Loading...
You are here:  Home  >  Data Education  >  Big Data News, Articles, & Education  >  Big Data Articles  >  Current Article

How can Experienced Programmers be Trained to Become Data Scientists?

By   /  December 6, 2016  /  No Comments

pg_dsprgms_120616In the Data Science industry, the year 2018 will be the one to watch in US as the business community faces a severe demand-supply gap in Data Science jobs. McKinsey Global has projected fresh Data Science job openings of up to 180,000 in US alone. This forecast may very well reflect the global need for Data Scientists in that year.

The salary reviewer Glassdoor states that the average salary that a Data Scientist fetches is an impressive $116,000. Moreover, the global Big Data market is expanding at breakneck speed as more and more businesses are going through a widespread Big Data adoption, further contributing to the gap in demand and supply within the Data Science community. Thus, it is evident that highly skilled Data Scientists are in urgent demand, and yet the average workplace is so short of experienced employees.

 The Forbes blog post titled Why Data Scientist Is the Best Job to Pursue in 2016 probably echoes the above sentiments aptly. A natural approach to tackle this talent gap in the Data Science field is to train up the existing breed of senior programmers to fill the void. In many businesses, the business owners and operators are seriously considering training up the highly qualified and experienced programmers to fit into the newer Data Scientist roles. The easiest way to do this is to filter out the interested and experienced programmers and counsel them on available retraining choices for stepping into Data Science.

According to What is a Data Scientist and should we instead be talking about data teams? Data Science, in many cases, has become synonymous with Data Analysis. This is a rather unfortunate situation for business as the so called analysts simply extract data from huge datasets without paying any attention to the extent of data corruption in those data sets. The modern breed of Data Scientists have to be appropriately trained to recognize and extract the right “data” for answering their questions, so that the answers are completely free of preconceived notions or biases.

Data Science Training for Programmers Approach 1: Going Back to School for Advanced Degrees in Data Science

In 9 Must Have Skills for a Data Scientist, Burtch Works points out the most important programmers need to have to be considered for Data Science positions. The significant observation here is that most Data Scientists usually have a Master’s or PhD, as an advanced degree prepares the candidate for a research-intensive job. The other popular academic degrees among data professionals are mathematics, computer science, statistics, and computer engineering.

Some working professionals prefer advanced academic degrees as they feel that an MS or PhD in Data Science or Computer Science may offer a strong theoretical foundation required for complex business projects. As a strong background in computer science or Data Science is more essential than that of mathematics or computer engineering, the candidates going back to school should only opt for the best campus programs offered. Going back to school is not easy, as it requires full-time commitment in time, availability of funding, and availability of savings to pull through family expenses during the academic coursework. Thus, normally working programmers or junior Data Analysts may be intimidated by the thought of making such a serious commitment for their future.

So, HR Departments, in conjunction with management, may have to go through some rigorous candidate review process to find existing staff for possible academic training for Data Scientists. If the candidates show strong promise, employers may even think of partially or fully sponsoring their degree programs as a means to invest in future.

Data Science Training for Programmers Approach 2: Sending Senior Programmers to Boot Camps

What are boot camps? Boot camps, for those who are unfamiliar with this concept, are basically brief, drill-intensive training programs to get working professionals up to speed in Data Science technologies. In other words, boot camps are ideal for programmers who have experience coding but need to fill knowledge gaps in Data Science in a short time.

Generally, most boot camps offer a three-month intensive program, which combines Data Science and programming classes, real-world projects, field work, seminars, networking for job interviews, and mentoring by business leaders. The best part about the boot camps is the direct exposure to industry think tanks, which is unavailable in other training modes.

The traditional boot camps like NYC Data Science Academy or Metis target existing programmers with expertise in at least one programming language. Many mid-level programmers or software engineers may fall in this category. The boot camps can train these people quickly as they already have a background in mathematics, statistics, and databases.

In recent years, the newer Data Science boot camps offer training designs to accommodate different types of skill levels. For instance, Microsoft’s DS3 summer school is aimed at college students aspiring to be Data Scientists. The specialized boot camps such as Insight Health Data Science: Fellows Program target MD or PhD students for Big Data jobs in the healthcare sector.

Data Science boot camps have always been in physical locations, though the virtual lab concept is becoming popular now.  The in-person boot camps have many advantages like meeting industry leaders, not attainable in virtual boot camps. Master in Data Science offers a comprehensive list of available Data Science boot camps in Mega List of Data Science Boot camps.

In Data Science boot camps, readers of this article will find the transcripts of interviews conducted with three candidates out of Data Science boot camps who share their experience and earned benefits.

Data Science Training for Programmers Approach 3: Advanced Certifications in Specific Subjects

This training approach may be highly suitable for those programmers who are already skilled on their job but need brush-up skills on specific Data Science courses like data visualization or Data Governance or on specific programming courses like R or Python. In such a case, the quickest and most economical training method would be to select any of the certification programs available on those subjects.

These days, online certifications have become very popular as most classroom instruction can be imparted, recorded, and downloaded through video technology and tests can be conducted online. Most working professionals prefer the online certifications as they can be taken anywhere, anytime without hampering their regular work schedule. The certifications, whether online or in-person, offer many advantages over traditional learning approaches. Coursera or Udemy offers such subject-specific certifications in Data Science.

Data Science Training for Programmers Approach 4: In-house Learning Academy Setup by Employer

Many reputed organizations like SAS Institute have set up their own training academy to train Data Scientists in house. The most notable feature of this type of training is that student Data Scientists get a chance to experience the proof of “concepts” learned in the academy through real-life projects at work.

SAS Academy for Data Science prepares the working professionals for a Data Science career. The SAS Programmers and Statisticians have a lifetime opportunity to join SAS Academy whenever they want to make a career switch into Data Science. Catherine Truxillo, Head of Development at SAS Academy for Data Science, feels that the best way to spot a future Data Scientist is to see who asks the most number of “whys?” The candidates in her programs come from a variety of academic backgrounds but the most successful practitioners in Data Science would be the ones who are always curious. The 12-week course in Data Science is meant for a career transition for mid-level professionals. This course offers the combined benefits of a boot camp and a certification.

Data Science Training for Programmers Approach 5: Apprenticeship under Senior Data Scientists

Although many programmers may be partially skeptical about this training mode, an apprenticeship under senior Data Scientists may be the most challenging and rewarding way to learn about Data Science. The problem with this approach is that the apprentice, though an experienced programmer or database expert may be made to feel like a novice working under the expert thought leaders; but, if the professional can develop a thick skin, the reward will be lifetime knowledge gained from experts!

Developing the Right Mindset

After all is said and done, what exactly is the right mind set for success in Data Science jobs?

The Data Scientist’s primary role is to convert a business problem into a set of data questions, develop predictive models to answer the questions, and then develop a story to communicate the findings. In that role, the Data Scientist plays the bridge between the theoreticians and the business operators.

The “mind-set” attribute is probably the most important job requirement, much more important than any of the previously mentioned training modes. Good programmers, who are already skilled in their jobs, must demonstrate the following character traits to be considered for Data Science jobs:

  • A passion for data and anything about data
  • An intense curiosity to dig deep into data
  • A sound background in multiple programming languages, databases etc
  • An ability to extract a story from data trends and patterns
  • Strong communication skills

Readers of this article can find additional tips on How to Get Hired As a Data Scientist in this article.

About the author

Paramita Ghosh has over two and a half decades of business writing experience, much of which has been writing for technology and business domains. She has written extensively for a broad range of industries, including but not limited to data management and data technologies. Paramita has also contributed to blended learning projects. She received her M.A. degree in English Literature in 1984 from Jadavpur University in India, and embarked on her career in the United States in 1989 after completing professional coursework. Having ghostwritten and authored hundreds of articles, blog posts, white papers, case studies, marketing content, and learning modules, Paramita has included authorship of one or two books on the business of business writing as part of her post-retirement projects. She thinks her professional strength is “lifelong learning.”

You might also like...

Webinar: Conformed Dimensions of Data Quality – An Organized Approach to Data Quality Measurement

Read More →