Some data science careers or job titles are: data scientist, data analyst, business intelligence analyst (BI), project manager, database administrator (DBA), data engineer and machine learning engineer. Actually, there are many more job titles in the data science field and many jobs overlap.
Have a look at our post called Top Skills for Data Professionals?
Learn with YouTube
There is a good video by Luke Barousse called Top Jobs in Data Science.
Here’s a video by Alex the Analyst called Top 8 Mistakes Beginner Data Analyst’s Make | CareerFoundry Webinar. I will mention the 8 mistakes here, but turn them around to positives. Learn the basics really well. Ask questions. Learn the business context (business domain). Clean your data properly. Plan well before building. Learn domain (industry) knowledge & specialize. Document your work well (for others and you). Keep in touch with others (don’t isolate).
ChatGPT for Data Science & Machine Learning: 5 Use Cases is a video by Tina Huang.
The Harsh Reality of Being a Data Analyst. Since no job is perfect, here are three things to be aware of. The salary is lower than technical roles, in general. Technical roles include software engineer, data engineer or data scientist. Secondly, the barrier to entry is low. It’s not too hard to become a data analyst and therefore it’s very competitive. Getting your first job may be tough. Thirdly, many people see being a data analyst as just a stepping stone to something else. Therefore many people leave data analyst jobs for other jobs. But that’s okay. If data analytics is your long-term career choice, don’t be discouraged if you see other come and go. There may not be much career growth beyond “senior data analyst”. Finally, the forth thing is that among the data science job family, the data analyst is considered less prestigious.
Ken Jee has a video at YouTube called Is Data Science A Good Career?. At time 2:40 he describes the type of person who might enjoy a career in data science.
- Are you comfortable with ambiguity?
- Can you work alone and in a team?
- Do you like learning new things every day?
The third one, according to Ken, is probably the most important one.
Why ambiguity? Many data science roles at organizations are poorly defined. You may need to be creative. You may need to find creative ways to find value for your team. You will need to be able to think creatively to find answers to questions, and also come up with new questions nobody else on the team has even asked yet. It’s not like school, where they give you specific work to do. You will need to communicate your findings and questions to others on the team, some of which may not be very technical in their knowledge. In other words, can you teach?
According to EMC Education Services book Data Science and Big Data Analytics on page 21, data scientists are generally thought to have five sets of skills and behaviorial characteristics.
- Quantitative skills – math and statistics
- Technical aptitude – software engineering, programming (Python, R or others), machine learning
- Critical thinking and a skeptical mindset – not bias and very detailed oriented
- Curious and creative – finding creative ways to solve problems
- Communicative and collaborative – able to articulate business objectives, work in teams