Anyone Can Become a Data Scientist-Myths & Facts

Data Science has always confused professionals! Is it okay to become a data scientist or not, remains an unresolved puzzle! But today we will bust all the myths and bring the facts to the table.

Transitioning a career into data science is a complex process and even scary sometimes. The reason for this is not the curriculum or the programming languages associated with it. In fact, online course on data science training institutes such as Learnbay provides very comprehensive guide and training courses. They make the entire career transition process as smooth as possible. The challenging part is to get over the false news regarding data science jobs and the future.

There are countless lies about data science that might discourage you from pursuing a career in it. Sometimes you might also feel that data science is meant for geniuses. Maybe Only people with excellence in mathematics and computer science can go for it. But that’s not right! Anyone can become a Data Scientist, want to know how? Keep reading!

Common Myths Busted

So let’s get started on the popular myths that people often hear regarding the job profile of

Data Scientists.

Myth: It is mandatory to have a Ph.D. degree to become a Data Scientist

Fact: One of the most commonly heard and known myths is regarding a Ph.D. degree for data science candidates. This is not true at all. Having a Ph.D. degree could be an added advantage for aspiring data scientists. It is nowhere compulsory to have a Ph.D. degree to become a successful data scientist. Having a relevant graduate degree and some Professional work experience is the basic requirement to get a place in the industry. Domain specialisation and a course certificate would be enough to be eligible for this job too.

Myth: Full-time Data Science degree is mandatory to become a Data Scientist

Fact: Not at all! Career transition in Data Science would have been a far-sighted dream for many if this was true. Many successful full-time professionals have given up their jobs to become Data scientists in their domains of expertise. They did not go for the undergraduate program in Data Science again. In fact, they took up short-time training courses in online or part-time mode. Previous domain knowledge and hands-on training on real-time industrial data together did wonders for them. They became Data Scientists without a full-time degree.

Myth: All previous work experience is helpful in becoming a Data Scientist

Fact: You might have worked successfully in another domain for five or 10 years. You might have all the domain knowledge and expertise. But all those previous experiences would not be transformed in the data science domain. For example, you might be a software engineer but as a data scientist, you would be provided with data and would be expected to perform analysis and predict results. You might feel inexperienced at this moment since you are a newcomer. Therefore, it is advisable to talk to higher authorities and people with prior experience. It is wrong to assume that your previous professional experience would completely be utilised in this new career.Another scenario is that you might remain stuck in the same domain. For example, if you belong to the healthcare industry, working as a healthcare data scientist might be more feasible and enjoyable. It is due to the previous knowledge about the domain.

Myth: It is compulsory to have a maths/computer background to become a Data  Scientist Fact: this is a very common assumption that most folks in data science have a programming or engineering background. They are aware of statistics and mathematics used in data science. This is not true at all. There are many successful non-technical background folks with inspiring stories of successful career transitions. You can read about Marios Michailidis who became a programmer on the number one platform Kaggle. However, the initial years of learning data science are struggling. Time, energy, and practice are invested immensely to become a pro in this field.

Myth: Learning all the Data Science Tools are enough to become a Data Scientist Fact: Actually it’s quite the opposite. Instead of gaining mastery over all the tools, an aspiring data scientist should concentrate on becoming an expert in one or two languages such as python or R. It is a common misconception that learning more tools and applying techniques in python is a step towards data science. Yes, it plays a central role in data science functions but it is not the only “need” to become a data scientist. The key is to be able to apply the right techniques for the right predictions. Making calculated decisions and applying logic are the primary components of data science. Possessing problem-solving skills, high-order thinking skills, and communication skills are the qualities that interviewers

look for.

Myth: Working with Top MNCs with high computational power is necessary to become a Data Scientist Fact: One of the data science branches called deep learning seems to be very machine intensive. It is assumed that it requires a considerable amount of hardware. It is true that a powerful hardware setup will produce efficient results for deep learning models but a supercomputer or a mainframe computer is not required to work with deep learning. The duration of preparing a model might become longer in this process. Google Collab is an excellent help for the data science community which provides free cloud services along with GPU support.

Myth: AI systems become advanced and evolve by themselves after training once Fact: Mostly inspired by imagination and fiction, AI systems are expected to possess human-like intelligence and work on their own in the future. The truth is that humans are not even close to the artificial general intelligence, AGI. So far, the models built cannot generalize to the other tasks. Progress is made every day in high-level research organizations regarding it. But achieving something like advanced neural networks creating their own neural networks is not realistic in the near future.

Myth: Limited data science job roles and unpredictable scope

Fact: Data science is not only about data scientists but it is a very complex and inclusive field. It requires individuals from different disciplines and a plethora of roles exist such as:

● Data analyst

● Data engineer

● Data science manager

● Decision scientist

● Researcher

● Software engineer

● Project manager

● Statistician

● Business analyst

● AI/ML engineer

● Data scientist

● Statistician

All these profiles play varying roles in a project. They all are somehow related to data science and the accomplishment of an Artificial Intelligence project. Therefore it is absolutely unjust to say that data science has very narrow applications and job positions.

Myth: Data science is all about building predictive models for business

Fact: The trait of predicting an event is incredible. This is an outstanding phenomenon in data science. However, there are multiple layers to this feature. Building a model is a small segment of the entire data science process. There are multiple steps involved such as understanding the problem, building hypotheses, collecting data, verification of data, data cleansing, exploratory analysis, building the model, verifying the model, deployment of model. The model building comes with all this added work. It is not merely about making predictions but also understanding how things will work in an optimum way for business.

Myth: collecting data is fun, the real focus should be on building model

Fact: Data is produced at an unprecedented rate but collection and cleansing are challenging parts. Until a Data Engineer collects relevant, accurate, and significant data, getting the final model would be next to impossible. The goal of a data science project should be the collection of quality data that allows analysis that leads to the formulation of convincing models for reliable predictions. There are multiple sources of data that engineers should be aware of. Getting data from each of these and applying it in a real-world setting is the greatest aspect of data science.

Myth: participation in data science competitions lead to real-life projects

Fact: Participating in competitions based on data science projects is an excellent stepping stone for career transition. This provides an opportunity to work on dataset skills along with a chance to win lucrative prizes. With the growing popularity of such hackathons and competitions, recruiters have started to pay less attention to this aspect of the portfolio. The competitions are entirely different from real-world projects with a guided pipeline. In a real- world scenario, more than half of the time is spent on collecting and cleansing the data. Therefore, it is an entirely false notion that competitions lead to real-world projects.

Anyone can become a Data Scientist-Facts!

It’s time to focus on the positive side of the data science domain. By now you have clearly got over the misconceptions around data science. Let us peep into the true facts that allow anyone to become a data scientist.

Knowledge/Education

It is wrong to believe that anyone can become a data scientist without proper knowledge and training. Around 88% of data scientists have a master’s degree and 46% have PhDs. Having a strong educational background with in-depth knowledge is a prerequisite. Bachelor's degrees in the fields of computer science, social science, physical science, mathematics, life sciences are mandatory. Having mathematics and statistics knowledge is an additional advantage. Aspirants without a master’s or Ph.D. degree go for special skill training in online mode before taking the career transition step.

Programming Skills

It is true that people without a programming background make a successful career in data science. However, having deep knowledge of analytics tools for data science such as R language is important. Around half of the data, scientist population uses this language to solve statistical problems. Besides R programming, the aspiring candidates should have knowledge about python coding, C++, Perl, Java, or any other language. Python is highly recommended in data science processes. The Hadoop platform is preferred for data science projects. Experience in Pig or Hive is preferred. These platforms aid in data exploration, filtration, sampling, and summarization.

Technical Skills

Data science is impossible without SQL database coding knowledge. Carrying out operations of addition, deletion, extraction from a database are critical for data processing. It helps in performing article functions and transforming the database structures.

Apache Spark is another popular Big data technology platform. Highly efficient in caching memory. Designed specifically to run complicated algorithms faster. Therefore, a data scientists can prevent the loss of data through this application. Hence it is highly i recommend learning it.

Machine Learning and Artificial Intelligence are the much-hyped branches of data science. The real application of data science skills is in this area. Very few professionals make it to advanced machine learning skills such as supervised and unsupervised learning. Having knowledge of algorithms and parameters helps in becoming a better data scientist.

Data visualization of vast amounts of data is a frequent need in the business world. The huge data is translated into simple comprehensible formats. Conversion into charts and graphs are much more understandable. A data scientist uses data visualization tools such as tableau, Matplottlib, ggplot, and similar others. Therefore, data scientists must know how to use these tools smoothly.

Conclusion

It is true that anyone can become a Data Scientist provided that they fulfill some basic eligibility requirements. Having knowledge and technical skills are the primary needs that one cannot move without. The non-technical skill that propels the data science career is intellectual curiosity, the ability to ask relevant questions and find their answers. Having a a business mindset with excellent communication skills are the signs of a strong data scientist. People with the ability to integrate the ideas of other team members into the project are key players in the success of data science projects.

We hope this piece of information enlightened you about the possibilities of becoming a data scientist. Our only advice is to start learning from scratch. Instead of worrying about what you don’t know, focus on what you already know. Work towards it. Concentrate more on real-life projects rather than theoretical learning. We encourage you to move into Data Science without fears as it is the Sexiest Job in the 21st Century as per the Harvard Business Review, 2012.