Data Analyst Vs. Data Scientist

The demand for professionals with data-driven skills is soaring. Employers increasingly seek people with grips in artificial intelligence, analytics, machine learning, and related fields. 

Companies rely on data to make crucial business decisions. Likewise, researchers and economists base their findings on key insights from valuable data. 

As such, the demand for data analysts and scientists is pretty much expected. 

Tech-savvy individuals seeking a reliable career in a data-driven world like today wonder how data analyst vs. data scientist roles differ. Do they have the same scope, or is one better than the other? 

This guide digs deeper into both roles, their unique responsibilities, average salaries, and skills.

What is a Data Analyst? 

A data analyst is someone who collects data to spot trends. This helps business leaders and managers make informed decisions. 

The role focuses on conducting statistical analysis to resolve issues and answer questions. For instance, a business seeking to grow its sales will take help from the valuable data driven by the data analyst. 

The professional will get to the problem’s core by identifying why the company’s sales are dropping. Later, they might offer helpful suggestions to boost sales. 

Data analysts use helpful tools like SQL to manage relational bases and Microsoft Excel to break the data into smaller subsets. They also clean the data, place it into a readable format, and discard the irrelevant information. 

They work closely with an organisation’s interdisciplinary team to understand the business goals and tackle data mining, cleaning, and evaluating. 

A data analyst also uses visualisation tools like Power BI and programming languages like Python, R, and SAS at various steps in their data analysing journey.

What is a Data Scientist? 

A data scientist is responsible for modelling processes, creating algorithms, and designing the data. Therefore, they’re more involved in creating automation systems and designing tools. 

A data scientist focuses less on the data and more on developing software and technologies to harvest the information for a company to answer complicated questions. However, one must have sound critical thinking skills to comprehend the implications of the driven data. 

They must have statistical and mathematical knowledge and an innovative mindset to approach problems creatively.

Data Analyst Vs. Data Scientist: Key differences explained 

Just because data analysts and scientists work with data doesn’t make their roles similar. A look into the key responsibilities of both will reveal more.

Data Analyst Role Requirements 

Those intrigued to pursue a data analyst role are expected to fulfil the following requirements. 

• Qualification. If you want to work as a data analyst, you must have industry-related qualifications. However, a few organisations focus more on your skills, passion, and determination. You must have a sound educational background, nonetheless. 

• Hard skills. Nobody expects you to be an expert as a beginner. However, you must be familiar with the latest tech related to data analysis. For instance, an awareness of ML algorithms and data frameworks is preferred.

• Non-technical skills. Outstanding critical thinking and communication skills are arguably the most important soft skills required as a data analyst. You must also have organisational and multitasking skills to work on multiple projects without compromising quality. 

• Programming skills. It goes without saying that a data analyst must have programming skills because you’ll use several programming languages, from R to SQL and Python to CQL, when conducting data analysis. 

• MS Office skills. You could know the ropes of data analysis, but your efforts will go to waste if you fail at this part. A data analyst must effectively share their findings with others. This requires them to translate the data and make it easier to understand. Therefore, you must know how MS Office products (especially Excel) works.

Data Scientist Role Requirements  

Data scientists may require skills demonstrating their capability to dig deeper into the data and draw helpful insights. Employers generally seek data scientists with a background in computer science. The requirement, however, shifts per employer and organisation. 

• Qualification. An employer would likely ask you for qualifications related to the data scientist field. Again, this varies depending on the company you seek to work with.

• Data mining experience. You must also have experience in data mining and particular tools related to statistics. For instance, employers seek data scientists with expertise in statistical tests, model regressions, text mining, and more. 

• Computer programming languages. Working with data is impossible without extensive knowledge and experience in programming languages. A data scientist must know the functionalities of popular programming languages, including Python, R, SQL, and Java. 

• Statistical technology and software experience. To land a reliable data scientist job, you must remain abreast with the latest technological developments like ML models, AI, deep learning, and artificial neural networks. 

• Web services. You must demonstrate expertise in web services like Hadoop, S3, and DigitalOcean. Plus, experience in using information harvested from Site Catalyst, Google Analytics, and other third-party providers is also crucial.

Key responsibilities of a Data Analyst 

A data analyst is essentially responsible for understanding what boosts a business's performance and how it can help a company make more informed decisions.

A straightforward way to learn about the key responsibilities of any role is to check out its job description. Here are some regular tasks you can expect as a data analyst working with an organisation. 

• Find valuable insights. Data analysts spend most of the day analysing and understanding the data related to business processes and their customers. This may include purchasing data or consumer feedback to help a company enhance its strategies. Later, they create charts, graphs, and dashboards to share this information with a company’s stakeholders. 

• Create algorithms. As a data analyst, you also have to build algorithms focused on comprehending customer behaviour and forecasting what they want to experience. For example, say data reveals that a few customers are encountering a problem with a company’s product. This will help brands understand unique customer needs and ways to elevate their experience. 

• Track KPIs and metrics. You must also report the company’s key performance indicators (KPIs). This helps businesses spot the required changes. It also allows companies to gauge their weekly, monthly, and annual progress. The data provides them with an understanding of how business operations have revamped or deteriorated. 

• Run the qualitative analysis. Data analysts also run qualitative analysis to comprehend a company’s processes and see how well a firm’s sales or customer experience processes perform. They can then use the data to discover the pitfalls and work to fill the gaps. Ultimately, it helps enhance a business’s decision-making.  

• Know how to work with data warehouses. Data analysts must know how to write SQL questions to extract data from data warehouses. Businesses can use this data to monitor their performance. For instance, they can determine the average price of goods a specific retailer sells and monitor the most active customers. This helps them tweak their customer services for the better.

Key responsibilities of a Data Scientist 

Data scientists have a wide range of tasks to perform, from software engineering to statistical analysis. Other typical responsibilities of a data scientist include the following: 

• Perform A/B testing by creating their own frameworks. One must not undermine the importance of A/B testing as it allows businesses to compare different features. For instance, a company can perform A/B testing before finalising its logo. However, because A/B testing can be subject to statistical errors, data scientists create their own frameworks. This reduces the issues associated with the testing and boosts the value of results. 

• Analyse new data sources. As a data scientist, you must understand how a new data source fits the existing customer and business models. Plus, knowing how it affects business analytics is essential, too. For instance, if a clothing store shifts from selling products in-store to online, you must understand how this model aligns with the existing business model. 

• Build customised software. Data scientists know how to create customised tools to upgrade a business’s performance. For instance, they understand the purchase behaviour of each customer and help firms monitor their progress. They can use the information drawn from tools to alter existing business practices. 

• Use predictive modelling. Data scientists also use predictive analysis to determine ad performance, customer preferences, and related data to help companies scale their revenue. For instance, a company can improve its targeting strategies by understanding customer preferences and the ideal time and place to display ads. 

• Create algorithms. Data scientists seek ways to utilise the raw data collected by the business. They create customised data models and algorithms to know whether a business wants to improve its services, product quality, or efficiency. Customised data models hit the bull’s eye because of their accuracy in what the business needs.

How much do Data Analysts make? 

The base salary of a data analyst in Ireland is €46,000+ annually. Note, this can include commissions and bonuses.

How much do Data Scientists make? 

The annual salary of a data scientist in Ireland is around €60,000 yearly. Note, this can include commissions and bonuses.

Again, the firm you work with may give more or less than the average national salary, depending on your experience and competence.

What’s similar between a Data Analyst and a Data Scientist? 

One of the biggest similarities between both roles is that they use data to drive actionable insights that help businesses make critical decisions. Both fields also rely on STEM and are in high demand across several industries. Here’s how else the two careers overlap. 

Technical skills 

Data analysts and data scientists require programming skills, including Python, SQL, R, and more. Besides, understanding statistics and Excel is also crucial to both roles. 

Further, they must have excellent data visualisation skills to present valuable data to business stakeholders. 

Non-technical skills 

Data scientists and analysts work closely with a management team that might not be tech-savvy. Therefore, they must have good communication skills to communicate the extracted data effectively. Besides, a blend of critical thinking, creativity, and organisational skills is a cherry on the top. 

Dealing with massive quantities of data 

Professionals in both careers deal with massive quantities of data regularly. The databases might have low-quality data that data analysts and scientists maintain and organise for accurate understanding and analysis.

Data Analyst Vs. Data Scientist: Which route to take? 

Once you know what each career entails, you’ll be intrigued to discover a better route for yourself. You must consider your professional goals, background, and interests to choose the right career path. 

Discover your interests 

Do statistics and numbers pique your interest, or are you intrigued by business and computer science? 

Data analysts are more into numbers and programming. Because they have to monitor a company’s data, they work with large databases to unveil data points from complicated sources. 

Data analysts must also understand how the industry works. Do not hesitate to break into the field if you believe it fits your interests. 

Those intrigued by the business world, statistics, and computer science should opt for a data scientist role. 

The likelihood of career progression boosts when you do something you love. The secret formula for choosing between data analyst and data scientist roles is the same. Consider your interests and pay close attention to what you believe you’ll enjoy doing. 

This will help you pick a career that best suits your skills and interests. 

Think about your ideal salary 

Different companies require different experiences for data analysts and data scientist positions. As such, the average salaries of both careers will be different. 

Given the data derived from Glassdoor and Indeed, the average salary of a data scientist is more than a data analyst. So, if you want to consider a career with a high-paying salary, you can opt for a data scientist role. 

However, if the role of a data analyst intrigues you and you seek more salary, you can always gain experience to maximise your earning potential. 

FAQs

Is a Data Analyst the same as a Data Scientist? 

Data analysts and data scientists do not have the same roles. A data analyst cleans existing data to make it more meaningful. A data scientist, on the other hand, discovers new ways to capture and analyse data to ease tasks for data analysts. 

Data Analyst Vs. Data Scientist: Which is better? 

No career is better or worse. A role that fits your preferences and interests is your ideal career path. For instance, if analytics intrigues you, consider starting your career as a data analyst. However, if you want to use ML and deep learning techniques to capture and interpret data used by analysts, go the data scientist route. 

Can Data Analysts become Data Scientists? 

Because data analysts and data scientists deal with large datasets and share a few similarities, a data analyst can transition to the career path of a data scientist. However, you’d need to learn relevant computer science skills for career progression. 

Conclusion

The demand for data analysts vs. data scientists continues to spike as data shapes global economics. 

Both roles are appealing and highly regarded in today’s data-driven digital world. You can pick one or another based on your interests, educational background, work preferences, and professional goals. 

Because career progression can get challenging in a highly competitive industry like data analytics, consider taking a professional course. 

A UCD Professional Academy Course can help you learn the ropes of data analytics and programming. Not only will it boost your confidence in the workplace, but it will help you further your career.