difference between a data analyst and a data scientist

The difference between a data analyst and a data scientist

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Data, it’s frequently claimed, is the new gold. Just like gold, which needs to be fashioned into coins or jewellery to have real value, data needs to manipulated or ‘mined’ to get the most out of it. These days all companies are able to collect vast quantities of data, which need to be processed properly to ensure key insights aren’t missed and advantage is taken of new business opportunities.Data analysts and data scientists are the individuals who do this data mining, but what exactly is the difference between a data analyst and a data scientist? Read on to find out this, as well as the demand and salaries for each of these occupations.

Data analysts: What are they all about?

Data analysts, in essence, translate numbers into everyday English. Every company collects data – e.g. sales figures, market research, transportation costs, logistics, etc. The data analyst needs to take this data and translate it into English to help companies make better business decisions. For example, a company could take its Google Analytics data, from website viewings, and make decisions about which products need to be pushed and which products should be culled. Basically, a company’s data is converted by the data analyst into trends, future growth opportunities and solutions.

What skills and abilities do you need as a data analyst? You need to be able to communicate well, be organised and pay attention to detail. Once you’ve understood the data, you’ll have to communicate it via clear visual, written and verbal communication. One key difference between a data analyst and a data scientist is that an analyst doesn’t really require coding skills in his or her day-to-day activities, but it certainly won’t harm to have them.

Data analysts: Job demand and salaries

Forbes reports on a recent IBM report that found that data analytics (and data science) jobs remain open an average of 45 days, which is five days longer than the market average. This indicates a high demand for these jobs, as there are not many candidates who are able to fill them. By 2020, there will be an increase of 364 000 openings for data professionals in the US to 2,720 000. What industries see the highest demand? Finance, Insurance, Professional Services and IT industries experience 59% of this demand.

And what’s the salary? Glassdoor reports that the annual salary for a data analyst in the US is $65,470 per annum. High salaries are in the region of $92,000 per year.

I want to be a data scientist – What can I expect?

Before we even mention what data scientists do, it’s worth mentioning that The Harvard Business Review calls Data Science the ‘sexiest job of the 21st century’. And Glassdoor ranked data scientist as its top job in the US based on an analysis of three factors: number of job openings, job satisfaction and salary.

In essence, a data scientist analyses data for actionable insights on anything from product development to customer retention to new business opportunities. Similar to data analysts, data scientists process and analyse data, but they do need to possess coding skills. The languages they are likely to know include the following: Python, R, Java and SQL.

As a data scientist, according to a piece on Medium, you’ll have the following responsibilities:

  1. Frame the problem: You need to work out how to translate the client’s issue into a concrete and well-defined problem – e.g. which products are 20-something customers likely to buy?
  2. Collect the raw data to solve the problem: You will need to work out what data points are actually worth collecting to solve the problem.
  3. Process the data: Raw data frequently needs to be cleaned for various problems including errors, missing variables, corruption in the data, etc.
  4. Explore the data: What are the patterns, trends or correlations?
  5. Conduct in-depth analysis: Here you will use machine learning, coding, statistical models and algorithms.
  6. Communicate the results: Here’s where you tell the story of your data, otherwise known as data storytelling, to the various stakeholders. You should now have an accurate machine learning model that can predict the answer to the initial problem.

Data scientists: Job demand and salaries

From January 2015 to January 2018 there was a 75% increase in job postings for data scientists, reports Indeed. Interestingly, there is a growing speciality for a job known as ‘sentiment analysis’, states Bloomberg – basically, this is finding a way to quantify the number of tweets supporting or trashing your company.

The average salary for a data scientist, says Glassdoor, is $120,931 in the US. Top salaries are around the $158,000 mark. If you want to become a data scientist in 2019, check this out for some tips.

In closing, we’ve covered the difference between a data analyst and a data scientist. Both options are excellent careers in terms of job demand, salary and overall satisfaction. If you want to learn data science in 2019 and enter this lucrative and stimulating field, consider signing up for HyperionDev’s online Data Science Bootcamp. You can also trial this course for free.