Every business today is thriving on data; it’s their most valuable asset. Moreover, the emergence of artificial intelligence and machine learning has opened new opportunities and career paths. And if you’re a data wonk, and yet don’t know what you can do with a data analytics degree, I am here to help you. We will discover the best possibilities with your data analytics degree and its current relevance.
What Do Data Analysts Do?
Data analysts are the driving force behind the entire data cycle, converting raw data into actionable information. They collect, clean, process, and interpret large amounts of multisourced data to solve problems and fulfill business needs and objectives. They help businesses eliminate the guesswork and make informed decisions by inculcating a data-driven culture. Data analysts are everywhere, including business, finance, criminal justice, science, medicine, and government.
Skills Needed to Become a Data Analyst
- Problem-solving & critical thinking: As a data analyst, it will be your everyday job to answer questions and concerns using data to display a solution. To understand trends and functionality of data, you need problem-solving and critical thinking skills.
- Communication: The basic and utmost need of any profession, and here it is especially needed. You will be presenting and giving a detailed analysis of your findings while collaborating with teams, departments, and often the stakeholders. You need to be able to explain complex ideas simply.
- Data tools: To clean and organize data, you must be well-versed in MS Excel and SQL, though Excel is the most commonly used, experts prefer SQL to handle large sets of data.
- Programming languages: Statistical programming languages such as Python and R will help you handle large amounts of data and solve complex problems. They are used everywhere in multiple industries and give you the option to choose from several job descriptions.
- Data visualization: After solving everything and interpreting what the data says, a data analyst must have the ability to present that information. Make sure to use charts, graphs, and visuals in a way that is understandable to your colleagues, employers, and stakeholders.
- Math & stats: Knowing mathematical and statistical concepts helps you choose the correct tools for solving problems. And you will easily be able to identify bugs and errors and find the best possible solution by looking at a problem from different angles.
Related: Data Analyst Roadmap 2026: Skills, Tools, and Salary Insights
Careers & Job Opportunities with a Data Analytics Degree
The field of data has opened career paths across sectors. I will give a list of job positions you can choose from, as per your industry and field.
1. Finance & Banking
Quantitative Analyst
The role of a quantitative analyst is to use mathematical and statistical techniques to help organizations make financial decisions. You will be responsible for designing and implementing quantitative investment strategies, using maths and stats to model financial and economic systems, and also identifying patterns and trends in data.
Risk Analyst
They are the ones helping companies to identify, assess, and prioritize potential risks that can harm business operations. Risk analysts develop and implement risk management roadmaps to mitigate or eradicate those risks. Also, gather insights from stakeholders to acknowledge requirements and provide recommendations. It is a demanding yet exciting job.
Financial Analyst
Of course, you can become a financial analyst with a data analytics degree; it is an exemplary combination of technical skills with financial intelligence. You need to have a keen eye for data-oriented skills to deal with heavily quantitative data. Collect and analyze it to make forecasts and predictions. Identify trends and patterns in finance and develop models.
2. Operations & Logistics
Operation Analyst
At the operations analyst positions, you will be expected to improve everyday operations and identify any inefficiencies and gaps. Your job is to make business processes faster, economical, and more efficient. Operations analysts work in close collaboration with logistics, finance, customer service, product development, and management teams to figure out issues and offer solutions on the basis of data. Their responsibility is to make working models that optimize resources, reduce costs, and maintain a smooth workflow.
Logistics Analyst
A logistics analyst is focused on making goods movement within the company’s supply chain more efficient. They work with supply chain tracking tools that help design the best possible transportation routes, warehouse operations, and distribution strategies. The solutions must be provided to save cost, reduce delays, better packaging, storage, and carrier selection. Logistics analysts often collaborate with in-house teams and outside vendors to identify issues and find the best solutions.
Related: How To Become a Data Scientist
3. Business & Corporate
Business Intelligence (BI) Analyst
A BI analyst often handles end-to-end data management, where they collect, clean, and organize. Then analyze patterns and trends, and accordingly build dashboards and analysis reports to form strategies based on insights. The role responsibilities may vary depending on the company size, as big firms often have a designated person for each job in the data cycle. For instance, data engineers will be responsible for collecting and making infrastructure for data, whereas business teams may collaborate on business strategies with BI analysts. It is a job that demands technical and strategic abilities.
Data Visualization Engineer
A newly explored career path, yet one of the most important roles in the field of data, as they are the backbone of data systems. They are responsible for designing, developing, and maintaining data visualization systems and dashboards. They use tools and mechanics to represent complex data insights appealingly and understandably to non-technical peers and stakeholders.
Remember, data engineers are different from data visualization engineers, though it may sound similar, their job roles are not the same.
4. Technology & Engineering
Machine Learning Engineer
A machine learning engineer’s job is to build and design self-running applications and AI systems to automate predictive models. Their work is to turn raw data into smart applications such as recommendation systems on streaming platforms, fraud detection systems, or an AI chatbot that understands relevant queries. Data analysts focus on driving insights, data scientists build models, and ML engineers deploy those models into real and scalable systems.
Big Data Analyst
A big data analyst deals with complex and very large data sets that can’t be worked on with simple tools or traditional datasets. Their role involves finding patterns, trends, and insights from multiple huge sources, including financial transactions, social media activities, logs, consumer data, and more. The job requires statistics and analytics skills such as scripting and software language, statistical analysis, planning and interpretation, and many other workplace skills as well.
Others
The other sectors and industries that offer careers in data analytics are marketing and customer insight, where you can serve as, marketing analyst, customer insight analyst, market research analyst, and sales analyst.
In healthcare & science, healthcare data analyst and research data analyst are the explored job roles. Whereas, human resources has job roles including HR data analyst and people analytics specialist.
Market Growth & Salary
The growth of the data science field is correlated with the increase in the amount of data generated, which is predicted to reach approximately 180 zettabytes by 2025 ( predicted by IDC).
Entering 2025, the global data science platform market is expected to be $241.2 billion with a growing CAGR of 28.8%.
| JOB ROLE | AVERAGE ANNUAL SALARY IN 2024 |
|---|---|
| Machine Learning Engineer | $161,286 |
| Business Intelligence (BI) Analyst | $123,284 |
| Data Engineer | $104,088 |
| Data Analyst | $108,336 |
| Data Scientist | $158,218 |
| Clinical Data Managers | $137,500 |
| Data Ethics Specialists | $121,242 |
As per the United States Data Science Institute factsheet
Also, the three top-tier leadership roles in data science, which are: Chief Data Officer, Chief Data Scientist & Director of Data Science, have expected annual salaries of 334,200 USD, 277,700 USD, and 202,447 USD, respectively.
Related: Top Open-Source Data Analytics Tools for Smarter Insights
Data Engineering Vs Data Science Vs Data Analytics
| Data Engineering | Data Science | Data Analytics |
|---|---|---|
| They are the backbone of data | They function as the brain of data | They become the voice of data |
| They are responsible for building infrastructure, designing systems, and pipelines to make data usable | They are responsible for making predictions from the acquired data by developing predictive models and algorithms | They are responsible for interpreting the predicted data insights to help organizations make informed decisions |
| Collecting, storing, and processing data | Using ML, forecasting, and advanced analytics on data | Designing reports, data visualization, and trend analysis |
| Technical skills are SQL, Python/ Java, ETL, Big Data (Hadoop, Spark) | ML, Python/R, Stats, and Artificial Intelligence frameworks | Tools such as Excel, SQL, Power BI/ Tableau, and the basics of stats |
| Their work complexity is high, involving systems and coding | Their work complexity is very high, involving math, ML, and coding | Their work complexity is medium, involving interpretation & visualization |
| They make the raw data usable | They build intelligent systems from data | They turn data into workable actions |
In A Nutshell
After reading this article, you can comprehend that the field of data is huge and has an enormous range for career exploration. If you are a number cruncher with an interest in leveraging the best available technical advancements in the world, you don’t have to worry about what you can do with a data analytics degree. You can choose any path across sectors in data science, from operations analyst positions to people analytics specialists; the options are wide open. The reports suggest the market is growing at a staggering rate and is also bringing in new job opportunities.
Now it’s your job to read, research, and study to select the best suitable role in the data industry for yourself.
Let us know which data science sector and application appeals to you more.
