What’s the difference between a Data Scientist and a Data Analyst?

What is a data scientist and a data analyst?

Data scientists are problem solvers. They figure out which questions need answers, and then come up with different approaches to try and solve the problem using statistical methods, data visualisation techniques, and machine learning algorithms to build predictive models and solve complex business problems. 

In contrast, a data analyst collects, cleans, and interprets data sets, uncovering trends in order to answer a given question or solve a problem for a business. They transform masses of business data into actionable insights that the business can act on. 

Roles and responsibilities

Data Scientist
Data scientists are expected to make sense out of massive amounts of data, combining business understanding, data analysis, programming and data visualisation to drive better business results. Some of their day-to-day tasks and responsibilities include:

• Performing ad-hoc data mining and gathering large sets of data from several sources
• Using a range of statistical methods, data visualization techniques to design and evaluate advanced statistical models from vast volumes of data
• Building AI models using various algorithms and in-built libraries
• Automating tedious tasks and generating insights using machine learning models

Data Analyst
Data analysts spend a large amount of time researching, sourcing, and analysing data to provide insights into particular areas of interest to a business. Some of their day-to-day tasks and responsibilities include:

• Liaising with key business stakeholders to establish information needs
• Gathering data from various sources, filtering and scrubbing it
• Identifying trends and patterns in complex data sets
• Creating and maintaining different reports with the help of charts and graphs using tools including Excel and BI
• Analysing results and reporting the findings back to key stakeholders
• Identifying and proposing new data collection and analysis processes and techniques

Skills requirements

The fundamental skills and knowledge that data analysts and data scientists need to possess are quite similar, however there are a number of key differences between these roles.

Data scientists possess key skills including:

• An in-depth understanding of data structure and data manipulation
• A strong foundation of calculus, linear algebra, statistics and probability including linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipeline
• Thorough understanding of Python, SQL, R, SAS, MATLAB, Spark
• Data visualisation using Power BI and Storytelling using Tableau 
• Data wrangling and data modelling
• Machine learning and cloud computing
• Analytical thinking and decision making skills

Data analysts have key skills including:

• Understanding of significant statistical concepts including measures of central tendency, dispersion, correlation, and regression
• Analytical skills, intellectual curiosity, and reporting accuracy
• SQL/CQL, R, and Python experience
• Use of data visualisation tools including MS Excel and Tableau 
• Understanding of linear and non-linear regression models and classification techniques for data analysis
• Knowledge of agile development methodologies
• Communication and stakeholder management skills

Job outlook

There are many similarities between data scientists and data analysts, so you may be wondering how the two paths stack up when it comes to remuneration.

According to SEEK, the average annual salary for a data scientist in Australia is from $115,000 to $135,000 annually. These figures are impacted by an individual’s work experience, education and qualifications, as well as the hiring company and the industry which they are applying for a role in. 

The average annual salary for a data analyst in Australia ranges from $85,000 to $105,000, with more qualified people in senior positions earning up to $125,000. Again, this is dependent on the skill set and experience of the candidate, the hiring company, and the industry.

Want to build a career as a data scientist or data analyst?

If you want to build a career as a data scientist or data analyst, then developing the key skills and experience you’ll need to succeed through an online course is the perfect step towards achieving your career goals.

The Data Scientist Bootcamp Program can help you to get hands-on experience with technologies such as R, Python, Machine Learning, Tableau, Hadoop, and Spark delivered through live interactive courses with expert practitioners, labs, and work on real-world projects.

By the end of the course, you will:

• Build comprehensive knowledge of learning models including linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipeline
• Use the SciPy package and its sub-packages such as Integrate, Optimise, Statistics, IO, and Weave to perform scientific and technical computing
• Master the principles, algorithms, and applications of machine learning
• Learn to use Tableau to analyse data 
• Become proficient in building interactive dashboards
• And more!

 The Data Analyst Bootcamp Program can help you to gain real industry experience to demonstrate key skills of a data analyst such as interpreting data and creating data visualisation.

By the end of the course, you will:

• Understand measures of central tendency, dispersion, correlation, and regression and other significant statistical concepts
• Master SQL concepts such as Universal Query Tool and SQL command
• Build an understanding of Python, allowing you to read and write files, load, work and save data with Pandas
• Explore Python’s data visualisation libraries such as Matplotlib, Seaborn, and Folium
• Gain comprehensive knowledge of the basics of R, and learn how to write your own R scripts
• And more!