What do data science apprentices do?

Data science is one of the fastest growing and most in-demand fields today. As organizations collect more data and look to gain insights from it, the need for data scientists continues to increase. Data science apprenticeships are an effective way for aspiring data scientists to gain real-world experience while still learning on the job.

What is a data science apprenticeship?

A data science apprenticeship is a paid, full-time job where an apprentice learns data science skills through on-the-job training and mentoring. Apprentices work alongside experienced data scientists and analysts to gain practical experience working with data. Apprenticeships typically last 1-2 years.

Data science apprenticeships combine hands-on learning with classroom-based instruction. Apprentices take online courses and participate in bootcamps to build their technical abilities. They apply these skills directly in their day-to-day work. This learn-while-you-earn model allows apprentices to develop data science skills faster than through traditional education routes.

What do data science apprentices learn?

Data science apprentices learn a wide range of technical and professional skills. Here are some of the key things apprentices will learn on the job:

Technical skills

• Programming languages like Python, R, and SQL
• Statistical modeling and data analysis
• Data visualization using tools like Tableau
• Machine learning algorithms
• Data engineering skills like APIs and cloud computing
• Math skills like calculus, algorithms, and linear algebra

Professional skills

• Problem-solving
• Critical thinking
• Project management
• Communication and storytelling with data
• Collaboration

What do data science apprentices do on the job?

Data science apprentices are actively involved in real data science projects and workflows. Some common tasks and responsibilities include:

Collecting, cleaning and organizing data

Apprentices learn to gather data from sources like databases, APIs, websites, sensors, and more. They format and clean the data to prepare it for analysis.

Performing exploratory data analysis

Exploring and visualizing data to uncover patterns, trends, and insights. They use statistics and programming to analyze dataset variables.

Building machine learning models

Under the guidance of experienced data scientists, apprentices construct machine learning models to make predictions from data. They learn techniques like classification, regression, and clustering.

Developing dashboards and data visualizations

Apprentices create dashboards, reports, and data visualizations to communicate insights from data analysis. They use tools like Tableau, Power BI, and Python libraries.

Documenting and presenting results

Throughout projects, apprentices document their methodology, results, and findings. They also learn to present analysis results and recommendations to stakeholders.

Common industries and roles for data science apprentices

Data science apprentices gain experience in a variety of industries and companies. Some common roles include:

Tech companies

• Data analyst
• Business intelligence developer
• Data engineer

Finance and banking

• Quantitative analyst
• Data science specialist
• Risk analytics associate

Healthcare

• Clinical data analyst
• Health informatics specialist

Retail and ecommerce

• Customer intelligence analyst
• Digital marketing analyst

Manufacturing and automation

• Quality control data analyst
• Sensor data analyst

Benefits of data science apprenticeships

Here are some of the main benefits of entering the data science field through an apprenticeship:

• Earn a salary – Apprentices earn competitive salaries while learning on the job.
• Gain real experience – Apprentices apply concepts and build skills by working on actual data science projects.
• Receive mentorship – Experienced data scientists provide coaching and supervision to help apprentices grow.
• Take classes – Apprentices strengthen their skills with supplemental training like online courses.
• Get certified – Many programs prepare apprentices for data science certifications.
• Start a career – Companies often hire apprentices full-time after completion of the program.

Challenges of data science apprenticeships

While data science apprenticeships offer excellent on-the-job training, there are some challenges to be aware of:

• Apprentices need to learn and work at the same time, which can be demanding.
• Rotating through different teams and projects keeps things interesting but can be disruptive.
• Balancing supplemental coursework with full-time job responsibilities takes discipline.
• Having less choice in selecting projects means potentially working on tasks outside your interests.
• As a junior team member, your responsibilities are more limited than a full employee.
• There may be fewer senior data scientists available for mentorship at some companies.

Education requirements for data science apprenticeships

While requirements can vary by program, here are typical education prerequisites:

• Bachelor’s degree – Common majors include STEM fields like computer science, math, statistics, economics, or information systems.
• Coursework in programming, data structures and algorithms.
• Basic statistics and probability knowledge.
• Some programs accept candidates with associate degrees plus experience.
• Master’s degrees not required but can improve candidacy in competitive programs.

In addition to formal education, data science apprenticeships look for candidates with:

• Curiosity and willingness to learn.
• Critical thinking and problem-solving skills.
• Ability to pick up new technical tools and programming quickly.
• Attention to detail.
• Teamwork and communication abilities.

How to become a data science apprentice

Here are some tips for launching your data science apprenticeship:

1. Build your data skills

Before applying, develop basic proficiency in data analysis tools like Excel, SQL and Python. Take online courses on topics like statistics, programming and data visualization to stand out.

2. Understand the day-to-day work

Research what data scientists do on a daily basis through job postings, blogs and informational interviews. Understand the mix of technical skills and soft skills required.

3. Highlight relevant experience

Draw out course projects, internships and academic work that demonstrate your motivation and abilities in data analysis. Quantify your contributions and impact when possible.

4. Prepare for the application process

Many programs have a competitive, multi-step application process. Expect to complete coding tests, data analysis assessments, technical interviews and submission of a resume and transcripts.

5. Consider data science bootcamps

Enrolling in an intensive data science bootcamp can help develop your skills and make you a strong applicant for data science apprenticeships.

This table provides examples of established data science apprenticeship programs at top technology companies along with program length and location information.

Conclusion

Data science apprenticeships provide an exciting way to break into a fast-growing field while earning a salary. Apprentices work on impactful projects and develop in-demand skills like Python and machine learning under the guidance of experienced data scientists. While apprenticeships can be challenging, they offer hands-on experience that kickstarts your data science career.