Data Scientist Career in the 21st Century Made Simple: What You...

Data Scientist Career in the 21st Century Made Simple: What You Need to Know

Posted 2022-08-18 08:13:19

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Summary

A data scientist's job is to examine data and interpret the findings in order to put them into practice for the organisation's gain

Careers in data science are in great demand, and this trend is unlikely to change anytime soon

It's not without reason that careers in data science have recently attracted a lot of attention. Data science has progressed from being limited to merely analytics and statistics to generating forecasts, making judgments, and taking actions that affect businesses big and small around the world. Data scientists possibly have the widest range of job titles and duties out of all the professions in the computer industry.

Utilising and arranging data for analysis is data science. The essence of data science is cleansing and arranging the data to help us make better decisions in the future, particularly when there is a wealth of data accessible.

Roles & Responsibilities of a Data Scientist

A data scientist will use mathematical, statistical, and computer science expertise to analyse data. Their multidisciplinary approach helps them to make sense of large repositories of data and make forecasts about and address problems that may crop up.

Here are 3 major roles and responsibilities of a data scientist:

Collaboration: Data scientists will often work in complex teams to inform key stakeholders about challenges and discoveries in an effort to improve company performance and decision-making.

Analytics: A Data Scientist is often responsible for providing insights on a variety of issues, such as projections, classification, clustering, pattern analysis, sampling, simulations, and other issues. Having the ability to analyse the data at their disposal to create economic and statistical models is a given.

Strategy: A Data Scientist is also essential in the development of creative strategies for understanding consumer trends and management in the organisation, as well as approaches to address complex business challenges.

Essential Skills Required To Be A Data Scientist

Understanding the skills that might make you a strong candidate is crucial since data science is a field that is continuously evolving and has applications across a variety of sectors.

Here are the top 5 essential skills required to be a good data scientist:

Mathematical Skills: A data scientist has to be very good when it comes to mathematics, particularly branches like algorithms, logarithms, calculus, and statistics.

Programming Skills: To benefit from computer-intensive technology like machine learning, data scientists must interact with machines. They will need programming skills for this.

Methodological Thinking: A data scientist frequently has to consider issues from several angles. Thinking with discipline makes it easier to stay on track, use one's time wisely, and maintain focus.

Curiosity: Although one might choose to follow the machine learning lifecycle, developing a curious mindset can enable a data scientist to continuously produce successful and innovative outcomes.

Data Intuition: A skilled data scientist has intuition and understands when to go beneath the surface for insightful information since useful data insights are not always visible in massive data sets.

Roadmap To Becoming a Data Scientist

A data scientist's job is to examine data and interpret the findings in order to put them into practice for the organisation's gain. If you think you have a knack for examining data and making profits for an organisation then you should definitely pursue a career in data science.

Here is a classic roadmap to becoming a data scientist:

Having a science background in high school.
Getting a data science bachelor's degree or equivalent qualification.
Completing a certified Data Science course.
Gaining relevant work Experience
Building a portfolio
Pursuing a master’s or doctoral degree in Data Science to gain further knowledge

Career Opportunities for a Data Scientist

In most businesses, data science has important applications. A profession in data science is a wise choice since, in addition to being a booming sector and profitable, data may very well be the axis around which the whole economy spins.

Let’s take a look at some of the career opportunities available to a data scientist:

Machine Learning Engineer

In order to give software solutions, machine learning engineers build data funnels. As well as having a working grasp of software engineering, they often require good statistical and programming abilities.

The design of an application's architecture, which includes elements like the user interface and infrastructure, is a primary concern for application architects.

Data Engineers are responsible for building and maintaining analytics solutions that create a strong link among big data within a company as well as making that information available to data scientists.

Data analysts support organisational leaders' decision-making by creating reports that clearly express patterns and learnings from their study. They can be working across sectors and departments.

Data is gathered, analysed, and interpreted by statisticians in order to find patterns and connections that might guide corporate decision-making. Hence, statisticians could find a place in any big corporate.

Nearly every industry, from government security to education applications and everything in between, requires data science specialists. Big data is essential to the success and improved customer service of millions of enterprises and government agencies - and data scientists are also the ones who facilitate proper handling of it. In fact, careers in data science are in great demand, and this trend is unlikely to change anytime soon.

Educating The Next Generation Of Data Scientists

Dr. Lee Harland, founder and Chief Scientific Officer, SciBite (an Elsevier company).

In recent years, artificial intelligence has become mainstream. Today, more enterprises in more industries are looking to technology like AI for a competitive edge and to help them manage the overwhelming volumes of data they both generate and collect. A recent study by IBM put the “global AI adoption rate” at 35% and found that 44% of global organizations are currently working on embedding AI into their operations.

But despite this continued uptick in AI adoption, one element sure to put the brakes on many initiatives is the global skills shortage. Successful AI requires competent data scientists. And companies are scrambling to hire people with the requisite skills in a very shallow talent pool. As technology continues to advance and new innovations emerge—from neural networks to quantum computing—the problem is only going to worsen. To solve it, a new approach to educating the next generation of data scientists is needed.

First and foremost, established curricula must develop in tandem with technology to equip students with a full spectrum of skills around AI. And secondly, businesses must transform their thinking around hiring data science candidates.

Practical Magic

Current curricula—from early years to tertiary education—are not designed to produce individuals with the kinds of data skills that businesses need both now and in the future. The focus in many curricula is on teaching students the “rules” and logic of coding and writing an algorithm. This produces people skilled at building a data model and who understand the ins and outs of coding but often at the expense of practical applications of AI that focus less on theory and more on utilization. The often-used analogy is that most of us don’t understand how a car works, but many of us use them to achieve our daily goals.

Moreover, technology is advancing all the time, which means the skills needed will change. Educators are unlikely to ever be able to modify curricula quickly enough to reflect trends. This does not mean we should not identify ways to build emerging technologies into learning. One way to achieve this is to greatly expand initiatives to engage technology businesses to bring in employees to explain new developments and practical applications to students.

As technology evolves, the question of how much expertise we all will ultimately need in data science is also worth considering. AI solutions exist that are capable of automatically executing analyses and are self-learning—the essential component is having a deep understanding of the data, including critical appraisal of quality and bias. While the theoretical and mathematical aspects of data science are crucial, the ability to understand business problems, prepare and critique data and interpret results in the context of other data are all important to a well-rounded data science team. As such, our approach to educating data scientists should look to provide basic training or resources for each of these areas.

New Routes To Entry

It might be assumed that the digital skills shortage will get better with time as a younger generation enters the workforce. But this isn’t necessarily true. Global research conducted by tech firm Salesforce found under a third (31%) of Gen-Z respondents, those considered the first true digital natives, feel “very equipped” for a digital-first job. And of those Gen-Zers, only a fifth (20%) think they have “advanced” digital skills in coding and just 7% in AI. Of all respondents, 64% classed themselves as “beginners” in AI skills.

In parallel with better data science education, widening the field of prospective entrants is critical to addressing skill shortages. Businesses should consider candidates who demonstrate an aptitude for data but come from a non-technical educational background. When part of a wider team, different perspectives can shed new approaches to analyzing or interpreting data. For instance, philosophical thinking and questioning are becoming more important as concerns around AI ethics and equity are raised, so businesses should look at candidates from philosophy and sociology backgrounds when recruiting data scientists. Technology also has a diversity problem. Non-white, female and working-class people are vastly underrepresented and often excluded from technology careers. Finding ways to broaden access to education and training for these groups not only increases broader social mobility but helps to address the acute skills shortage facing businesses, as well as bring a wider range of perspectives to the table.

Initiatives such as scholarships and degree apprenticeships (where students are paid a standard salary but obtain a degree after three to five years) are ways to increase the numbers entering the field. There was recent encouraging news on this front in the U.K., with the government announcing 2,000 new scholarships for masters AI conversion courses (part-funded by industry) to develop new data science and AI experts regardless of underlying experience. Longer term, higher education institutions should consider that many non-STEM subjects may still be “data-rich” and ensure broader data science awareness across the student population, which in turn will produce data-savvy graduates.

A Vocation For All

Educators, businesses and governments must work together to establish new routes to entry, develop flexible and broad curricula and make data science a valid vocation for all. Charities like The Roysia Foundation, of which I am a trustee and chair, also have a role to play. It currently partners with several U.K. universities to widen access to STEM education through bursaries and scholarships to those from underrepresented social groups that may not otherwise enter the system.

Businesses must also “sell” AI to students and candidates to close the gap. People want to engage in work that has meaning, and for industries that can’t match the pay of sectors like finance and technology, showing how AI is contributing to solving the world’s challenges is key. From tackling climate change to discovering new antibiotics, these skills will be essential. By taking a new approach to education and hiring, businesses can tackle the shortage of data scientists and fill the talent pipeline.

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

Executive Q&A: New Survey Reinforces the Importance of Data Science and AI/ML

The results of a new study by Domino Data Lab confirm the importance of data science and advanced analytics to modern enterprises. The company’s head of data science strategy and evangelism, Kjell Carlsson, drills down on the survey results.

By Upside Staff

August 8, 2022

Upside: A recent survey conducted by Domino Data Lab found that nearly four in five respondents agreed that data science, ML, and AI are critical to the overall future growth of their company. In fact, 36 percent said these were the single most critical factors. That’s a pretty strong statement. What’s driving such belief in these technologies?

Kjell Carlsson: It’s worth noting that these numbers are similar to the surveys I’ve run in the past. I did a survey at Forrester in 2021, where 25 percent said data science was the single most important factor for their competitiveness and expected that to rise to 51 percent in the next two years. In two surveys I did that year, 21–25 percent said data science/ML/AI was their largest investment area, rising to 49–54 percent in two years.

Arguably the reason why there are such expectations around data science is that more and more companies are seeing the results of their data science initiatives. In large part, this is because companies are becoming more mature in their use of these technologies and their data science teams are becoming more established, integrated with, and supported by the rest of the organization. In the surveys I’ve run, companies regularly report a 4–5x ROI from these investments. However, there is a significant (30 percent) gap between leaders and laggards and that divide is growing.

Management’s expectations for bottom-line benefits are also growing rapidly. Nearly half of respondents to your survey said their company’s leadership expects data science efforts to produce double-digit revenue growth -- which is up from just 25 percent in a similar survey you conducted last year. What accounts for this big leap in expectations?

Again, this is (happily) due to companies seeing results from existing projects. There has been a leap forward not just on the tools and technology side but, more important, on the people and process side. Organizations have been able to bridge the chasm between developing data science solutions and deploying them, which has required better integrated MLOps platforms as well as alignment between data science, data engineering, operations, and line of business leaders. The problem companies are running into is being able to scale these successes. Many teams are finding themselves victims of their own success in that they are now spending more time maintaining existing models and projects and struggling to take on new ones.

What challenges are enterprises facing when it comes to data science according to your survey?

When it comes to data science, there are several challenges that enterprises face.

The majority of respondents found that the most challenging technological issues related to scaling and operationalizing data science were accessing appropriate data science methods and tools (27 percent of respondents rated this their most significant challenge) and security considerations (26 percent of respondents rated this as their most significant challenge).

When it comes to people- and process-related challenges, most respondents ranked having enough data science talent (26 percent) as the most significant challenge. It’s no exaggeration that every fast-growing organization needs more data scientists -- they play a critical role in turning raw data into innovative new products and services.

However, it’s also important to understand that the notion of a “normal” data scientist is a myth. Today’s data scientists come from a wide array of backgrounds, ranging from computer science to applied physics, so when looking to hire and recruit data science talent, organizations should be ready to cast a wide net.

How to Prepare for Data Science Job Interviews

The Data Science field has exceeded all estimates of career opportunities made in the last five years and outperformed with more than 2 times of the projected numbers. The Data Science career unarguably stood as the most desired job.

Also it is the fact that there are millions of Data Science jobs left unfilled every year due to the lack of qualified Data Science professionals. In the year 2021, there were close to 3.2 million of Data Science jobs left unfilled worldwide, of which around 2.0 million jobs are from Asia Pacific region.

On the other hand, many aspirants who pursued a Data Science course, gained strong foundation knowledge, did many learning projects, are finding it difficult to get a job in the Data Science field.

So the question: Why are many Data Science aspirants with good preparation finding it difficult to get their first job in the field of Data Science?

And the answer: Structured interview preparation. In this article we shall discuss “How to prepare for Data Science Job Interviews” in a structured manner.

4-Phase Data Science Job Interview Preparation

APTRON Noida, a leading institute for Data Science training, have trained more than 25,000+ Data Science aspiring professionals and enabled thousands of them to transition to Data Science careers.

With this deep experience over 6 years into Data Science training, APTRON Noida formulated a 4-Phase approach for preparing Data Science aspirants to get job ready.

Technical Readiness
Resume Preparation
Interview Skills
Job Application Strategy

1. Technical Readiness

A strong knowledge of Data Science concepts and practical experience in applying these concepts is one of the most important aspects of getting a Data Science job ready. Data Science is a vast subject and mastering all the Data Science concepts is difficult.

Technical readiness indicates that the candidate should be able to demonstrate key concepts which are relevant to the practical application of Data Science concepts to real-world applications.

The topics of the Data Science that is considered as essentials are as below

Programming - Python for Data Science
Statistics and Mathematics
Data Preparation
Feature Engineering
Machine Learning
Data Science Model Deployment
Projects & Internship

A strong foundation in key concepts of above topics and applied knowledge in solving data problems and machine learning modelling suffice the essential technical readiness for Data Science and Machine Learning Job interviews.

Resume Preparation

The resume is the first touch point with your potential employer. As per the research, most of the interviewers/initial resume scanners make-up their minds in less than a minute on glancing through the resume. So it is very important to present your resume not only in an impactful manner but also in a clean, simple and professional manner.

Here are the few areas to strengthen your resume:

First is First - Get through ATS

In most cases, Applicant Tracking System (ATS) has a built-in system to filter relevant resumes. These ATS filter systems work with simple keyword matching and density mechanisms. This means that ATS gives a high score for the resume which mentions the same keywords as in the job description(KD) key skills requirement section.

First is first, the resume has to get past the ATS, so it is important to fine tune the resume inline with the job description for every job application.

Prioritise Relevant and Important

It is a common practice to write the experience in the resume in chronological order. But reviewers would be more interested in the skills and experience that are relevant to the job description, so that they can evaluate the resume for the job.

It is recommended that the relevant skills and experience for the job be placed at the time and highlighted. The rest of the information can be presented below in chronological order.

Remove Not Relevant Information

It is equally important to either remove or minimise the not relevant information. It would be a distraction to mention your achievements, experiences and skills that are not relevant to the current job you are applying for..

Highlight Technical Skills

Always use a separate section- “Technical Skill”, to highlight the technical skills that are relevant to the job as mentioned in the job description.

Mention Participation in Relevant Events

It is a good practice to mentioned the participation in relevant events such as webinars, tech meets, conferences, etc., Only mention the events that are relevant to the job

Interview Skills

The data science interviews are usually on relevant skills and practical experiences, it is also important to have good Interview skills including sitting posture, eye contact, concise answers, professional mannerism, display positive attitude, being a good listener etc. There are a plethora of articles available to improve your interview skills but the key is to practise.

Mock interviews with constructive feedback are valuable to improve interview skills.

Job Application Strategy

Finally, you should have a job application strategy. It is common that you need to attend multiple job interviews before starting to get successes. Before you venture into applying for jobs, you should have a clear understanding of your career ambitions, your skills and other considerations. Here are the few steps to start with your job application strategy.

Identify the right role
Fine Tune Resume for each Job
Channels: Job Portals, Company Pages, etc.,
Follow up on Applications
Network for References

Which is the best Data Science Training Institute in Noida?

APTRON Noida provides quality training for Data Science Course in Noida. Trainer had a good knowledge of Data Science which includes python, Deep learning, Machine Learning & AI. APTRON Noida is one of the places for learning Data Science Course in Noida. It provides a friendly environment with a quality trainer. Take Online Data Science Training at APTRON Noida. Trainer provides vast knowledge on individual topics with real-time examples. APTRON Noida provides training on software courses. It also provides a Data Science course in Noida with a quality Training & Friendly environment to learn. Enroll with APTRON Noida for Data Science Training in Noida after visiting multiple institutes. APTRON Noida is the only institute which provides quality training at an affordable price. The Online Data Science Course at APTRON Noida is one of the best places. They provide quality training at an affordable cost. APTRON Noida helps you to start a career in Data Science.

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