Best Data Science Bootcamps to Get You Skilled & Hired

The data science industry is expected to reach a market size of $322.9 billion by 2026, with job growth projected at 35% over the next eight years. The demand for skilled data science professionals is significant, with data scientists in the US earning an average salary of $135,000.

As data science evolves rapidly, obtaining the right education is crucial. Data science bootcamps offer intensive, focused training programs designed to equip participants with relevant industry skills quickly. With numerous options available, the key challenge lies in selecting the best one.

We’ve conducted extensive research to curate a list of this year’s top data science bootcamps, providing a solid foundation for those seeking to excel in the field.

Best Data Science Bootcamps

In the following section, we look at some of the best online data science bootcamps on the market, providing an in-depth review based on key criteria such as skills taught, price, features, and job placement support.

What’s the Benefit of Attending an Online Data Science Bootcamp?

Not only does a data science bootcamp cost significantly less than completing a Computer or Data Science degree; they can also launch your data science career in a matter of weeks, not years. Online data science courses are a great place to learn data science fundamentals, like data mining, data analysis, and visualization – and can accelerate your entry into the data science field by giving you access to real professionals in the industry. Prospective students will gain valuable skills like data wrangling and data cleaning, with one-on-one support from a career services team. Many data science bootcamps allow aspiring data scientists to work at their own pace, online or in-person, which is great if you’re working full-time.

Many data science bootcamp graduates learn data science part-time because they want to change careers or upskill. Data science bootcamps prepare students by focusing on practical skills, which means that the right data science bootcamp can give you a head start when you enter the workplace.

In short, these online programs are not only a great way to learn data science but also to gain real exposure to the field.

Making the Most of a Data Science Bootcamp

Embarking on a data science training program can be a transformative experience. This section will guide you through how bootcamps work, what to expect, and a glimpse into the typical schedule. Ask yourself these questions to get the most out of your bootcamp:

How Do Data Science Bootcamps Work?

Data science bootcamps usually include a structured curriculum with lectures, hands-on exercises, and real-world projects, honing theoretical understanding and practical skills. You’ll learn how to visualize data, explore the fundamentals of software engineering and analytics, and more.

With the guidance of experienced instructors, students learn to navigate complex tools and frameworks. 

Moreover, these online bootcamps often involve collaborative learning, where students work in teams to solve problems, mirroring the dynamics of an actual workplace.

What Will the Schedule Look Like?

Some bootcamps offer full-time schedules requiring around 40 hours a week, while others might offer part-time schedules that allow for work or other commitments. Regardless of the format, students should expect considerable self-study and project work outside of class hours to consolidate their learning.

What Should You Expect To Learn in a Data Science Bootcamp?

Attending a data science course usually comes during the “how” phase of your career journey – meaning it’s often the answer to the question “how can I land a data science job”. With that in mind, it makes sense that a course or bootcamp should be focused on preparing you for your first job.

So, we went ahead and did a bit of analysis with the help of ChatGPT to better understand what hiring managers are looking for in their entry-level applicants. We analyzed job titles like “Data Scientist I” and “Jr Data Scientist” from companies like EOS, INTEL, and Apollo and learned that the following tools, skills, and qualifications were most common in job listings:

Technical Skills and Tools:

• Programming Languages: Proficiency in Python and R for data manipulation, analysis, and machine learning. SQL for database management and queries.
• Data Analysis Tools: Experience with data visualization tools such as Power BI, Tableau, and R Shiny for creating dashboards and reports.
• Machine Learning/Deep Learning Frameworks: Knowledge of TensorFlow, PyTorch, Scikit-learn, Keras for building and deploying machine learning models.
• Big Data Technologies: Introductory knowledge of Spark or PySpark, and willingness to learn more. Experience with big data platforms like Hadoop and Spark is a plus.
• Cloud Platforms: Experience with Azure (Azure Data Factory, Azure SQL Database/Warehouse, Azure Databricks) for data pipeline development and deployment. Knowledge of cloud services for data storage, processing, and analytics.
• Statistical Analysis: Strong foundation in statistical analysis, hypothesis testing, and modeling techniques.
• Data Engineering: Skills in data cleaning, transformation, and preparation for analysis. Experience with ETL processes and data pipeline development.

• Communication: Effective communication skills to articulate technical information to non-technical stakeholders and collaborate with cross-functional teams.
• Problem-Solving: Ability to independently pursue and develop solutions to complex problems. Creative problem-solving skills.
• Teamwork: Ability to work well in a fast-paced, team-oriented environment. Collaborative spirit and willingness to support team members.
• Adaptability: Strong comfort with working in a dynamic environment and adapting to new technologies and methodologies.

Additional Skills:

• Version Control: Familiarity with version control tools like Git.
• Agile Methodology: Understanding of Agile development methodologies and project management.
• Statistical Programming: Experience with statistical programming and analysis.
• Machine Learning Techniques: Knowledge of machine learning techniques and algorithms.

Wondering how to use this list? Our advice is to cross-reference with any bootcamp or course to ensure you’re getting the skills needed to land a job – you shouldn’t expect to check everything on the list, but make sure aligns with the jobs you’re after or where your skills are weak.

What Kind of Job Can You Get After an Online Data Science Bootcamp?

After completing an online data science bootcamp, various job opportunities in the field of data science open up. 

You could start as a data analyst, using statistical tools to interpret data sets and generate business insights. With more experience and specialization, you could progress into more senior roles where you’ll be developing predictive models and machine learning algorithms. Other potential roles include data engineer, software engineer, or roles in business intelligence.

FAQs About Data Science Bootcamps

We answer your most frequently asked questions.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!