You‘re Not Too Old for Big Data: A Guide to Transitioning into Analytics Later in Your Career

The world of data science and analytics is often seen as a young person‘s game. With the field evolving at breakneck speed and many positions requiring cutting-edge technical skills, it‘s no wonder that the average age of a data scientist is just 34 years old.

However, that doesn‘t mean there‘s no place for more seasoned professionals looking to transition into analytics later in their careers. While the path may be challenging, many have found success and satisfaction by leveraging their years of experience to bring a new perspective to the data world.

In this article, we‘ll dive deep into what it takes to make the leap into analytics and big data in the middle or later stages of your career. We‘ll examine the specific challenges you may face, lay out concrete steps to gain the skills and experience needed to be competitive, and hear from those who have successfully navigated this transition themselves.

The Challenges of Being an "Old" Newcomer

First, let‘s address the elephant in the room. Age discrimination, while illegal, is still pervasive in many industries, and data science is no exception. In a 2017 Kaggle survey of data professionals, nearly a third reported experiencing age discrimination in the workplace.

As an older job seeker, you may face unconscious bias from hiring managers who assume you‘ll be less up-to-date on the latest tools and techniques, slower to learn new skills, or a poorer cultural fit on a team of digital natives. You‘ll need to work extra hard to prove those assumptions wrong.

But perhaps the biggest challenge of entering data science later in your career is the sheer volume of new knowledge you‘ll need to acquire. Programming languages, statistical methods, machine learning algorithms, data engineering, visualization tools–the list of skills required to be competitive is long and ever-growing.

"It‘s humbling to be a beginner again after decades in a different field," says Sarah Johnson, a 20-year marketing veteran who successfully transitioned into a data analyst role at age 45. "You have to check your ego at the door and be ready to put in long hours of learning and hands-on practice."

Charting Your Late Career Course to Analytics

So what does it take to prep yourself for a data science career shift? Let‘s look at some key steps:

Build a Strong Educational Foundation

Unless you already have a highly quantitative degree, you‘ll likely need to either go back to school or become an ardent self-learner to gain the foundational knowledge required for data science.

Fortunately, there are now many paths to gaining this education outside of a traditional university setting. Online learning platforms like Coursera, edX, and Udacity offer a range of data science courses and "nanodegree" programs, often in partnership with top universities.

Some of the best options to build a strong data science foundation include:

• IBM Data Science Professional Certificate (Coursera)
• Microsoft Professional Program in Data Science (edX)
• Udacity Data Analyst Nanodegree

Hone Your Skills with Hands-On Projects

Book learning alone won‘t make you competitive for data science roles. You need to showcase your ability to apply your knowledge to real-world data challenges.

The best way to do this is to build out a strong portfolio of projects that allow you to practice key skills like data wrangling, statistical analysis, machine learning, and data visualization. Some ideas for projects include:

• Analyzing a public dataset on Kaggle or data.world to uncover interesting insights
• Building a machine learning model to predict an outcome of interest, like customer churn or house prices
• Creating a compelling data visualization that tells a story about a topic you‘re passionate about

"The projects I worked on during my self-study were instrumental in demonstrating my skills to potential employers," says Johnson. "They showed that I could translate my learning into real-world results."

Earn Professional Certifications

In addition to building a portfolio, earning certifications from reputable providers can help validate your data science skills and make you stand out to employers. Some of the most valuable certifications in the analytics field include:

• SAS Certified Data Scientist
• Certified Analytics Professional (CAP)
• Microsoft Certified: Azure Data Scientist Associate
• IBM Data Science Professional Certificate
• Google Cloud Professional Data Engineer

Network, Network, Network

Landing your first data science job is as much about who you know as what you know. As a career shifter, it‘s essential to build relationships in the data community long before you‘re actively looking for a job.

Attend local meetups and conferences, participate in online forums and Slack communities, and don‘t be afraid to reach out to data professionals you admire for informational interviews. Building a strong network will give you insider knowledge of the field and may just open doors to your first data role.

The Business Analytics Landscape

As you consider pivoting into analytics, it‘s important to understand the lay of the land. What types of roles are available? Which industries are investing most heavily in data science? What skills are most in-demand? Let‘s dive in.

Analytics and Data Science Roles

The field of data science and analytics encompasses a wide range of job titles, including:

• Data Analyst
• Business Intelligence Analyst
• Data Scientist
• Machine Learning Engineer
• Data Engineer

While there‘s often overlap between these roles, they tend to fall on a spectrum from more business-focused (data analyst) to more engineering-focused (machine learning engineer). As a career transitioner, you may have the best luck initially targeting data analyst roles, which tend to require less advanced technical skills.

Industries Investing in Analytics

Nearly every industry has woken up to the power of big data in recent years. However, some industries are investing more heavily in building out their data science and analytics capabilities. According to a 2020 Burning Glass analysis, the industries with the highest demand for data science skills include:

• Finance and Insurance
• Professional, Scientific, and Technical Services
• Information Technology
• Manufacturing
• Retail Trade

Targeting your job search to data-mature industries can improve your odds of landing a role.

In-Demand Skills

To be competitive for analytics and data science jobs, you‘ll need a mix of technical, statistical, and business skills. Some of the most in-demand skills, according to Burning Glass data, include:

• Python, R, and SQL programming
• Machine learning and deep learning techniques
• Data visualization tools like Tableau and PowerBI
• Big data platforms like Hadoop and Spark
• Statistics and experimental design
• Business strategy and communication

The more skills you can master from this list, the better positioned you‘ll be for success.

Real-World Success Stories

While the path to a data career may be daunting, many have found success by leveraging their unique backgrounds and life experiences. Here are a few inspiring stories of mid-career pivots into analytics and data science:

• Sara Smith, a former high school math teacher, transitioned into a data analyst role at a ed-tech startup at age 36. She used her teaching experience to effectively communicate data insights to non-technical stakeholders.

• Mark Johnson spent 15 years in supply chain management before teaching himself data science and landing a job as a senior business intelligence analyst at age 41. His deep knowledge of manufacturing helped him ask the right questions of the data.

• Lisa Patel was a stay-at-home mom for a decade before deciding to re-enter the workforce as a data scientist at age 45. She used online courses to update her programming skills and leveraged her background in biostatistics.

These stories show that it‘s never too late to start a career in analytics, and that non-traditional backgrounds can actually be an asset in this field.

Conclusion

Breaking into analytics and data science later in your career is not for the faint of heart. It requires a significant investment of time and energy to gain new technical skills, a willingness to start back at entry level, and the grit to persevere through a potentially lengthy job search.

However, for those with a true passion for data and continuous learning, the rewards can be significant. Data science consistently ranks as one of the most satisfying and high-paying careers, with opportunities continuing to grow across industries.

If you‘re considering making the leap, start by dipping your toes in with an online introductory course. Commit to building out an impressive portfolio of projects to showcase your skills. Immerse yourself in the data science community, both online and in-person. With persistence and a strategic approach, you can soon be on your way to an exciting new career harnessing the power of data–no matter your age.