Data is everywhere, and we have all seen exponential growth in the data that is generated daily. Information must be extracted from this data to make sense of it, and we must gain insights from this information that will help us to understand repeating patterns. Analysing these patterns will help us to know more about consumers and their behaviour, hence provide services and manufacture products that will benefit both the organization as well as the consumers.
This is where Data Science comes into the picture. The art of analysing the data, extracting patterns, applying algorithms, tweaking the data to suit our requirements, and more – are all parts of data science. The field has seen massive growth in the last few years and this growth may not stop for the next 5 years at least.
Some people are apprehensive about the future, but the opportunities in the field of data science are improving day by day, creating new paths for people to get in and contribute their expertise/experiences.
As mentioned previously, data is generated in large amounts daily. This data generation happens everywhere, ranging from a small organization to multi-national companies. This kind of data is also known as 'big data', given their specific characteristics in terms of volume, type of data and speed at which the data gets generated.
It is important to make use of this big data by processing it into something useful so that the organizations can use advanced analytics and insights to their advantage (generating better profits, more customer-reach, and so on).
A data scientist is a person who is trained and experienced in working with data, i.e. data gathering, data cleaning, data preparation, data transformation, and data analysis. These steps will help understand the data, extract hidden patterns and put forward insights about the data. All these processes are done with the help of algorithms which are specially designed to perform a specific task.
Many analyses have revealed that Data Scientist, Machine Learning Engineer, Artificial Intelligence Engineer are some of the most sought-after jobs. Not to forget the high pay that comes with it.
Data science is an intricate combination of mathematics, statistics, analytics, and computer science. Mathematics & statistics are required to understand the ideas behind the algorithms and their working.
On the other hand, analytics is associated with many data cleaning, transformation, preparation and analytics operations that are performed on the data with the help of computer science (programming languages).
All these skills (which a data scientist possesses) will help the businesses to thrive.
Data scientists are usually those who are able to find out why things work the way they do, why they don’t work as expected, what has gone wrong in the business and how it can be fixed. All these are different processes in the world of data analytics. They would also have to interact with potential stakeholders, discuss business challenges and help improve it.
If the general idea of stand-up meetings and sprint meetings is not taken into consideration, a day in the life of a data scientist would revolve around gathering data, understanding it, talking to relevant people about the data, asking questions about it, reiterating the requirement and the end product, and working on how it can be achieved. It looks like this:
This part deals with the collection of raw data from various resources. These resources include websites, various social media platforms, people’s profiles, and so on. All this data needs to be collected and stored in a place which is easy to access while working with the data.
This is considered as one of the most important steps in data science. This is because good data yields great results, whereas noisy, unclean, missing and redundant data yields unsatisfactory results. Once raw data has been collected, it needs to be accessed and cleaned by various methods. Redundant rows or columns have to be deleted, missing data either needs to be filled or deleted, irrelevant columns have to be eliminated, and so on.
In this step, the data (which is usually in the form of row x column) is converted into a format that is required by the algorithm to process upon. For example- a text analysis task may require data in the form of text whereas a prediction or regression problem may require data in the form of a table, i.e. rows and columns. Based on the requirement and the end product, data has to be transformed into the respective formats.
The basics of statistics are considered to be a foundation while working as a data scientist. Understanding distributions, priors probabilities, posteriors probabilities, Bayesian theorem serve as a foundation while working with the data. The data needs to be interpreted and mangled with the help of statistics. It helps understand and solve problems by helping the data scientist extract meaningful and relevant insights/patterns from data.
It is important to stay up-to-date, know the new trends, packages, frameworks, new releases and changes that occur frequently if not on a daily basis. It is important to adapt and use whatever revolutionary technology comes our way and seems to be helpful in the specific scenario. It is essential to stay on top by knowing new algorithms, techniques, data mining algorithms, and so on.
It is important to keep learning, revising your career plan and update the skills that are necessary for the current world. Updating knowledge is vital to pursue future opportunities and make sure that your career path is aligned with your personal interests.
Salaries for Data Scientists are on the higher end of the spectrum, with a mean salary of about £60,000. With experience and constantly upgrading the skills, the salary can go up to £100,000 too. This also depends on the organization. If it is a start-up or a new company, they might not pay as much, but as and when the company grows, the pay-out increases. On similar lines, experience increases along with skills, which would make a data scientist more valuable to the organization.
Analytics has revealed that the number of data science-related jobs will see a surge and it has also been labelled as the 'sexiest job of the 21st century’. The demand is growing steadily. Almost every organization wishes to have a machine learning wing where data scientists would be much needed.
If it is one of the companies from FAANG – Facebook, Amazon, Apple, Netflix, Google, it is not really a requirement to have a bachelor's degree or anything. There might be certain positions that require specific qualifications, but entry-level positions don't usually have any specific requirement of a degree. These companies certainly expect the data scientists to be hands-on in one or two programming languages (object-oriented such as C++ or Java, and Python). They might also require knowing specific frameworks (TensorFlow, Keras), deep learning algorithms (Neural networks, convolutional neural networks, recurrent neural networks), NumPy, Pandas and so on.
Machine learning is a concept which data scientists will have to be familiar with. This doesn’t mean just the definitions. It involves understanding the algorithms, the mathematical working behind it, the kind of results it would provide, the kind of cases where certain algorithms can be used and how the output can be improved by tweaking certain parameters present in the algorithm.
It is also essential to understand where Machine Learning can be used, and how it plays an important role in understanding the data, and prediction as well.
It never harms to get a bachelor’s degree, a master’s degree and a PhD. All these degrees add formidable value to the knowledge already gained.
Any job requires a resume where the relevant skills are mentioned in the right way and format. It is important to present yourself in the right way, and also exhibit the enthusiasm of learning and being updated. An entry-level data scientist job will require the basics of object-oriented programming, Python, scientific computing packages, basics of machine learning, statistics, analytics and hands-on programming abilities. Taking up certain foundational courses, working hands-on in projects, internships and group projects also help in providing a considerable amount of experience around working with data.
It is also important to have a niche for data, be able to play around with data, extract patterns, have an eye out for insights, packages that could be used, the approach and so on.
Technology will create new jobs, but Data Science and Artificial Intelligence will be a major part of our life in the upcoming years. This means some jobs may be lost too (because many processes which seem too trivial would be automated) but think about the new jobs that would be created!
Instead of worrying about the jobs that would be lost due to AI replacing their work, it is essential to foresee and adapt to it. We have seen technologies revolutionizing the current world, and all this has happened because of ‘change’, because of how people have foreseen the circumstances and have adapted to it.
For example, AI will not replace a doctor. But AI can replace a doctor without AI knowledge.
We need to be up-to-date with the current trends, the technologies and the new and ever-changing requirements of the real world. The focus should be on learning to work with machines, not outwork them.
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