Data Science exists everywhere, to be honest, every exchange and interaction on any technological domain includes a certain set of data, be it Amazon purchases, Facebook/Instagram feed,Paytm, Netflix suggestions or even finger and facial recognition facility provided by phones.
Today in 2021, most companies are adopting a data science strategy to make more revenue by automating different scenarios and replacing dozens of IT people with a single data scientist who can automate the task of those IT people using various automating tools like BluePrism, UI Path, Python and machine learning algorithms.
Amazon is a key example of how data influences all our lives and shoppers particularly. Its data sets store every buyer’s data; what you have bought, the amount paid and your search history is all remembered in Amazon’s system by virtue of data collection.
This greatly enables Amazon to personalize and customize its homepage according to your preferences and shopping history.Data Science encompasses many breakthrough tech concepts like Artificial Intelligence, Internet of Things, Deep Learning to name a few. With its progress and technological developments, data science’s impact has increased drastically.
We are constantly being faced with unpredictable situations — like the Covid pandemic — which has called for businesses to do what they can to minimize human-to-human contact. Data science and rapidly changing technology have helped drive these changes and prove that a bright future exists. This will, however, depend on the quality and the extent of data that organizations can acquire.
IDEAL DATA SCIENTIST
“If you look at the next five years, AI is coming up in a big way across multiple industries. An ideal candidate should know the algorithms, mathematics, code and technical skills. Surely, these are a must. But, in addition to this, candidates should work on their problem-solving skills, develop innovative solutions, and out-of-the-box thinking,” said Sambasivam
RAY OF HOPE FOR DATA SCIENTIST
Will, there be a shortage of jobs or will there be fewer hiring?
Well, things become easier when we think differently.It is true that companies will keep focusing on the automated workflow of machine learning.
But, remember, no company wants to depend on another company for their work. Each company aims to build their product so that instead of depending on others, they can build their automated system and then sell them in the market to earn more revenue.
So, yes, there will be a need for data scientists who can help industries build automation systems
that can automate the task of machine learning and deep learning.
Why Data Science Will Continue To Be the Most Desirable Job ?
Data science talent shortage
The demand for skilled professionals in the field of data science has grown remarkably owing to the urgent need for strategic decision-making tailored for specific regions.
Data has become the backbone of business decision making.
Data science has proven to be a powerful tool to extract meaningful insights from this large chunk of data. These insights help organizations in determining any prominent changes that are to be made basis the changing consumer behavior, shortcomings of previous solutions, forthcoming challenges and competition analysis.
Shortage of Talented Pool:
While the demand for professionals adept in data science skills is at an all-time high, there is a major demand-supply gap due to the non-availability of skilled talent.
Highly lucrative career
According to Michael Page’s 2021 India Talent Trends report, professionals with 3-10 years of experience receive an annual salary ranging between INR 25-65 lakh and those with more experience can command packages upwards of INR 1 crore.
A large selection of roles within the field
One may choose to opt for a job role based on their interest as well as experience level. Some of the job roles that are high in demand include data scientist, data architect, BI engineer, business analyst, data engineer, database administrator, data, and analytics manager.
Is there really a shortage of data scientists?
Despite an large numbers of people or things arriving suddenlyof junior level candidates, high pay data science skills are still in shortage. The highest-paid Data Scientists have highly specialized skills that set them apart from others in their field. These roles are in high demand but cannot be filled by undergraduates with no experience.
Impact of data sceince on 5G Technology
Low latency and its high speed will immensely benefit data analytics. These features make it possible for analysts to collect, clean, and analyse large volumes of data quickly. This will spur new analytics technologies soon. For example, autonomous cars – earlier autonomous car production was limited and a pipedream because data analytics was restricted by the high latency offered by 2G, 3G, and even 4G. But now, 5G offers low latency, better information processing, and does it in real-time.
One of the most significant opportunities 5G offers analytics is real-time data exchange or insights.
AI AND DATA SCIENCE
On one hand, Data science centers around data representation and a superior show, while AI zeros in additional on the taking in calculations and gaining from ongoing information and experience.
Continuously recollect – data is the primary concentration for data science and learning is the fundamental concentration for AI and that is the place where the distinction lies.
To see the value in this distinction more, let us take a utilization case and perceive how the two data science and AI can be utilized to accomplish the outcomes we need –
Allow us to say you need to buy a telephone on xyz.com. This is whenever you first are visiting xyz.com and you are perusing telephones, all things considered. You utilize different channels to limit your inclinations and out of the outcomes you get, you pick 4-5 of the telephones and think about those. When you select a telephone model, you will see a proposal beneath the item – for a comparable item in a lesser cost or with more elements, or related adornments for the telephone you have picked, etc. How does the site suggest you these things? It has no set of experiences about you!
That is through the data from a large number of other who might have attempted to buy a similar telephone, and looked/purchased different adornments along. This makes the framework naturally prescribe something similar to you.
The whole course of assortment of information from the clients, clearing and sifting through the necessary data for assessment, assessment of the separated information for building designs, tracking down comparative patterns and building a model for a proposal of exactly the same thing to different clients lastly the streamlining – is information science.
Where is AI in this? Indeed, how would you construct a model? Through AI calculations. In view of the information gathered and drifts produced, the machine comprehends that these are the extras that are typically purchased by different clients with a specific telephone. Henceforth, it recommends you exactly the same thing dependent on what it has ‘encountered’ previously.
Skills for data scientist
There is a massive shortage of skilled data scientists! Yes, that’s right! Even though the jobs in the field of data science are seeing growth, there remains a scarcity of data scientists with the right skills.
Fundamentals of Data Science.
The first and foremost important skill you require is to understand the fundamentals of data science, machine learning, and artificial intelligence as a whole.
Statistics and Probability
statistics is an essential concept before you can produce high-quality models. Machine Learning starts out as statistics and then advances. Even the concept of linear regression is an age-old statistical analysis concept.
The knowledge of the concept of descriptive statistics like mean, median, mode, variance, the standard deviation is a must. Then come the various probability distributions, sample and population, CLT, skewness and kurtosis, inferential statistics – hypothesis testing, confidence intervals, and so on.
Statistics is a MUST concept to become a data scientist.
Analytics and Modeling
Data is only as good as the people performing the analytics and modeling on it, so a skilled Data Scientist is expected to have high proficiency in this area. Based on a foundation of both critical thinking and communication, a Data Scientist should be able to analyze data, run tests, and create models to gather new insights and predict possible outcomes.
Data visualization is an art.Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.
Deep learning is the most hyped branch of machine learning that uses complex algorithms of deep neural networks that are inspired by the way the human brain works. DL models can draw accurate results from large volumes of input data without being told which data characteristics to look atIn a nutshell, data science represents the entire process of finding meaning in data. Machine learning algorithms are often used to assist in this search because they are capable of learning from data. Deep learning is a sub-field of machine learning but has improved capabilities.
Data wrangling is the process of cleaning and unifying messy and complex data sets for easy access and analysis.
At the heart of the data science role is a deep curiosity to solve problems and find solutions — especially ones that require some out of the box thinking. Data on its own doesn’t mean a whole lot, so a great Data Scientist is fueled by a desire to understand more about what the data is telling them, and how that information can be used on a broader scale.
Learning never stops in this field. You master the tool one day and it gets run over by an advanced tool the next day. A data scientist needs to be curious and always learning.
We have seen how there will be a lack of data science jobs in the next five years because companies will be adopting the automated pipelines of data science. But, there will also be high demands for data scientists who can automate data science pipelines.
As per my thought to automate those pipelines, we first need to understand machine learning algorithms to build a better-automated system, which will eventually lead to more jobs.