Do you like working with numbers and data? You might want to think about having a career as a data geek! Data analyst vs data scientist? Thinking hard? And confused about which to choose? Which one is for you? Come along, we have an answer to that as we discuss.
If you have an analytical mind and are interested in working with data, a career as an analyst or data scientist can be rewarding. Although there are some similarities between these roles, it is important not to misinterpret them. This is because there are several differences. These include education requirements, salary, and responsibilities that may influence your decision. Information is power, and nowhere is this more evident than in the world of big data. Even seemingly innocuous data can, when collected and analyzed, provide actionable business insights.
Do You Know?
The data science and analytics industries are experiencing rapid growth right now. Data science jobs have increased by 650% since 2012. And data analytics jobs are set to grow another 22% by 2030.
However, the terms “data scientist” and “data analyst” are so often used interchangeably that it can be difficult to distinguish between them. And it can be especially confusing when you’re trying to break into the big data field. That’s why we created this guide.
What all is covered in this post?
To make it easier for you, we have created a quick guide to everything you need to know about data analyst vs data scientist.
Before moving further let’s first understand…
Who are data analysts and data scientists?
Not surprisingly, a data analyst analyses data. This involves gathering data from a variety of sources and putting it through the processes of conflicting data mining. These processes structure and derive information from data, which can then be presented to those who can act on it.
Stakeholders and decision-makers often ask data analytics questions and ask them to find a way to answer them. This means collecting and correlating relevant data and connecting it together to create a bigger picture.
A data scientist can have several different roles within a company. Some of which are very similar to the role of a data analyst, including collecting, cleaning, and analyzing data to generate useful information. However, if a data analyst is likely given specific questions to find answers to, a data scientist may instead analyze the same data set with the goal of discovering patterns that may lead to a new line of inquiry. In other words, a data scientist must find the right questions as well as the right answers.
In addition, the data scientist will design models and write algorithms and software to simplify the data analysis process for both himself and his data analysis team members. A recent study found that cleaning and structuring data takes up more of a data scientist’s time than any other task, so there is an ongoing effort to automate more and more of this process so that more time can be spent on analysis.
Note: If you want to know more about the field of data science, refer to our blog post!
Data analyst vs data scientist – Comparison
Data Analysts and Data Scientists are both in demand in today’s data-driven world, but they have distinct skill sets and responsibilities. As data analysts, they primarily focus on analyzing and interpreting data to support decision-making. They work with large datasets to identify trends and patterns, create charts and dashboards, and communicate their insights through reports and presentations. Data Scientists, on the other hand, use advanced statistical and machine-learning techniques to solve complex problems and build predictive models.
Here’s a comparison of Data Analyst vs Data Scientist to help you choose –
|Feature||Data Analyst||Data Scientist|
|Overview||A data analyst focuses on collecting, processing, and performing statistical analyses on large datasets to extract insights and support decision-making.||Data scientist combines statistical and machine learning techniques with their domain expertise to solve complex problems, build predictive models, and discover hidden insights in data.|
|Skills & Responsibilities||– Analyzing large datasets and identifying trends and patterns. – Creating charts, graphs, and dashboards to visually represent data insights. – Collaborating with stakeholders to understand their data needs. – Cleaning and pre-processing data to ensure accuracy. – Writing SQL queries to extract data from databases. -Communicating data insights to stakeholders through reports and presentations.||– Developing advanced statistical models and algorithms. – Applying machine learning techniques to build predictive models. – Conducting complex data analysis and interpretation. – Solving real-world problems using data-driven approaches. – Building scalable data infrastructure to support analysis. – Communicating results and insights to technical and non-technical stakeholders.|
|Salary & Career Outlook||Median salary of a data analyst is around $62,000 per year (source: Glassdoor) with a good job outlook as demand for data analysis skills is increasing.||Median salary of a data scientist is around $120,000 per year (source: Glassdoor) with high demand and a competitive job market.|
|Education & Training||A bachelor’s degree in a relevant field such as mathematics, statistics, economics, or computer science is typically required. Professional certification in data analysis tools such as SQL and Tableau is also desirable.||A master’s degree in statistics, mathematics, computer science or a related field is commonly required. Professional certification in machine learning, big data, and statistics is highly valued.|
|Key Tools & Technologies||SQL, Excel, Tableau, PowerBI, Google Analytics, Python (optional), R (optional).||SQL, Python, R, SAS, Spark, Hadoop, TensorFlow, PyTorch, Git, Tableau, PowerBI.|
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Which is right for you?
Businesses experience great benefits and growth with the help of obtained data available within the organization. This is primarily why there is a huge increase in the number of job opportunities for data scientists and data analysts across most large industrial sectors.
Data has become the new fuel for any big or small business, more so for startups.
- Data Science is useful for analyzing raw and unstructured databases to obtain useful information to drive a business further toward higher profitability & growth through useful predictions.
- This field focuses on finding answers to questions that the company does not know about. Data scientists use a variety of methods and tools to find answers.
- Data Analytics uses available data sets and performs statistical analyses to determine which data can be extracted.
- It focuses on solving current business problems from available data by presenting information in a visual way that is easy to understand for everyone.
- In addition, data analysis focuses on coming up with results that can provide rapid improvement for a business.
According to a Future of Jobs Report by the World Economic Forum, both data scientists and data analysts have a huge demand in the market.
How to choose a right between the two?
Following are some of the factors to consider when choosing between a data analyst and a data scientist-
- Education and Skills Requirements: Data analysts typically need a bachelor’s degree in a related field. Such as mathematics, statistics, or computer science, while data scientists often require a master’s degree in a similar field. Data professionals need a more advanced skill set that includes machine learning, big data, and statistical analysis.
- Career Goals: Do you want to focus on data analysis to support decision-making? Or do you want to use data to solve complex problems and create predictive models? These are the main responsibilities of data analysts and scientists respectively.
- Job Outlook: Demand for data professionals is high and the job market is competitive, while data analysts also have good job prospects, but lower salary expectations.
- Domain Expertise: Data scientists often have a deeper understanding of the domain in which they work and can use data to solve problems in that domain. If you have extensive experience in a specific industry, a career as a data scientist may be a better fit for you.
- Technical skills: Data analysts typically use SQL and data visualization tools like Tableau or PowerBI, while data scientists use more advanced tools like Python, R, and machine learning libraries like TensorFlow or PyTorch. Consider existing technical skills and what you would like to learn more about.
Ultimately, deciding between a career as a data analyst or a data scientist will depend on your personal interests, skills, and goals.
Both Data Analysts and Data Scientists play critical roles in the field of data analysis. Data Analysts focus on analyzing and interpreting data to support decision-making, while Data Scientists use advanced statistical and machine-learning techniques to build predictive models and solve complex problems.
When considering a career in data analysis, it’s important to consider your personal interests, skills, and goals, as well as the job market demand and salary expectations. Ultimately, both Data Analysts and Data Scientists require strong analytical skills and a deep understanding of data, and the choice between the two will depend on your preferred level of technical proficiency and your desired career path.
Talk to an expert today to figure out more of what you must know before you begin your journey. At iDreamCareer with the help of our Career Counselling and Guidance Services and educational counsellors, we try to help many young confused minds from the 9th class, 10th class, class 11, and class 12 with an aim to select their suitable career choices.
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Data scientists are experts at data interpretation, but they also often can write codes and work on mathematical modeling. Most data scientists have advanced qualifications, and many go from data analysts to data scientists. They can do the job of a data analyst, but also participate in machine learning, have advanced editing skills, and can create new data modeling processes. They can work with algorithms, speculative models, and more.
Data Analysts typically need a bachelor’s degree in a relevant field such as mathematics or statistics, while Data Scientists often require a master’s degree in a similar field.
Data Analysts typically use SQL and data visualization tools, while Data Scientists use more advanced tools such as Python, R, and machine learning libraries.
Data Analysts face challenges in dealing with large amounts of data and finding meaningful patterns, while Data Scientists face challenges in developing models that can effectively solve real-world problems and making those models accessible to non-technical stakeholders.
Data Scientists tend to have a more technical background, with advanced skills in mathematics, computer science, and programming. Data Analysts may have a technical background, but it is not required for the role.
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Anushree has 4+ years of experience in the career counseling industry as a Content Writer. She has also worked as a Social Media Marketing Expert for a startup and Content Quality Analyst for Publishing and E-learning Industry. She has done her Master’s in Commerce and PGDM in Finance & Trade and Marketing & HR, but she is currently following her passion for writing.