What is Data Science?
Data science is a multidisciplinary field that combines computer science and statistics. With so much data being produced every day, data science makes it possible to draw useful insights from datasets that may be too large for traditional statistics to analyze. This data is often called "big data,” and can include anything from analyzing complex genomic structures to interpreting handwriting to optimizing a marketing strategy. And data science is not limited to just numbers! Data science finds patterns in all unstructured sets, including text, images, and audio files.
How to Learn Data Science
The best data science course for you depends on your learning style, career goals, availability, etc. Here's our advice for choosing the right data bootcamp and what you can expect:
- Examine the curriculum – Does the bootcamp teach the most modern data science tools, programming languages, and skills?
- Ask About Projects – At a good data science bootcamp, you'll build at least 2-3 projects using real-world data sets!
- Meet the Instructors – Do the bootcamp instructors have qualifications like quantitative degrees, master’s degrees, or even PhDs? This is much more common in the world of data science bootcamps.
- Ask About Outcomes – Ask the school for their CIRR report or job outcomes report, read data science bootcamp alumni reviews, and even reach out to alumni on LinkedIn to ask questions.
Data Science bootcamps can cost between $10,000 and $20,000. However, many data science bootcamps offer scholarships and financing options to make tuition affordable. There are also fellowships and free data science bootcamps, such as Insight Data Science.
Types of Data Science Jobs
There are a wide variety of data science jobs, and if there's one thing that we've learned after interviewing so many data science bootcamp graduates, it's that no two companies use the same job titles. Common job responsibilities of data scientists include data engineering, machine learning, natural language processing (NLP), data cleaning, and data analysis.
The median yearly salary for a Data Scientist is $108,020. Here is a list of the most frequent data science job titles for bootcampers:
- Data Engineer
- Machine Learning Engineer
- Data Scientist/Software Engineer
- Big Data Analytics Consultant
- Decision Scientist
- Data/Business Analyst
- Data Journalist
- Financial Analyst
- Product Analyst
- Database Administrator
What Does a Data Scientist Do?
In general, a Data Scientist analyzes, organizes, and interprets complex digital data. A Data Scientist’s responsibilities will vary, depending on the type and size of company they work for. For example, a Data Scientist at a startup may be responsible for data engineering as well as data cleaning. A Data Scientist at a larger company, such as a consulting firm, may be directly working with clients to build dashboards for their teams or they may be devising models to better predict trends for shareholders.
What Kind of Skills Do Data Scientists Need?
While math and coding skills are an essential part of data science, business intelligence and a scientific approach are key to making sense of data. Most data science students have a previous background in STEM, but overall, data science students and professionals are detail-oriented and excel at problem-solving, logical reasoning, and communication.
Hard Skills for Data Scientists
Here is a quick list of data science skills and technologies used in the field and often taught at data science bootcamps:
Soft Skills Needed by Data Scientists
Data scientists need more than tech skills; they need creativity, foresight, and the ability to communicate their findings to a variety of stakeholders. Other soft skills needed by Data Scientists include:
- Curiosity
- Critical thinking
- Flexibility
- Problem-solving
- Emotional intelligence
- Time management
Job Market and Salary Insights
The job market outlook for Data Scientists is very positive, with a projected 35% job growth from 2022 to 2032, significantly higher than the average for all occupations. This translates to approximately 17,700 openings for data scientists over the next decade. Data Scientists can expect a $108,020 median salary as of May 2023. Other factors that may affect the salary earned by a Data Scientist include:
- Location: Those in larger cities or “tech hubs” may earn more.
- Experience: The more years you have in your career, the more you'll earn. An entry-level data scientist can earn an average of $86,906, while someone at a senior level may earn, on average, $126,631.
- Education: Data Scientists with advanced degrees are likely to earn higher salaries.
FAQ Section
How do you start a career in data science?
Many Data Scientists start their careers in academia. If you have a bachelor's degree or PhD in a quantitative field, you're already close to a data science career. A data science fellowship like Insight Data Science would be a fantastic option.
Most data science bootcamps require students to have a degree in STEM and an aptitude for math and statistics. Then, you’ll be ready to apply to data science bootcamps, typically intensive 3- to 6-month programs. Data science bootcamps prepare graduates for entry-level and junior data science jobs, such as Data Analyst, Data Scientist, Machine Learning Engineer, and Data Engineer.
Keep in mind that a data science bootcamp is not a traditional coding bootcamp. In addition to learning programming languages, such as Python and R, data science bootcamps teach students how to extract valuable knowledge from large datasets using analytical and programming skills.
Is data science a good career?
According to US News and World Report, data science ranks number four on the list of the best tech jobs, largely due to the higher salaries and low unemployment rates. It also offers opportunities for career growth, work-life balance, and a low-to-moderate stress level.
Why is data science a growing field?
With everyone from businesses to non-profits to the government collecting data, there is a high demand for turning that data into smart business decisions. With the increase in AI and machine learning, companies will need people who can collect, analyze and interpret data, and turn it into profitable solutions.
