What’s the difference between these 3 roles?

Careers in data science have been listed as the hottest topics in IT in recent years. A recent survey from LinkedIn called “2020 Emerging Jobs Report” listed Data Scientist as one of the top-15 emerging jobs in the UK. It’s no different anywhere else in the world as the profession is getting incredibly popular. Businesses are demanding data scientists more than ever to help them understand their growing databases and uncover useful intelligence. 

A search for the term “data analyst” on the website Indeed.ie returned 1074 positions in Ireland when this article was being written. The same search for “data scientist” shows 323 jobs in Ireland, which demonstrate the demand for data professionals in general and how this can be a great skill to master in 2020 and beyond.

The definition of data science can be defined as the field of study which combines business, technology, statistics, and communication into one role to gain knowledge and insights from big data (i.e., structured and unstructured data). The field uses the scientific method alongside algorithms and probability to organize and analyse information.  

Even though the three careers are related and similar, there are substantial differences between them. As data professionals are getting popular and in need, it’s important to understand the differences and special requirements for each one.

Data Science Goals

With the immeasurable amount of data available nowadays, Data Science is becoming an essential presence to help businesses translate these data into new potential revenue streams. Their main goal is to clean up and analyse large amounts of big data. Using software designed specifically for big data and analytics, data scientists must be able to extract insights and present their findings in a way that business leaders and other stakeholders can understand. 

Data scientists are required to deliver data-based deliverables, such as pattern detection analysis, optimization algorithms, prediction engines, etc. 

Fields Within Data Science

Data Scientist

What does a Data Scientist do?

Besides the similarities between the professions, data scientists combine business knowledge, data analysis, and communications to help companies find and extract knowledge from big data. Once they present their findings to business leaders and stakeholders, the insights are used to make decisions about moving the organization forward. 

Skills:

Data scientists should have some programming skills for data science-specific languages (such as Python, R, SAS, etc.), a background in statistics and mathematics, the ability to tell stories using data visualization and knowledge of Hadoop, SQL and machine learning. 

Data Engineer

What does a Data Engineer do?

Before the Data Scientist analyse and put their hands into data, data engineers are the ones who will develop, deploy, optimize and manage data pipelines and infrastructure. This role is required to manage the influx of rapidly changing data and prepare information to be sent to data scientists.

Skills:

Data engineers need skills in programming, such as Java and Scala. They must also have a background in NoSQL databases and frameworks such as Apache Hadoop.

Data Analyst

What does a Data Analyst do?

Data analysts and data scientists are often mistaken for one another. Data analysts, however, often act as the middleman between data scientists and business analysts.  

Analysts sift through the data to find the answers that align with high-level business strategy, rather than generating the questions themselves. Another difference is that data analysts don’t usually have a computer programming background, and they aren’t typically involved with machine learning or statistical modelling. 

Skills:

While data analysts still need programming skills in Python, S and R, they don’t need as advanced programming skills as data scientists. They also need to be able to map out and visualize data to make it easy to understand, and they still need statistical and mathematical skills.  

Common Data Science Tools

We listed below a couple of tools that are essential for data professionals to master their daily challenges. Usually, it’s enough to have good knowledge about them and you can easily learn the rest with time if you need to.

Programming Languages: 

Data scientists and analysts must know a handful of programming languages, including R, Python, Scala, Julia, SQL, Java, etc. You don’t have to be an expert in all these languages, but many are helpful for breaking down data. 

Data Modelling and Visualization Tools: 

Tools include Scikit-learn, Pandas, TensorFlow, Numpy, e1071, Mat plotlib, Shiny, D3,and ggplot2. These pre-existing packages and libraries all help with the statistics, mathematics, data visualization, algorithms and modelling needed to organize data.

Database Tools: 

Data engineers also have backgrounds in NoSQL databases such as MongoDB and Cassandra DB. Scientists and analysts must be able to access and query data, so they also need to be able to use NoSQL, NewSQL and relational database management systems (i.e., MySQL, Redshift, Hadoop, HBase, etc.)

Big Data Tools: 

Hadoop, Spark, Pig, Drill, Hive, Presto and other big data technologies are used to analyse data and provide a framework for processing and distributing big data. 

Data scientist’s Typical Job Duties

A data scientist’s typical routine may include the topics below:

• Researching and developing statistical learning models for data analysis
• Collaborating with operations, business teams and engineering departments to understand company needs and devise solutions
• Optimizing joint development efforts through database use and project design
• Communicating results and ideas to key decision-makers
• Implementing new statistical methodologies as needed for specific models or analysis

*The above article was based on the content provided by New Horizons. 

**The 2020 Emerging Jobs Report by LinkedIn can be accessed by clicking here.

How to Master Data Science

The path to working with data might sound difficult, but there are plenty of ways to become an expert. The rising opportunities and need for these types of professionals, it will make the investment worth and a good choice in the short and long term.

The good news is that you don’t need a master’s degree, though many data scientists do have one. And if you are looking to specialize in a specific field, such as healthcare or science, you should also dive into courses and training in those industries.

Data science certification courses will help you develop your skills and earn high-paying roles as you grow your expertise and build up your career. Click below to see our list of Data Science courses.

Looking to build your career in data science but unsure where to begin? Talk to one of our Account Managers today, they are ready to help you with the best career advice to your career and help you thought the best option available.