Ever since Harvard Business Review has named data scientist jobs, “The Sexist Job Profiles of the 21st century”, the world has turned upside down. There is a sudden spike for data scientists in the market.
And there are less skilled people around. The professionals who fail to update themselves overtime are spreading stereotypes about data science that are not always true.
It is better to neglect them and focus on what and how well you can evolve your lucrative career in data science.
Why Are There So Many Stereotypes About Data Science?
Being one of the top trending technologies worldwide, and there are many great futures in data science. It is continuously evolving and will go down as one of the finest technologies of all time.
Data Science needs a systematic approach, planned strategies, and the right execution to succeed. That is one reason why many fail, and they think data science is sturdy. And it is almost impossible to match it.
Read more about: Top Big Data Companies in USA
The upside is there are more and more new job opportunities open every day as it is emerging. And the downside is that certified professionals fail to skill-up themselves and hone various skills.
They feel tough to sustain themselves in the corporate world, and technologies like data science get all the blame. There are so many stereotypes in the market, and airs from one ear to another with all rumors.
How Data Science Stereotypes Are Impacting When Someone is Building a Career in it?
For someone who is building a career in data science, stereotypes always add negative values to what they think about the future.
And it is for two reasons. Firstly, they are new in the field, and secondly, they do not have unfathomed domain knowledge, and they go to the downside without realizing it.
But data science has the potential to leverage your career from beginner to expert as you gain experience. For building an outstanding vocation in data science, focus on how you can develop skills.
And help the business to solve problems, despite emphasizing various stereotypes out in the market.
Many companies hire data scientists without proper or suitable infrastructure and demand to extract the right information.
And there lies the cold start problem and unhappy relationships between companies and employees.
And when they get asked for ML and AI coding, they do not have much experience as a beginner. They fail to provide the real values to the corporate, that they get hired. Too many expectations and workload and less skilled make professionals quit their jobs.
What Does The Future Say About Data Science? And How Should You Neglect all Stereotypes?
Data Science has a top-notch future ahead. And according to the founder of the world wide web, Tim Berners Lee – “Data is a precious thing and will last longer than the systems themselves.”
From this statement, all you can predict is data science proliferation is never going to end.
Instead of opening new doors and opportunities in fields like ML, AI, and big data.
Different sectors use data differently, but they use it for common goals – like providing better solutions to the customers, creating effective strategies, and maximizing profits; data science has broad areas of applications everywhere.
Just like there are so many positive sides to data science over the internet, there are so many myths overspreading and spoiling data science.
And its reputation in the list of trending technologies that you should ignore is by focusing on building your career and upskilling yourself with the recent trends related to data science and data-driven technologies.
Six Stereotypes About Data Science That You Should Never Believe to Stay Away from Self-Doubting.
There are mainly three types:
Career Related Myths:
A Full-time Data Science Degree Will Help You in Transition
Many data scientists, generally who are at the start of their vocations, think that it requires a full-time data science degree to transition from one position to another.
And many newcomers are already spending huge on this to get their data science degree certification, which is a myth.
The truth is practical experience will get you there, working on various projects and honing the required skills by solving the problems to various real-time business problems.
You Should Belong To Computer Science, Having Mathematics and Programming To Elevate Your Career.
Again many people have a common misconception that to become a data scientist. You should belong to computer science and be good at programming languages (Python and R). Along with computer science, you should be good at mathematics, statistics, and probability.
Does it mean that coming from this background will get you into data science? The answer is no.
You can learn from scratch. It might be frustrating sometimes as your technical colleagues will remain ahead in every turn. But with the right way of mindset, dedication, and prudence you can overcome them.
Tools and Framework Related Myths
Learning A Single Tool Is Enough To Become a Data Scientist
There is always a debate between Python and R, which one to learn to excel in a data science career? Many even consider mastering one of either can help you build a lucrative career in data science.
Of course, Python has many advantages (NumPy, Sci-Py, Scikit-learn, Pandas). But knowing how to use other tools and software like Tableau is also crucial. And to understand different patterns of data visually.
Therefore, using multiple tools and mastering various skills like problem-solving, communicating, and better decision making can boost your data science skills in the least time.
Once You Build Your Model, It Will Continue To Evolve And Generalized In The Future
Data science is not a movie or a song, or a web series to generate revenue for a lifetime after its release.
But a set of algorithms to get regular updates; that can look for hidden opportunities and help corporates in smart decision making.
So once you build a model, you need to keep on testing and upgrading to cope in the market, else you lag in the market for competing with the competitors.
Data Science Job Roles Related Myths
Data Science Is All About Creating Predictive Models
Predicting the future from past data is a powerful thing about data science. Building models using KNN Classifiers, logistic regression, and time series analysis will help you to predict what the customers are likely to make a purchase is the most crucial part of any business.
There are always multiple layers in data science projects, and nothing is straightforward here.
If you have come across the market basket analysis, you can easily predict the clustering techniques. And association rules to understand the buyers’ journey and their intent.
Participating In The Data Science Competition Transcends To Real-life Projects
Although data science projects are a stepping stone to boost your confidence and handle multiple things simultaneously, it also opens new doors and opportunities to carry out your vocation to the next level.
No matter what your project is, there is a big difference between real-life projects and competition projects.
Real-life projects need up-gradation with time, but competitive projects may win you grand prizes, but never a good fit for the real-life undertaking.
It is one reason why data science competitions are clean, but maximum times, either repeated or on the same concepts.
And recruiters pay less attention to it as they are more interested in life-changing or life-impacting projects.
Real-life projects are end-to-end pipelines, and you need a team to work on them. And 70 to 80% of your time will get spent on collecting data, where most of your time will get consumed by data cleansing and data engineering, and other processes.
Data science is a great choice to kick-start your career and also into it. It carries ample opportunities for budding and experienced professionals in the data-driven industry, regardless of so many myths about it.
There is so much competition in the market for a specific post, and all you need is to become the best in the market and hone your data science skills to get the top-notch jobs as hands-on with the best packages in the industry.
Senior Data Scientist and Alumnus of IIM- C (Indian Institute of Management – Kolkata) with over 25 years of professional experience Specialized in Data Science, Artificial Intelligence, and Machine Learning.
ITIL Expert certified APMG, PEOPLECERT and EXIN Accredited Trainer for all modules of ITIL till Expert Trained over 3000+ professionals across the globe Currently authoring a book on ITIL “ITIL MADE EASY”.
Conducted myriad Project management and ITIL Process consulting engagements in various organizations. Performed maturity assessment, gap analysis and Project management process definition and end to end implementation of Project management best practices