Data Analytics Programme: A Complete Career Guide at TMU

Data Analytics is simply the process of examining, cleaning, transforming and modelling data to uncover insights. It is a composite process involving various stages to extract meaningful insights from raw data that can be used to make informed decisions. Data Analytics covers multiple domains like healthcare analytics, business analytics, financial analytics, retail and government analytics.

Whether it is social media communication and shopping habits or healthcare knowledge and business choices, data is ubiquitous. The growing digitalisation in industries has created an unparalleled demand for professionals who know how to analyse information and turn it into valuable knowledge. Hence, data analytics plays a key role in business growth, education and almost every industry by turning data into actionable knowledge.

Understanding Data Analytics Programme

Data analytics is the process of collecting, transforming, and organising data to make informed decisions. It combines statistics, mathematics, programming and domain knowledge to extract values from data. The programme equips students with the skills to transform a large body of data into actionable insights that can be used to enhance business growth, efficiency and innovation.

Types of Data Analytics

There are four main types of data analytics, which include:

• Descriptive Analytics: Descriptive analytics focuses on understanding what happened by analysing historical data. It uses historical data to identify trends and patterns.
• Diagnostic Analytics: Diagnostic Analytics gives answers to why something happened by identifying patterns. It delves deeper to understand the cause of past events.
• Predictive Analytics: Predictive analytics uses historical data and statistical models to predict future outcomes, such as demand prediction and risk assessment. 
• Prescriptive Analytics: The prescriptive analytics recommends what actions to take. It addresses pricing strategies, inventory optimisation, market campaign decisions, and much more.

Data Analytics Programme Levels

Eligibility Criteria for Data Analytics Programme

Core Subjects in Data Analytics Programme

Tools & Technologies Covered

TMU’s Cutting-Edge Data Analytics Programme

TMU’s Cutting-Edge Data Analytics Programme is designed to equip students with the latest skills and tools required in today’s data-driven world. TMU provides both theoretical skills in statistics and business and practical training in Python, R, SQL, Tableau, Power BI, and the technologies of big data. The university prioritises practical education based on projects, case studies, and real-life data so that students can acquire industry-relevant experience

Real-World Projects & Practical Learning at TMU

TMU’s Data Analytics programme emphasises experiential learning through:

• Case studies
• Live industry datasets
• Capstone projects
• Internships
• Simulated business problems

Data Analytics Programme: Career Opportunities 

Future Scope of Data Analytics Programme

By pursuing a Data Analytics programme, students can gain access to a world of career opportunities in the modern-day data-centred age. The programme provides the students with marketable competencies in statistics, programming (Python, R), SQL, big data technology, machine learning, and data visualisation software like Tableau and Power BI. As AI, cloud computing, and predictive analytics continue to grow, individuals with data analytics skills will be in high demand, and thus this programme may not only be career-focused but also future-proof.

• Growth of artificial intelligence: Artificial Intelligence is the creation of computer systems that are capable of doing jobs that would have traditionally been done by human intelligence, including learning, reasoning, and problem-solving. The pace at which AI is growing is increasing owing to improvements in algorithms, computing capacities, and access to data.
• Automation and smart decision systems: Automation refers to applying technology to carry out routine or repetitive tasks automatically. Combined with AI, this results in smarter decision systems that have the ability to analyse data, forecast, and make informed decisions automatically.
• Digital transformation across industries: Digital transformation refers to the incorporation of digital technologies in every business or organisation's operation. Healthcare, education, banking, retail, and logistics are among the industries to modernise operations and remain competitive in a digital-first world with the use of analytics, AI, and cloud technologies.
• Increasing importance of data-driven governance: Cloud computing is a way for businesses to access, process, and store data remotely, which is scalable, flexible, and cost-effective. Data-driven governance is also influenced by the growing dependence on data and analytics, whereby the decisions, policies, and strategies made rely on real-time insights rather than intuition.

Conclusion

The Data Analytics programme is leading the digital revolution, providing promising career opportunities and industry appropriateness for future careers. As the use of data-driven decision-making, AI and automation, and cloud technologies grows in popularity, data analysts are in high demand in various fields, including IT, finance, healthcare, e-commerce, and marketing.

FAQ

Q1. What is a data analytics programme?

Ans. Data analytics is the process of collecting, transforming, and organising data to make informed decisions. It combines statistics, mathematics, programming and domain knowledge to extract values from data.

Q2. What are the programmes for data analysis?

Ans. 7 Data Analysis Software Applications You Need to Know

• Excel
• Python
• Tableau
• MySQL
• SAS
• Jupyter Notebook

Q3. What are the 4 types of data analysis?

Ans. There are four main types of data analytics, which include:

• Descriptive Analytics
• Diagnostic Analytics
• Predictive Analytics
• Prescriptive Analytics