There are three education options that you will need to look at when considering a career in data science.
- Graduate certificates and degrees provide recognized academic qualifications, networking, internships, and structure for your resume. This will end up costing you a lot of money and time.
- Self-guided courses and MOOCs are cheap or free, targeted, and short. They will let you complete your projects within your own timeframe, but they will require you to structure your own career path.
- Bootcamps are a lot faster and more intense than traditional degrees. They may even be taught by data scientists, but they will not provide you with a degree that has initials after your name.
Academic qualifications are probably more important than you think. It’s very rare for a person that doesn’t have an advanced quantitative degree to have the skills that a data scientist needs.
Burtch Works, in its salary report, found that 46% of data scientists have a PhD and 88% have a master’s degree. For the most part, these degrees are in rigorous scientific, quantitative, or technical subjects, which includes statistics and math – 32%, engineering – 16%, and computer science – 19%.
Many companies are desperate to find candidates that have real-world skills. If you have the technical knowledge, it could trump the preferred degree requirements.
What skills are you going to need to be a data scientist?
1) Technical skills:
- Cloud tools such as Amazon S3.
- Big data platforms such as Hive & Pig, and Hadoop.
- Python, Perl, Java, C/C++
- SQL databases, as well as database querying languages.
- Unstructured data techniques.
- Data visualization and reporting techniques.
- Data munging and cleaning.
- Software engineering skills
- Machine learning techniques and tools.
This list is always changing as data science changes.
2) Business Skills:
- Industry knowledge: It’s important to understand how your chosen industry works and how the data is utilized, collected, and analyzed.
- Intellectual curiosity: Data Scientists have to explore new territories and find unusual and creative ways to solve problems.
- Effective communication: Data Scientists have to explain their discoveries and techniques to non-technical and technical audiences in a way that they can understand.
- Analytic problem-solving: Data Scientists approach high-level challenges with clear eyes on what is important. They employ the right methods and approaches to create the best use of human resources and time.
