A combination of data analysis and mathematics, data science is one of today’s hottest fields. Depending on what area of data science interests you most, you might consider learning Python programming, SQL, and machine learning.

Data science professionals are needed in a range of sectors, both public and private, and different sectors define different positions. In addition to the most obvious title of Data Scientist, data-centered roles can include titles like:

  • Data Engineer
  • Artificial Intelligence (AI) Engineer
  • Machine Learning (ML) Engineer
  • Machine Learning (ML) Scientist
  • Business Intelligence Developer
  • Data Analyst
  • Data Architect
  • Infrastructure Architect
  • Applications Architect

These positions can vary greatly, and salaries often reflect the difference. A top Data Analyst position may not pay anywhere near what a top Data Scientist role pays. Training, education, and the field will dictate terms.

Where to Begin 

The U.S. Bureau of Labor Statistics lists the mean annual wage for Data Scientists as $108,660, falling within an extremely wide range between $59,000 and around $167,000 per year. While this doesn’t exactly render the mean wage figure meaningless, it does point out the need for more targeted information. Here are some top considerations before you begin to research the overall data science field.

  1. Location - Data scientists in Washington and New York earn the highest annual salaries, while states like Nebraska and Oklahoma rank toward the bottom of the list. If you’re willing to relocate for the right job, consider all your options.
  2. Sector - IT services, healthcare, cybersecurity, banking and financial services, and digital marketing are all sectors that require data science professionals. Additionally, there are sub-sectors within many of these.
  3. Budget - Data science training can be expensive. Your career goals and plans should match your ability both to learn and to finance your education if necessary.

Starting a Data Science Career

If you plan to study data science, you most likely know that research and self-study won’t take you too far. The complexity of the field, combined with the number of sectors that require different skills, lends itself to education in the classroom setting. Classes can be either in-person or online. The main question then becomes: what’s the best way to learn?

Consider the primary three alternatives of single classes for beginners, college degrees, and bootcamps or certificate programs.

Single Classes

Although introductory courses in some topics can be a great way to start, they typically will not add up to enough data science knowledge to gain work in the field. The following subjects are essential to many data scientists, and a single class can help you enter the discipline as a beginner:

  1. Python - Python data science is common today, as libraries like Pandas and NumPy provide essential capabilities. While not every Data Scientist makes Python their language of choice, many do.
  2. Machine Learning (ML) - Often paired with Python, machine learning is a discipline in itself. You’ll need to master substantial ML tools and algorithms for positions like Machine Learning Scientist, Machine Learning Engineer, or Machine Learning Architect.
  3. SQL - Structured Query Language, or SQL, is used to communicate with databases. Often taught alongside Python in bootcamps, the popular programming language is essential for Data Analysts and Administrators, as well as many Developer positions.
  4. R - Another language common for data science, data analysis, and software engineering, R programmers are in high demand today among researchers, statisticians, and data miners of all types.
  5. JavaScript - A top programming language in nearly every field, JavaScript and its frameworks and libraries include React, Node.js, Vue.js, and jQuery.

College Education

A college degree held by a data science professional may not be in data science per se. In fact, many professionals get into the field by way of a Business Administration, Computer Science, or Engineering program. This means that not everyone who gets into data science even knows they want to work in the field before they declare a major.

If you already know you want to learn data science, you may be able to get work much more quickly while saving money if you get there by way of the next option.

Bootcamps/Certificate Programs

The bootcamp learning model has become increasingly popular among students and busy professionals, with good reason. For certain fields, bootcamps offer a way to learn principles and essential tools in a matter of weeks or months, not years.

Noble Desktop, which hosts this search tool, provides multiple bootcamps and certificate programs for data science students. The following are all options in the field.

  1. Data Science Certificate - Noble’s flagship product for students planning careers in data science, this certificate program is offered in three weeks full-time or three months part-time. It combines a Python for Data Science Bootcamp and Python Machine Learning Bootcamp with training in automation and SQL. You’ll learn NumPy, Pandas, and Matplotlib on the way to earning a verified Certificate of Completion.
  2. Data Analytics Certificate - For students interested in the data analytics aspect of data science, this program prepares you for work as a Data Analyst or Business Analyst. Combining Excel and data analytics foundations training, it includes the Python for Data Science and Python Machine Learning Bootcamps. You’ll also get in-depth training in Tableau, one of today’s most popular data visualization tools.
  3. Python Data Science & Machine Learning Bootcamp - If you want to be a skilled Python programmer, you’ll need to learn Pandas, NumPy, and Matplotlib. This program covers all of them in the Python for Data Science Bootcamp, and adds the Python Machine Learning Bootcamp along with Python for Automation. It’s perfect training for entry-level data science or Python engineering roles.
  4. FinTech Bootcamp - Many data professionals study with the goal of working specifically in financial and investing analysis. Noble Desktop’s FinTech Bootcamp paves the way, with a special Python for Finance Bootcamp. Also included are the Python for Data Science Bootcamp and Python Machine Learning Bootcamps, along with SQL and automation training.

Data Science Professionals and Career Advancement

Once you begin working in the data science field, your goals for furthering your education may change. Data Scientists’ ongoing training can differ significantly from that of Financial Analysts, so there are no hard and fast rules as to what you’ll need to succeed.

If you want to level up to a better position in the future, you may want to join networking groups, professional organizations, and even obtain particular certifications. Here are some examples of popular certifications.

  • SAS Advanced Analytics Professional Certification
  • SAS Certified Data Curation Professional 
  • Microsoft Certified Azure Data Scientist Associate 
  • DASCA: Senior Data Scientist
  • Google Professional Data Engineer Certification

Of course, there are many other options, including Amazon AWS Big Data Certification, MySQL Certification, and Dell EMC Data Science Certification. Your position and its requirements may push you toward one over another, and the job you want may require still another. Be open to learning, and make sure you have the bandwidth to take on additional certification training.

Final Thoughts

Preparing for a career in data science can be much more complex than moving from an established role to one higher in the field. Depending on your position, you’ll need to have more or less expertise in areas like machine learning, modeling, programming, databases, and statistics. To move up from one level to another in the field, you might need an entirely new certificate program, on-the-job training, or even a degree.

If you use your training to become a Data Scientist, consider the following professional organizations:

  • The Data Science Association - Non-profit professional association of data scientists
  • INFORMS - Institute for Operations Research and the Management Sciences
  • Association of Data Scientists (ADaSci) - Global body of data science and machine learning pros
  • Society for Data Science (S4DS) - Non-profit, includes data science pros and academia
  • Data Science Council of America (DASCA) - Offers multiple advanced certifications

Whether you specialize in an area like Python data science or train as a Machine Learning Engineer, the study of this interdisciplinary field can pay dividends both personally and professionally, as you work toward your highest career goals.