Can a Non-Technical Student Become a Data Scientist?

Statistics reveal that 90% of the world’s data was generated in the last few years, thanks to data engineers, analysts, and scientists. The last group identifies the right data sources from where they can find relevant patterns to solve business-related problems. Can a person without a technical background join their ranks as a data scientist?

A non-technical student can become a data scientist, provided they have the right approach, motivation, intense curiosity, and the dedication to seek information. They should also be willing to learn the required skills, such as statistical analysis, programming, and machine learning. 

Anyone adept at logical thinking and armed with a structural thought process, a willingness to learn new tools, and a spot-on business acumen can get into data science. Read on to find out how.

Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!

What Do Data Scientists Do?

Listed below are some of the most common tasks that data scientists perform on a day to day basis:

• They make sense of vast amounts of structured and unstructured data and expose hidden solutions to complicated business problems.

• They analyze data from a business standpoint and predict outcomes.

• They use machine learning for prediction purposes.

As data scientists extensively utilize machine learning, optimizing data models is an important part of their daily routine. Most of them can perform the analysts’ tasks. However, their work is different in terms of their data that come from multiple and disconnected sources. They are also more adept at making better business judgments.

Roles and Responsibilities

Listed below are the major roles and responsibilities that data scientists handle on a day to day basis:

• They choose features, build, and optimize classifiers by using machine learning techniques.

• They perform data mining.

• They perform predictive data analysis by processing, sorting, and integrating data using machine learning and predictive algorithms.

• They develop data algorithms and models.

Salary Comparison

Listed below are the average annual salaries for the three most common data science job titles:

• Data analyst: $65k to $170k

• Data engineer: $80k to 170k

• Data scientist: $95k to $250k

According to the US Bureau of Labor and Statistics, a data scientist’s national average salary is $118,000.

Recommended Educational Qualifications

The most recommended educational qualifications for data scientists are as follows:

• A bachelor’s degree in any of these courses will give you the skills needed to process and analyze big data and to transition to data science, including computer science, social science, physical science, statistics, astrophysics, or software engineering

• A specialization in mathematics or statistics

• A master’s degree, though not mandatory, is an added advantage because, if one considers today’s data science environment, 46% of all the data scientists currently employed hold doctorate degrees, and 88% have at least a master’s degree. 

It is possible to learn data science without a computer science or mathematics background, or a postgraduate degree, and get a data science job. Lack of a highly quantitative degree shouldn’t bar high-functioning individuals with knowledge, expertise, and skills in other fields from learning data science.

After all, even those with postgraduate degrees still have to undergo training to learn additional specialized knowledge. While you don’t necessarily need a specific degree, you have to acquire the necessary skills.

Required Skills

Listed below are some of the must have skills for data scientists:

• Must have an astute business insight, which includes the ability to discern which problems should be prioritized.

• Must be capable of identifying new ways of leveraging the data of a corporate endeavor.

• Must have the ability to clearly and concisely translate and convey technical findings to non-technical personnel, such as sales or advertising staff, including providing business leaders with quantified insights to enable them to make the right decisions.

• Must have the ability to employ data storytelling, which involves creating storylines around data to make it understandable. This requires filtering to identify the results and values embedded in the analyzed data. This makes the information palatable to business owners so they don’t have to delve into the back roads of what scientists have uncovered and just learn how the data can positively impact their business.

• Must possess intellectual curiosity or the inclination to acquire more knowledge and update it. Data science is fast-evolving, and you need this trait to sift through massive amounts of data to discover answers and gain more insights. More importantly, it will help you remain at the top of your field. 

• Must know advanced data visualization competencies.

• Must be proficient in statistical modeling, data mining, data wrangling, and machine learning on extensive and complex data sets.

• Must have the ability to program in at least one high-level programming language, such as Python, Java, Perl, Ruby, C/C++, or SAS. Python is the most common coding language required in data science roles. According to O’Reilly’s survey, 40% of their data scientist respondents use Python as their major programming language. Thanks to Python’s versatility, users can create any data sets found on Google.

• Proficiency in R programming is preferred because it is specifically designed for data science needs. You can use it to solve any problem in data science. Unbelievable? Just ask 43% of data scientists who use it to solve statistical problems. Although R has a steep learning curve and may thwart you if you’ve already mastered another programming language, you can always learn from books or online resources.

• Must have the ability to write and execute complex queries using SQL database/coding. A structured query language, SQL allows users to carry out operations like add, delete, and extract data from a database. It helps to users execute analytical functions and to transform database structures. It is specially designed to access, communicate, and work on data.

• Must be competent in the latest technologies, such as Big Data querying, machine learning, deep learning, or artificial intelligence. Machine learning techniques, including decision trees and logistic regression, help solve various data science problems based on predictions of major organizational outcomes.

• Advanced machine learning skills include reinforcement learning, adversarial learning, supervised and unsupervised machine learning, neural networks, time series, natural language processing, outlier detection, computer vision, recommendation engines, and survival analysis.

• Must be competent in Hadoop, the platform used for data exploration, data filtration, data sampling, and summary execution. Hadoop isn’t a requirement, but it is preferred in many cases. According to a study by CrowdFlower, Apache Hadoop, with a 49% rating, is the second most essential skill for data scientists.

• Experience with Hive or Pig, the two key components of Hadoop’s ecosystem

• Familiarity with cloud tools, such as Amazon S3 (Amazon Simple Storage Service)

• Must be competent in Apache Spark, which is designed for data science to run its complicated algorithm faster. It disseminates data processing when dealing with a huge amount of data, saving users time. Apache Spark helps data scientists handle complex, unstructured data sets. It can be used on one machine or a group.

• Must have the ability to visualize data with data visualization tools, such as ggplot, d3.js, Matplotlib, and Tableau. These convert complex results from projects into easy-to-understand formats. Data visualization helps organizations work directly with data. It helps them obtain insights that allow them to act on new business opportunities and stay ahead of the competition.

• Must be proficient in coding, mathematics, statistics, programming, and communication.

• Must have the ability to understand and manipulate unstructured data, also known as dark analytics, from different platforms. Unstructured data are undefined contents that don’t fit into database tables. Sorting through these data types is difficult, as they are not streamlined. Working with dark analytics allows data scientists to unravel insights that are beneficial in decision-making.

• Must have the willingness to be a team player and collaborator. Data specialists cannot work by themselves. Part of a data scientist’s role is to develop strategies with company executives, producing better products with product managers, present concepts to designers, launch conversion campaigns with marketers, and create data pipelines and improve workflow with client and server software developers.

• Must be inclined to go beyond classroom learning by building an app, blogging, or exploring related concepts.

It is worth to take into account that not all the above-mentioned skills are required for a non-technical student to become a data scientist. A few of these skills and a somewhat knowledge of others is sufficient.

Major Companies Hiring Data Scientists

The fields of data science and analytics have opened up many job opportunities. The growth will be exponential in the coming years. Because of this, many people with non-technical backgrounds want to switch to data science. This transition requires persistence and hard work but is achievable.

Real-life stories abound of data scientists who reaped the rewards of continuing education and were subsequently hired by big corporations. These are just some of them:

• Google
• Facebook
• IBM
• Amazon
• Accenture
• Intel
• Walmart
• Oracle
• Apple
• Spotify
• Adobe
• Microsoft
• American Express
• Tesla

What Do Data Science Students Learn?

Students learn how to use data science to solve modern-day business problems. Python-based curricula introduce best practices in machine learning, statistical analysis, natural language processing, and data visualization. Students examine case studies and master the tools needed to secure jobs as data scientists.

Skill Upgrade for Employability

Students drawn to data science come from backgrounds such as data analysis, engineering, and mathematics. But you don’t have to be in these fields to learn the subject.

At institutions like Galvanize, students develop substantive connections with faculty members and career services staff. Together, they help students identify strengths and define goals. More importantly, they connect students with their 2,250-plus hiring partners.

Sources of Funding

Many course providers offer tuition assistance. Galvanize, for instance, has an Income Share Agreement scheme, where students learn from their boot camps first, then pay tuition when they’re employed.

This is subject to regulatory approval, however. They also ask for an up-front deposit of $2,000. Then, once their students make at least $60,000 per year, they are expected to surrender to Galvanize 10% of their income for up to 48 months, with repayment capped at 1.4 times the full tuition fee, which is $17,980.

Galvanize awards full-tuition scholarships in software engineering and data science to two exceptional students per cohort to their immersive programs. It also offers veterans benefits and financing plans through their lending partners, Skills Fund, and Climb Credit.

Students may opt to ask their employers or companies, such as IBM, Google for Entrepreneurs, Adobe, Silicon Valley Bank, Women Who Code, and Atlassian, to sponsor their data science education.

What the Naysayers Say

Not everyone agrees that anyone can be a data scientist. One Quora contributor has painted a bleak picture for aspiring data scientists but was probably referring only to a certain region’s data science environment. He says that it is very difficult for non-technical people to become data scientists because they need engineering skills beforehand.

He stresses that it’s a waste of time to pursue a data science career if one is not already an engineer and is equipped with proficiency in programming languages. Companies hiring data scientists always prefer engineers because they already have more than half of the related skills when they graduate. He also criticizes some low-end data analysts from non-engineering backgrounds who call themselves data scientists but are not so.

He accuses many coaching centers in the region that claim “they will make you a data scientist in months.” He says these organizations promulgate money-making schemes, “imparting zero valuable skills.”

What the Cheerleaders Say

There is hope for novices, though. Take it from Springboard. They say entering data science may not be the easiest task for those without a technical background, but it is not impossible, either. The path is difficult to tread, since there is “much to learn, unlearn, and relearn.”

With the right amount of motivation and guts, however, anyone can enter data science. You don’t have to be a scientist to work in this field. Don’t forget there are multiple paths that you can take in a data science career. Data Engineers and Analysts are two such career tracks. Additionally, you can practice data science without holding a title.

Learning Data Science for Beginners

How Do You Transition From a Non-Technical Field to Data Science?

Get a kick start on the field with tutorials and courses for beginners. Check out the various online course providers, such as EdX, Udemy, Udacity, Codecademy, and Coursera for big discounts during the pandemic.

An example is Stack Social’s The Very Big Data & Apache Hadoop Training Bundle, currently on sale for $29 (99% off the original course fee of $3,000). Another is Data Science Essentials by EdX.

Other avenues for learning data science:

• Books
  • Think Stats Book: Exploratory Data Analysis
  • Python Data Science Handbook
  • Statistics for Dummies by Deborah Rumsey

• Degree Programs – Maryville’s data science degree integrates computer skills with mathematical theory.

• Certificate Programs – UC Berkeley Extension offers a Certificate Program in Data Science.

• Mixed Courses
  • Intellipaat’s Data Science Architect Master’s course – Students can choose either self-paced training for $702 or attend an online classroom for $1,099.
  • Khan Academy’s Statistics and Probability course
  • Flatiron School’s 15-week data science course – Flatiron claims this about their alumni: 93% employment rate for both on-campus and online graduates, $76k average starting salary for on-campus graduates, and $72k average starting salary for online graduates.

• Presentations – IBM’s Data Science Demo

• Video Tutorials
  • Intellipaat’s Data Science For Beginners
  • Intellipaat’s Data Science Tutorial—Learn Data Science from Experts
  • Jose Portilla’s Udemy courses
  • Code Basics Hub’s free data science and data analysis courses

• Boot Camps
  • Columbia Engineering’s Data Science Boot Camp – Students get applicable machine learning training and hands-on experience for building portfolios.
  • Data Science Immersive Boot Camp by Galvanize – They use a Python-based curriculum, real-world case studies, and machine learning concepts to prepare students for data science careers. Their courses used to take place on-site but are now delivered live online during the pandemic. The same on-site instructors deliver the courses online, and students can interact with each other.
  • For those who want to test the waters first, Galvanize offers a free fast-track coding boot camp prep course where students learn fundamentals like Python, machine learning, and SQL needed to succeed in their Data Science Immersive Boot Camp.

• Blogs
  • Intellipaat’s Data Science Blogs
  • Springboard’s “How to Learn Data Science Without a Degree”
  • Great Learning’s “How to Get Into Data Science from a Non-Technical Background”

• Data Science Groups – Join relevant associations, such as LinkedIn Groups, for interaction with other members of the data science community.

• Data Science Competitions – Participation in these contests is optional but can help aspiring data scientists in the long run. Kaggle hosts competitions that allow future data scientists to practice and hone their real-world data skills and tackle actual business problems. Employers take Kaggle rankings seriously. They see these as legitimate and relevant hands-on project-based work.

• Data Science Events – Attend workshops, seminars, and expos to discover trends, obtain the latest information from industry experts, and network with personalities. 

• Mock Interviews – These gauge your expertise level and reveal what hiring managers are looking for. Some courses offer students assistance in career transition and placement support.

• Mentors – The advantages of a seasoned mentor include obtaining valuable insights from professionals’ experiences, networking opportunities, and having a resource for securing advice.

Conclusion

Becoming a data scientist is not for the weak. But it is possible for those with conviction. There is an adage that goes: “Faint heart never won fair lady.” Aspiring data scientists without a technical background should replace “fair lady” with “data science proficiency.” This mantra will propel aspirants to greater heights. Use it to fulfill your goal.

Prove your detractors wrong. The more they discourage you from reaching your seemingly impossible dream, the more you should fight. Push yourself to the limit. For anyone with a strong foundation in faith, nothing is impossible.

BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.

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