Difference between a Data Analyst and a Data Scientist

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Both data analytics and data science involve the study of historical statistics. So what is the difference between data analyst and data scientist roles?

Data Analyst Duties

A data analyst usually observes trends relevant to immediate decision making. It’s typically related to tracking recent purchase histories that may predict a customer’s future needs.

The data collected and analyzed also enables advertisers to plan promotional campaigns. This objective usually includes some market research and demographic information.

The demographic information that a data analyst gathers helps companies learn as much as possible about customers. It also shows them new people to target who may have the same interests as their current customers.

Other business uses include economic forecasting and assessing economic risks of an operation. However, data analysts also might spend time looking at numeric activity to help prevent online fraud.

Examples of an analyst duties in other fields include as follows: monitoring how a medication helps a patient, viewing sports statistics of a player in a current season, or collecting lab results for patients.

Data Scientist Duties

Data scientists usually observe trends intended for long-term predictions. This serves well in a variety of industries, including business or medical.

In business, a data scientist might use project team activity records to streamline workflows. In the process, they might notice redundant application steps.

For instance, they may have discovered that a user can start collaboration software in one step instead of two. It’s nuances like this that are studied over time. Then, they’re detected and changed.

A data scientist typically strives to improve productivity. As a result, they show businesses how they can reduce time spent with one customer to free up time for another. This enables a company to serve more people within a period, which can increase revenue.

In business, a data scientist also might observe long-term economic trends. Some of these patterns are predictable, such as higher sales during seasonal periods. Others might be less predictable, such as an upcoming recession.

Even if the data scientist can’t always see into the future, they can plan for it based on patterns. If they notice signs of trouble based on past data, they can prepare the public.

Data analytics in science typically returns different results than it does for business evaluations. In science, a data scientist would study patient health histories over months, years or decades. They might also evaluate clinical trial results to determine treatment effectiveness. This involves saving lives instead of saving money.

The data scientist also might study financial aspects of the healthcare industry. This enables medical centers to provide quality care, run efficiently and operate within their budget.

A Data Scientist vs Data Analyst Skills Required

Remember, both data scientists and data analysts don’t just manage business promotional campaigns or contribute to medical advancements.

Some scientists or analysts might work in supply chain management. Otherwise, they perhaps study workplace accident statistics to improve occupational safety. Alternatively, they might work as an engineer, architect, construction lead or project manager.

Regardless of the job role, a data scientist vs a data analyst has specific skill sets. This might include learning programming languages.

Programming languages that a data scientist has include Java and Python. They also typically have advanced training in creation of and interpretation of mathematical algorithms.

Skills that a data analyst has include data visualization, statistics and communications. A person in this position typically fulfills a front-end role as opposed to a data scientist. They usually become proficient in Excel, SQL and BI tools.

Note that a data analyst and a data science specialist often have the same skills. For that reason, you may not notice the crossover from a data analyst to data scientist. You could benefit from a person who has experience serving in either of these roles.

The Process of a Data Scientist vs Data Analyst

A data scientist starts with curiosity. They seek out answers to unanswered questions, or they explore new ways of approaching unsolved problems.

Data scientists collect any information that helps them come to conclusions. In the process, they take extensive measures to verify that the figures they gather are correct.

Data scientists also make sure apps provide customers with positive online user experiences. This happens after a period of testing, which works out any “bugs” that website software might have.

Other responsibilities of data scientists include designing the algorithms that make apps work. They also provide frameworks that turn backend data sets into visualizations customers understand.

A data analyst may work alongside a data scientist to continue the data visualization. Although some analysts may work on the backend interpreting statistics, they often don’t have to.

The responsibility of presenting data in a form the public understands rests on the analyst, however. They must have an understanding of the information a data scientist collects if they want to turn it into a form that customers understand.

How do data scientists and data analysts solve problems?

When seeking data science consultancy, it’s best to think of how you want to use statistics in practical terms. Does it help in the “real world?”

This extended list of examples shows how a data scientist or a data analyst uses numerical information. It presents itself in real-world applications to solve both consumer and business problems.

Supply and Demand Forecasting

Data science provides supply and demand forecasting. This discipline benefits any industry, including retail, manufacturing, medical and foodservice. It shows a business (or non-profit organization) the top human needs that people have and how that business or organization can help them.

Transportation Route Optimization

Transportation remains a hot topic. It calls for statistical knowledge that both a data scientist and a data analyst provides for making mobility improvements. Evaluating numerical records can influence reform in both public and private sectors.

Vehicle Failure Diagnosis

New ways of identifying problems with vehicles not operating at their best happens when data scientists and data analysts work together to interpret tech data. In this case, the objective is diagnostics of vehicle problems in as little time as possible.

Retail Product Recommendations

Stores selling products or services online can keep track of what you searched. They also have records of your past purchases. This is how they process the data that triggers automatic recommendations. Streaming services or e-commerce websites are the best example of this, with their placement of “related videos” or “related products.”

Environmental sustainability and related industries also tap into the benefits of data science. They also capitalize on how a data analyst can extract the information they can use for promoting their services. Any business or organization can use skills of a data science researcher from time to time.