1. How can I prepare for a career in data science?
Most data science or business analytics programs will require the following:
- A high level of quantitative ability
- A problem-solving mindset
- Programming proficiency
- The ability to communicate effectively
- Ability to work in a team
Hence if you’re deciding which bachelor’s degree to take, you may start by pursuing quantitative discipline such as science, technology, engineering, mathematics, business or economics in preparation for a data science career. Alternatively, if you’re already working and are looking for a career switch, you may wish to build strong foundational knowledge in these areas.
2. Am I patient and determined enough to continue working on projects even after hitting a roadblock?
Data science projects can be demanding and take months to complete depending on the scale of the problem. Therefore, as a practicing data scientist, hitting roadblocks is inevitable. This is why patience, tenacity, and perseverance are key qualities essential for a successful data science career.
3. Do I have the business acumen to draw meaningful insights to support data-driven decisions for my company?
Data science is a very practical field. You can be the proficient in building models. However, as a data scientist, real-world application is what matters. Therefore, your role as a data scientist or analyst should also be able to make sense of the data obtained from the models your built and translate it to meaningful insights that can support your organisation in the high level decision making process.
4. Am I a lifelong learner?
Data science is a dynamic and ever-changing environment. So, be prepared to embrace and learn new technologies. One way to keep yourself abreast with the latest technology and methodology is to network with other data scientists and analysts. A simple way you can do this is by staying active on community platforms such as Github and be open to learn new things.
5. Am I fair and ethical?
Ethics and privacy considerations are essential in the field of data science. Therefore, you need to understand and be honest about the up and downstream implication of your project. Additionally, it is important that you do not intentionally manipulate your data to produce skewed data to mislead or manipulate your audience. Although the frustration from roadblocks may tempt you to perform otherwise, you need to stay ethical in all phases of your job – from data collection and analysis to model building, analysis, testing, and application – and interpret the findings from your data science project.