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How Automation is Improving Data Science Techniques

Data automation


Worried your career might be made redundant by a robot in the not-too-distant future? Data automation doesn’t always result in the disappearance of jobs. Rather than fearing automation, artificial intelligence, and machine learning, the data science sector has embraced it as an opportunity – in fact, automation is changing data science. 

Data science is a multidisciplinary field that uses scientific processes, algorithms and methods to turn raw data into business assets. Many of the techniques used by data scientists have been enhanced by data science automation, creating more opportunities for innovation and creativity. Keeping up with these advancements is crucial to navigating a career in data science as the industry evolves. 

Research by Forbes found that 60% of decision makers at companies adopting artificial intelligence (AI) cite data quality as either challenging or very challenging, proving there’s demand for data scientists who can implement new technologies. Designed to broaden your creativity and problem solving skills, Monash Online’s Graduate Diploma of Data Science will help you adapt to the following ways automation is improving data science techniques and the data science process.  

Data Cleaning

Previously time-consuming tasks like sourcing data, preparation and data cleaning can now be done more efficiently due to automation tools or an automated system – one of the great benefits of automation. An integral discipline of data science, data cleansing allows businesses and data science teams to avoid significant hassles like processing errors, incorrect invoicing data and erroneous consumer profiling in their data science projects. This process can be slow. And while automation hasn’t removed the need for data cleansing, it’s become a less complex business process. 

Data cleaning typically involves several steps, including: 

  • Accessing data 
  • Formatting data correctly 
  • Identifying errors 
  • Connecting insights within the data  

Automation solutions have made this analysis process less laborious, leaving data scientists with more time and resources for other, more interesting tasks. 

To adopt the most efficient data preparation and data cleansing techniques, it’s important to understand the latest trends. During our Graduate Diploma of Data Science, you’ll learn the most up-to-date fundamental principles of data cleansing in the FIT5196 Data Wrangling unit

Data Visualisation 

Human brains process visuals 60,000 times faster than they do text according to scientists, making visual representations of data essential. Only humans can accurately identify narratives in data, but computers are getting the hang of telling those stories in an easy-to-understand way. Narrative generation software can now be integrated with data dashboards, creating visual summaries in a fraction of the time it would take to do so manually. 

This takes the complexity out of turning data into cutting edge charts, videos and infographics, allowing data scientists to create increasingly intuitive channels of communication. Combining the inherently human ability to identify stories worth telling and the automation of data visualisation will make data even more valuable to businesses. 

If you want to remain at the forefront of data visualisation techniques, our Graduate Diploma of Data Science includes an elective on data exploration and visualisation.

Learn more about the art of storytelling with data visualisation and how it can advance your career as a data scientist. 

Data Modelling 

Building data models is a crucial data science skill. It involves turning data into tangible results, which businesses can then turn into profit. Most data scientists will already know this process can be challenging, but data automation is making it less difficult. 

As data modelling techniques rely on rigid mathematical limitations and formulas, it’s well-suited to automation. Data modelling automation relies on the use of libraries, which can now automate specific parts of the modelling process. These libraries take your data in its current form and construct a complete model out of it, without any need for human input. 

Need to advance your data modelling skills? While studying the Graduate Diploma of Data Science, you’ll learn the latest trends in data modelling during the FIT5197 Modelling for data analytics unit. Students will also explore data warehouse automation, which primarily focuses on managing large amounts of data. 

With so many improved techniques to make the most of, there’s no need for data scientists to be afraid of data automation. Machines may be taking over some aspects of data science, but humans will be left to enjoy the most exciting aspects of the industry, such as research, critical thinking, decision-making, and finding creative uses for data. 

Prepare yourself for the automation revolution by earning formal recognition of your experience with Monash Online’s Graduate Diploma of Data Science. To learn more about this qualification, book a 15-minute phone call with an Enrolment Advisor today.