Data Science: Good Career But Beware!
This article is all about data science. Although it has a rather negative title, it is actually an encouragement for people to join the ranks of data scientists. However, it makes a strong plea for them not to be arrogant but rather, humble servants of humanity with a vital mission, as we all lurch into the twenty-first century.
Before we begin, a few popular terms that need clarification:
What is data science?
Data science is a field of knowledge that uses scientific methods, statistical techniques, mathematics, algorithms, and computers to get knowledge from all sorts of data then apply that knowledge and make predictions using it. Other terms for data science are data mining and big data. Data science has been described as a sexed-up term for statistics. Some say the term is used too much and has lost its usefulness. A very good website for getting a feel for the vastness and scope of data science can be obtained following this link.
Data science, data analytics, machine learning, artificial intelligence
Data science is closely associated with data analytics, machine learning, and even artificial intelligence. Without giving a precise definition of these, the reason for this is that, currently, each of these three disciplines uses data science. In fact, data analytics and machine learning are forms of data science. Artificial intelligence is a bit more though, but we won’t go into that at this stage.
What are some applications of data science?
- Search engines, such as Google, DuckDuckGo and Bing
- Targeted advertising
- Optical character recognition
- Speech recognition
- Facial recognition
- Product placement in supermarkets
The list is almost endless.
Data scientists are those who practice data science. Data scientists need mathematics and statistics. They should be adept at performing scientific experiments. They should be competent computer programmers.
Why become a data scientist?
- There are strong financial incentives. The median base salary for a data scientist is more than $100K.
- At the present time, there is a great demand for data scientists.
- By becoming a data scientist, you become very important, as you run the numbers for all sorts of things. You are able to see what works and what does not and this may give you great power.
- By analyzing all sorts of data, you will get a glimmer of understanding about the world, beyond what the average person gets from mere reading and viewing, using the media.
- In some cases, what you do will be of extreme importance for humanity.
How do you become a data scientist?
In an earlier section, we said you need a background in mathematics. This should include lots of probability and statistics. You also need plenty of experience in using computers. If you can program in one language then you can easily switch to another, as some languages, such as R and Python are widely used.
How to become a data scientist when working in another job?
The previous paragraph is a useful guide to someone planning an undergraduate program, but what about someone working in another field? There are plenty of people who have switched to data science from other careers. Assuming they have a good background in mathematics and computing, they can become a data scientist in at least three different ways.
- Masters degrees and other programs offered by a number of very good universities, often online so you can study at home. These courses are usually very expensive.
- MOOC [massive open online courses]: these are specific and inexpensive, with costs often as low as $10. They may or may not be associated with a university. Some, which are not are EDX, Coursera, Udemy, and Udacity.
- Boot camps: these are intensive short courses, usually taught by professional data scientists. They can be very expensive, although not as expensive as the master’s programs.
Data scientists are usually in a team: Is this a problem?
Unless a company is small and only employs one data scientist, data scientists usually work in teams. Roles in this team could include people whose duties are in technical, interpersonal, business, or legal areas. A quote from a data scientist who works for a well-known accommodation company emphasizes this,” Every product team at our company includes designers, product managers, accountants, engineers and data scientists.”
A good team working together generally achieves far better results than a group of ‘stars’ that do not get on with each other.
Data scientists who are not team players are a serious problem
This statement applies to anyone on a team, not just data scientists. Anyone who becomes a data scientist could well become a menace when working in a team they do not contribute to as best as they should. They are at their most dangerous when they are unchecked and become the numbers guy or gal.
Anyone perceived and treated this way has to be very mature and exhibit high integrity.
Some data scientists who were not team players
- X was a very clever data scientist who could extract all sorts of information from the data his organization produced in vast quantities. Unfortunately, he thought that if he said something then it should be accepted without query. All he needed to do was run a simple hypothesis test to confirm his calculations. Instead, he took sensible questioning of his ideas personally and reacted almost vindictively.
- Y was also a brilliant data scientist. Sadly, he lacked the ability to explain his ideas in a way that laypeople understood. As a result, some of his findings were ignored and the company lost millions.
- Z was hired to extract information for a cosmetic company. She was the only person in her team who understood what was going on and sadly she abused the power this gave her. She made recommendations, which benefitted her but not her company.
In this short article, I hope I have convinced you to consider becoming a data scientist. The article has indicated the background and training that you need in order to become one. If you become a data scientist though, you will usually be part of a team. Human beings work best in teams and their greatest accomplishments were executed in teams. Data scientists who fail to realize this fact are a menace.