Ashish is a techology consultant with 13+ years of experience and specializes in Data Science, the Python ecosystem and Django, DevOps and automation. He specializes in the design and delivery of key, impactful programs.
Data Science is the fastest emerging field in the world. It analyzes data extraction, preparation, visualization, and maintenance. Data scientists use machine learning and algorithms to bring forth probable future occurrences. Organizations analyze themselves to grow. Data Science in the future will be the largest field of study.
Data Science is the study of data such that a pattern emerges from it. This pattern helps in making better business decisions. It's not something new, but the application of Data Science has been tremendous in this age of the internet. Data Science combines business and mathematics by employing a complex algorithm to the knowledge of the business. As a result, you can have a prediction model for your business with just a dash of statistics.
Not only in business, but data analysis is also paramount in various fields like predicting disease outbreaks, weather forecasting, recommendations in healthcare, fraud detection, etc.
Before a data scientist can draw any conclusion, it goes through five stages, also called the lifecycle:
Data Science bootcamps are also a great way to learn about these skills and more in a short time. Data Science jobs in the future are about to explode, which is discussed in the article further.
So now that we understand data science, the question arises how viable is it in the future?
The answer is- very! Bill Gates once said, "Content is King." But Data is the queen. Consider this… twenty-five years ago, when the internet was still a thing of the future, the local grocers were still using Data Science to analyze which products were selling more and which were selling less. Then, based on this data, they would order the next batch of groceries. This was data analysis, albeit at the crudest level, but it was still data analysis.
So, with the advent of the internet, this analysis is becoming increasingly sophisticated with the use of artificial intelligence, or AI and machine learning. Moreover, as the economy evolves, learning consumer behavior will be the chief tool for marketing. We are at the very cusp of the data collection explosion in such a case. There is currently a shortage of Data Science engineers.
The world is data-driven, and the need for qualified data scientists will only increase in the future. People now realize the importance of their data privacy on the so-called "free apps". On the other hand, giant companies like Facebook, Amazon, Flipkart, etc., are collecting data at an alarming rate.
As the data on the internet grows exponentially, the contribution to Data Science will also increase in the same way, and so will the Data Science job future. Whether fraud detection in a bank or finding a country's happiness index, Data Science will be around for a long time. The industries that are sure to benefit the most are:
The future looks like a place where data will rule our every decision.
When a field is as popular and emerging as Data Science, there is bound to be a lot of competition and opportunities. Therefore, there is a need for a data scientist in every industry. Self-analysis is vital if any business needs to grow and stand out. A data scientist does this analysis. So, the job of a data scientist is very high in demand and will remain as such in the near future.
A data scientist uses various tools and recognizes the pattern in data. So, the most logical next question is - How do you become a data scientist?
First, let's talk about the skill set required to become a good data scientist. A data scientist works with quantum computing. Therefore, the most important thing to know is programming languages like Java, Python, R, SAS, SQL, etc. Additionally, a data scientist understands Big Data frameworks like Pig, Spark, and Hadoop. Finally, deep learning and Machine learning can help take your career forward.
A data scientist can take these skills forward and become a specialist in an industry with hands-on experience to become highly sought after. A certification in a Data Science course is highly recommended to develop the portfolio. Additionally, hands-on experience is a must to build up that resume.
As we have seen that a data scientist uses an amalgamation of several subjects, higher qualification is generally preferred for a data scientist. An advanced degree in Mathematics or Statistics is seen as a plus point in problem-solving. As many programming languages are required, a degree in computer science is also appreciated.
However, the most important of all in any job is knowledge. And knowledge of the technical aspect of programming and business acumen is fundamental. The skills that a data scientist should focus on are
SAS stands for Statistical Analytics Software. This is used for the management of information, analysis, and reporting.
This software is used for analyzing, cleaning, and analyzing complex data.
This programming language is used for statistical computing and graphic support.
This is a programming language that is used for managing data.
This is a java-based language used to process extensive data. It has growing popularity; however, it is not required to become a data scientist.
These technical skills are necessary for data scientists to excel in their field. However, if a data scientist wants to leave their mark, they should work on the following non-technical skills.
Understanding the business is very important for data scientists if they are willing to take the organization to the next level. Mitigating an organization's problems should be a data scientist's primary goal.
Soft skills are a relevant requirement in every job. A data scientist should be able to communicate effectively. The data findings must be aptly communicated to make better business decisions.
Statistics is one of the essential parts of data science. The analyzed data is represented in one of two forms, inferential or descriptive.
Topics like probability and linear algebra play a vital role in the study and practice of data science.
Finding a solution to complex problems is an everyday task for a data scientist. Training your brain to think logically is a skill a data scientist can acquire.
Various courses online are also a great way to upskill. For example, KnowledgeHut Data Science Bootcamp is one of the most reputed online courses to improve. Whether at a beginner, intermediate, or even an expert level, upgrading yourself is always a good idea because Data Science is the future.
Data has applications in almost every field. The Data Science future is studded with career opportunities. Future of Data Science 2030 is estimated to bring opportunities in various areas of banking, finance, insurance, entertainment, telecommunication, automobile, etc. A data scientist will help grow an organization by assisting them in making better decisions.
There are three types of Data Science careers:
These roles are closely related and sometimes overlapping. For example, a data scientist can perform the role of a Data Scientist or a Data Engineer.
This all seems like a rosy picture with the ever-growing opportunities in this field. However, the reality is that every industry is bound to be automated. There is already software that can efficiently perform the analysis.
Artificial intelligence and Machine learning are bound to take the place of human beings in this field too. So, the Data Science demand in the future be fulfilled by AI? The answer is yes and no. The data scientist will become increasingly qualified as a quantum theorist to take advantage of this highly evolving technology.
The future of Data Science jobs will look like the middleman who can communicate with computers and humans.
AI and Machine learning are just tools that a data scientist uses to deal with big data. Data Science and Machine learning go hand in hand.
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The most hush-hush question about any job is the salary. When you become a data scientist and excel in your field, you can earn upwards of 100k USD.
If you are choosing a career option or are considering a change in your career, becoming a data scientist is a viable option; if you are passionate about computer languages and statistics, Data Science is the way.
Just remember always to update your knowledge and keep up with the current trends.
Yes, Data Science has applications in almost every industry, so it has endless opportunities in the near future. Data Science.
Yes, with the advent of technology, there has been an increased demand for data science.
Yes, as you gain more and more experience in this field, the salary also starts to grow.
It can be. If you enjoy working with numbers and data and solving complex problems, the stress can be reduced.
Yes, if they like working with numbers.
For the next few decades, Data Science prospects in the future are at no risk of being replaced by automation.
Yes, it would be a fun career if you enjoy playing around with numbers. If you like solving complex problems, it will be a fantastic opportunity.