|

From Musician to Data Scientist: How Zachary Greenberg Orchestrated a Career Change

Zachary Greenberg headshot

When the COVID-19 pandemic struck in 2020, Zachary Greenberg found himself at a crossroads. After a successful decade-long career as a professional singer performing at theme parks and on cruise ships, the global shutdown of live entertainment left him out of work and rethinking his future.

"I never would have imagined a world where live performances just didn‘t exist anymore, but that‘s the reality we were facing," Zachary said. "I knew I needed to make a change."

With a bachelor‘s degree in psychology and a background in statistics, Zachary was drawn to the booming field of data science. "I kept hearing about how data science was this incredible intersection of math, computer science, and real-world impact. It seemed like a perfect fit for my skills and interests," he explained.

Data Science Takes Center Stage

Data science has exploded in popularity and importance in recent years as organizations seek to harness the power of data for better decision making, innovation, and efficiency. IBM estimates that the number of data science and analytics job listings will grow from 364,000 in 2020 to 2,720,000 by 2025.

Bar chart showing projected growth of data science and analytics jobs
Source: IBM Burning Glass Report

But what exactly is data science? At its core, data science is the practice of using scientific methods, processes, and systems to extract insights and knowledge from structured and unstructured data. Data scientists leverage skills in math, statistics, programming, and domain expertise to tackle data-intensive problems and drive informed decision making.

Some common data science tasks and techniques include:

  • Collecting, cleaning, and processing large, complex datasets
  • Exploratory data analysis to spot patterns, trends, and relationships
  • Statistical modeling and machine learning to make predictions and recommendations
  • Data visualization to communicate insights to technical and non-technical audiences
  • Extracting actionable insights to drive business value and innovation

The impact and applications of data science span virtually every industry, from healthcare and finance to retail and entertainment. By harnessing data, organizations can improve operational efficiency, personalize products and services, mitigate risks, innovate faster, and create better customer experiences.

Pie chart of top industries hiring data scientists
Top industries hiring data scientists. Source: Glassdoor

Becoming a Data Scientist

To gain the necessary skills to break into data science, Zachary enrolled in Programming School‘s immersive data science bootcamp. The full-time, 15-week program covers key data science concepts, tools, and techniques, including:

  • Python programming: Writing scripts and working with powerful data science libraries like NumPy, Pandas, and Matplotlib
  • SQL databases: Querying and manipulating relational databases to extract and analyze data
  • Statistics and probability: Applying statistical methods and models to datasets
  • Machine learning: Building and deploying predictive models using popular ML algorithms like linear regression, classification, clustering, and natural language processing
  • Big data and cloud computing: Analyzing massive datasets using cloud platforms and big data tools like AWS, Hadoop, and Spark
  • Data visualization: Creating impactful charts, graphs, and dashboards to communicate data insights using tools like Matplotlib, Seaborn, and Tableau
  • Capstone projects: Developing end-to-end data science solutions for real-world problems

"The program was intense but incredibly rewarding," Zachary said. "I went from dabbling in Python to building complex machine learning models in just a few months."

For his capstone project, Zachary analyzed customer churn at a telecommunications company and built a predictive model to identify at-risk customers for proactive retention efforts. "It was amazing to see how data science could directly impact a company‘s bottom line and customer experience," he shared.

Programming School graduates have gone on to land data science roles at top companies like Apple, Google, Twitter, Spotify, Nike, and more. In fact, Flatiron reports that 98% of their on-campus graduates land a job within a year, with an average starting salary of $74,566.

But a bootcamp isn‘t the only path to becoming a data scientist. Other common educational routes include:

  • Earning a master‘s degree in data science, computer science, statistics, or related STEM field from a college or university. Degree programs tend to be longer (1-2 years) and more expensive than bootcamps but provide more in-depth theoretical knowledge.
  • Self-studying data science through online courses, tutorials, books, and projects. This approach requires a lot of self-motivation and discipline but allows aspiring data scientists to learn at their own pace and tailor their education to their specific interests. Some popular online learning platforms for data science include Coursera, Kaggle, DataCamp, and edX.
  • Transitioning from adjacent roles like business analyst, software engineer, or data engineer and picking up data science skills on the job or through additional training. Many organizations offer professional development opportunities for employees to upskill into data science.

Regardless of the path, the most effective way to learn data science is through hands-on practice with real datasets and problems. "Having a strong portfolio of data science projects was the single most important factor in landing my first role," Zachary said. "Employers want to see that you can take a data problem from start to finish and communicate the results effectively."

Some data science portfolio project ideas include:

  • Analyzing your Spotify listening history to create a personalized playlist generator
  • Scraping and visualizing data from your favorite subreddit to identify top trends and topics
  • Building a machine learning model to predict housing prices based on features like square footage, location, and number of bedrooms
  • Creating an interactive COVID-19 dashboard to track cases, hospitalizations, and vaccinations by region

Landing a Data Science Job

After completing Programming School‘s bootcamp, Zachary landed a data science internship at Sentara Healthcare and was eventually promoted to a full-time data scientist consultant role at Guidehouse, where he‘s been working for the past two years.

"I get to work with a talented, cross-functional team and tackle fascinating data challenges every day," Zachary said. "We‘ve built machine learning models to predict patient readmission risk, analyzed social determinants of health data to improve health equity, and created dashboards to track key performance metrics. It‘s incredibly rewarding work."

According to hiring managers and recruiters, the top skills they look for in data science candidates include:

  • Technical skills: Proficiency in programming languages (Python, R, SQL), statistical and machine learning methods, and data viz tools
  • Problem-solving skills: Ability to break down complex data problems, ask the right questions, and devise creative solutions
  • Communication skills: Ability to explain technical concepts to non-technical stakeholders and present insights in a clear, compelling way
  • Business acumen: Understanding of how data insights translate to business value and decision making
  • Continuous learning: Commitment to staying up-to-date on the latest data science tools, techniques, and best practices in a constantly evolving field

"My biggest advice for aspiring data scientists is to focus on building a strong foundation in the fundamentals and to never stop learning," Zachary shared. "Data science is a vast, complex field and you‘re never going to know everything. The most successful data scientists are the ones who are always looking for opportunities to learn and grow."

Other tips for landing a data science job include:

  • Tailoring your resume and portfolio to highlight your most relevant skills and projects for each role
  • Practicing data science interview questions and technical exercises on platforms like LeetCode and HackerRank
  • Attending data science meetups, conferences, and networking events to meet other data scientists and learn about job opportunities
  • Contributing to open source data science projects and engaging in online communities like Kaggle and GitHub to build your skills and reputation
  • Seeking out mentors in the field who can provide guidance, feedback, and connections

The future looks bright for data science careers, with the BLS projecting 31% growth in data science jobs from 2020 to 2030, much faster than the average for all occupations. As more industries and organizations realize the value of data-driven decisions, opportunities for data scientists will continue to expand.

Bar chart of top cities hiring data scientists
Source: Indeed

Embracing the Data-Driven Future

Two years into his data science career, Zachary has no regrets about making the leap from the stage to the server. "I never would have predicted I‘d be working in healthcare tech, but I absolutely love it," he said. "I get to be creative, solve interesting problems, and make a real impact with data every day. I‘m excited to see where my data science journey takes me next."

For anyone considering a similar career change, Zachary offers this advice:

"Don‘t be afraid to take a risk and bet on yourself. With hard work, dedication, and a commitment to lifelong learning, it‘s never too late to pivot into a meaningful new career in data science. The world of data is waiting for you."

Are you ready to begin your own data science journey? Check out these resources to get started:

References

  1. IBM Burning Glass Report
  2. Glassdoor Data Scientist Job Market Report
  3. Programming School Student Outcomes
  4. BLS Occupational Outlook for Data Scientists
  5. Indeed Best Cities for Data Science Jobs

Similar Posts