly and is secure from hacking.
Business Analyst: This individual is responsible for improving business procedures. He/she serves as a liaison between the business executive team and the IT department.
Data/Analytics Manager: Data analytics managers oversee a team of data analysts. They supervise an analytics department's work and ensure its accuracy. They are also in charge of designing systems for efficient data analysis and reporting. They will require a combination of data analysis and business abilities to accomplish so.
If you've been reading this post, you've probably made an educated guess about the increase in data science careers.
Since 2012, there has been a 650 per cent increase in data science occupations, according to LinkedIn. Glassdoor provides proof for this allegation since they had approximately 1700 job listings in 2016 with data science as the major role. That figure grew to 4500 in 2018, before levelling off around 6500 in 2020.
COVID-19 was the big news in 2020, and it's likely that's what's causing this levelling out. Overall, IT jobs have held up well during the pandemic, which is in its tenth month.
Demand for Data Scientists remains high, while supply is limited. According to IBM, this trend will continue for many years to come. The United States Bureau of Labor Statistics is another trustworthy source that agrees with this statement.
Is data science still a growing field in 2021? Yes, it is a resounding YES! Data Scientists are in high demand all over the world, and the absence of competition for these positions makes data science a very lucrative career choice.