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 which may be too large for traditional statistics to analyze. This data is often referred to as “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 Become a Data Scientist

Many Data Scientists start their careers in academia – if you have a bachelor’s degree or PhD in a quantitative field, then you’re already close to a data science career. A data science fellowship like The Data Incubator or Insight Data Science would be fantastic options. 

Most data science bootcamps require students to have a degree in STEM and an aptitude for math and statistics. If you don’t have a quantitative degree, then you should sharpen your math skills like linear algebra, calculus, statistics, and probability. Then, you’ll be ready to apply to a data science bootcamp. 

Master’s in Data Science vs Data Science Bootcamps

While a Master’s in Computer Science with a specialization in Data Science typically takes 3 years to complete, data science bootcamps are typically intensive 3- to 6-month programs. Data science bootcamps often offer career assistance, and prepare graduates for entry-level and junior data science jobs, such as Data Analyst, Data Scientist, and Data Engineer.

A data science bootcamp is not a traditional coding bootcamp. In addition to learning programming languages, such as Python and/or R, data science bootcamps teach students how to extract valuable knowledge from large datasets using analytical and programming skills. 

How to Choose a Data Science Bootcamp

The best data science course for you depends on your own learning style, career goals, availability, etc. Here's our advice for choosing the data bootcamp:

1. Narrow down your options – you can rule out some bootcamps based on location, cost, and time commitment.
2. Examine the curriculum – does the bootcamp teach the most modern data science languages (Python or occasionally R), tools (Hadoop, Spark, Tableau etc), and topics (machine learning, data engineering, etc).
3. Ask About Projects – communication is so important in data science, and projects are a great way to walk a hiring manager through your thought process and experience. At a good data science bootcamp, you'll build at least 2-3 projects using real-world data sets!
4. Meet the Instructors – do the bootcamp instructors have qualifications like quantitative degrees, masters degrees, or even PhDs? This is much more common in the world of data science bootcamps. Pro-Tip: Take an intro course or free workshop hosted by the bootcamp to really understand the teaching style.
5. Ask About Outcomes – do past bootcamp graduates get jobs in data? Ask the school directly 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.

Red Flags – instructors don't have enough experience or qualifications, projects don't use real-world datasets, or the bootcamp can't tell you about past student career success. 

Are Data Science Bootcamps Worth It?

 “I would definitely do it again in a heartbeat. It was definitely worth the money. The learning environment, having the type of classmates that you can learn from, the instructors — you definitely couldn’t have gotten it just by learning on your own.” – Itelina, graduate of Metis

“I can say that, for me, 100% it was worth it – but I will say the boot camp is also about the work you’re putting in and the goals that you have. I really wanted to sponge off of the people around me – not just the teachers, but also all these PhD students around me. It was the most intelligent group of people I’ve been around in such a small space before. I’ve never had such an amazing opportunity.” – Sumanth, graduate of NYC Data Science Academy 

“I definitely think I needed Galvanize. Because I wanted to break into the tech industry, having Galvanize on my resume along with the skills I learned there, bolstered my ability to get a job.” – Rosie, graduate of Galvanize

“For me, It was totally worth it. I would do it all over again. IE in particular taught me a lot more than how to use Python and SQL. It teaches the technical skills, but at the same time it gives you access to professors that are going to make the process a lot less frustrating and more fun so that you have the motivation to keep going. Plus all of the extras. You get to network with companies and those companies come to give talks. I made a lot of friends in this bootcamp, too.” – Inna, graduate of IE Data Science Bootcamp

How much does a data science bootcamp cost? 

Full-time, immersive data science bootcamps typically cost between $10,000 and $20,000. 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 and The Data Incubator.

Data Science Jobs

With everyone from businesses to non-profits to the government collecting data, there is high demand for turning that data into smart business decisions. There is 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 exact same job titles. Common job responsibilities of data scientists include data engineering, data cleaning, and data analysis. Here is a list of the most frequent data science job titles for bootcampers:

• Junior Data Scientist
• Senior Data Scientist
• Data Engineer
• Machine Learning Engineer
• Data Scientist/Software Engineer
• Big Data Analytics Consultant
• Data Science Instructor
• Decision Scientist
• Data Analyst
• Business Analyst
• Business Intelligence Analyst
• Data Journalist
• Financial Analyst
• Product Analyst
• Database Administrator

Data Science Salaries

Data Science Skills

According to Josh Wills, Director of NYC Data Science Academy, a data scientist is a “person who is better at statistics than any software engineer and better at software engineering than any statistician.” While math skills 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.

Here is a quick list of data science skills and technologies used in the field and often taught at data science bootcamps:

• SQL - SQL stands for Structured Query Language. SQL is designed for managing data in relational database management systems. SQL extracts data for data analytics and reporting. 
• Hadoop - Hadoop is a suite of technologies used to manage data and execute programs in a cluster. Hadoop includes a file system designed for large datasets, the MapReduce system which allows for running programs in parallel, the SQL-like Hive database for querying data in a cluster, and other components.
• Spark - Spark is a system for writing parallel programs to run in clusters. Spark has a powerful machine learning library, mllib, and can be used with R. 
• Python and R - Both languages are equally important in data science! Python is used for computer science components of a data scientist’s job. R is used for building statistics packages and visualizations. Combining both Python and R greatly increases their range of capabilities.
• Machine Learning - Machine learning is a set of algorithms that is put in place to analyze large sets of data. Many of these algorithms are built so that the machine learns to feed better data into later models, and ultimately make predictions about future events. 
• Natural Language Processing - Natural Language Processing (NLP) is a method for pre-processing text to turn it into numerical data. NLP is also known as computational linguistics.
• Data science math includes linear algebra, calculus, statistics, and probability.