The upsurge of big data has introduced the business industry with two new buzzwords, Data Science and Analytics, which also represent the two most in-demand and high-paying jobs of contemporary times. The popularity of data science and analytics careers has created significant demand for academic courses, such as MSc Data Analytics, to nurture and prepare aspiring individuals to become data professionals. The MSc programme allows students to build fundamental knowledge to work in data analytics by equipping them with the knowledge and skills required to become a part of the highly competitive and fast-growing industry.
To put that in perspective, the World Economic Forum stated that by the end of 2020, everyday global data generation is expected to reach 44 zettabytes, and by 2025 it is expected to reach 436 extra bytes of data. This is because Big Data includes everything from texts to emails to tweets and user searches to social media chatter and data generated from IoT(Internet of Things) and every other online activity.
Since big data, data science, and data analytics are emerging technology, people typically use data science and data analytics interchangeably. However, Data science is an umbrella term that encompasses data analytics but is also a different entity altogether. The starting point of the confusion is that both works with big data but do so differently. Keep reading this article to learn about the difference between data science and analytics.
Data Science and Data Analytics
What is Data Science?
Data science primarily cleans, builds, and organizes data sets to create and leverage algorithms and statistical models. It does custom analysis to collect and shape data into meaningful, insightful information. Data science combines multiple disciplines, such as mathematics, statistics, Information Sciences, Artificial Intelligence, Machine Learning, and computer science.
What is Data Analytics?
Data analytics is a branch of data science that focuses on answers to questions that data science brings forth. It typically aims to find solutions to questions related to business innovation to determine how they can be implemented within the organisation to foster data-driven decision-making, eventually leading to business growth and profitability.
Required skills for Data Science and Data Analytics
Data Science and Data Analytics primarily work with data. However, each requires a different skill set and tools. Though data science and data analytics require mathematics, software tools, and programming languages, they differ in both situations. Here’s the list of skills required for data science and data analytics:
Skills for Data Science
- Advanced Statistics, Predictive Analytics
- Hadoop, Spark, MySQL, TensorFlow
- Machine Learning, Data Modelling
- Advanced Object-oriented programming
Skills for Data Analytics
- Foundational Mathematics, Statistics
- Analytical Thinking, Data Visualisation
- Excel, business intelligence software, SAS
- Basic Fluency in Python, R, SQL, Tableau, Power BI
Conclusion
These are the key difference between data science and data analytics. However, the distance might look small in the bigger picture. However, for students or professionals aspiring to leverage their understanding of data by focusing on frameworks, key concepts, and techniques that underlie both fields.
Big data is growing exponentially, leading to massive amounts of data generation. Therefore, data science and analytics will be needed to manage and produce valuable results. If you are keen to pursue a career in data, then an MSc in Data Analytics might be the right course.