Big data analytics and data science are two of the most popular job roles in the entire computer science and business analysis job market. However, there are some key differences between the two roles that often confuse job seekers, giving rise to the question of data analyst vs data scientist - which career path to adopt.
Both data analysts and scientists have responsibilities that lead to a more informed audience, and better business decision making.
If you're looking for a job in one of the two roles but can't decide which one has the better career prospects for you, keep reading.
In this article, we'll clearly define what data analysts and data scientists do, the main differences between the positions, and how they both function within the field of data.
Let's jump right in.
Data Analyst vs Data Scientist At a Glance
While both roles off with handling data and turning it into insights that people can benefit from, analysis is a preliminary step towards developing self-collection and analyzing models, something that data scientists do.
Simply put, data analysis is just one aspect of the larger data scientist role.
Data analyst roles are meant to break down larger sets of data and gather actionable insights from them. They then adopt various data visualization methods to present that data in a way that's easily understandable and palatable to stakeholders, customers, or whoever the intended audience is.
Data analyst skills include various data collection, processing, sifting, and visualization techniques.
Data scientists, on the other hand, do all of the above while coming up with case scenarios where data can be used to benefit the host , and developing automated systems that collect and process data on their own.
In other words, they do the job of the analyst, create systems that can function as analysts, and think about questions that may need data analysis to answer, and that can benefit the once answered.
Data Analyst vs Data Scientist: Roles and Responsibilities
Now that you have an idea of how the two positions function, let's expand on both and see how they actually differ in the workplace.
are the job descriptions and responsibilities of both data analysts and scientists.
Data Analyst Responsibilities
The basic data analyst job description comes with the following responsibilities:
- Data Collection: Analysts are responsible for collecting the data that will then get processed into valuable pointers that companies can make business decisions on. For this, they use a variety of methods, including surveys, questionnaires, data collection software, and social media.
- Pattern Recognition: Once they have collected the required data, analysts have to sift through it to see common factors in the accumulated data sets. The patterns will reveal common subject behaviors that companies can leverage. Analysts are also referred to as data engineers, in this specific role.
- Data Consolidation: The data that results from recognizing patterns is then consolidated into reports that contain pointers on how the results can be used to maximize business success.
- Data Visualization: Once they have built a data-backed case, analysts have to then create a visual representation of the data to make it easy for various stakeholders and non-technical audience to understand it, and use it as part of their business strategies.
Additionally, some analysts also perform statistical analysis with the help of various statistical models and business analytics software developed with the 's problems in mind.
Data Scientist Responsibilities
The responsibilities of a modern data scientist are as follows:
- Data Accumulation: Data scientists are responsible for the entirety of the new data collection, analysis, and consolidation at their host organization. They do this by developing custom data mining tools and software systems.
- Algorithm Development: As part of the data collection and analysis process, data scientists use a variety of programming languages and visualization platforms (Python, SAS, Hadoop, Tableau, SQL, Hive, Spark, Java, etc.) to develop algorithms that help analyze data according to pre-set parameters.
- Predictive Models: In order to avoid lengthy data collection and processing, data scientists develop models that help perform predictive analytics, to accurately predict potential business problems and create solutions beforehand.
- Data Visualization: Data scientists take the data visualization responsibility further by presenting much larger amounts of data into multimedia forms, with insights from both raw data analysis and predictive models.
- POC Development: As part of convincing stakeholders of the validity of a particular data set and the results gained by it, data scientists have to develop proofs of concept after testing every possible use case for each specific data set.
In addition to the above, data scientists are responsible for all the high-level exploratory data analysis at their host organizations. This is usually the responsibility of the data analyst, however, some companies allow only senior officials to handle data that could impact business significantly.
Some data scientists also develop and implement AI-powered machine learning models for business intelligence applications.
Data Analyst vs Data Scientist: Typical Qualifications
The basic qualifications for the data analyst and data scientist are the same, more or less.
The primary difference lies in the advanced responsibilities of the data scientist, which require similarly advanced qualifications.
To compare both, are the required qualifications for a data analyst vs a data scientist.
Data Analyst Qualifications
Analysts usually require either a bachelor's degree in a relevant field or an advanced diploma, along with the requisite experience.
For the most part, are the required qualifications for data analysts:
- Bachelor's degree in business administration, computer science, or applied statistics
- Accredited certification in data management and analysis
- Master's degree in a relevant field (optional)
- Online certification with several years of experience in applied data analysis
Entry-level data analysts can also make do with an intermediate online certification in data analytics and an internship that provides hands-on experience in the field.
Data Scientist Qualifications
Data scientists are slightly higher on the professional ladder as compared to analysts, and so require more advanced educational credentials.
The typical qualifications for a data scientist, are:
- Bachelor's degree in data science, applied statistics, or computer science
- Master's degree in data science
- Technical courses for programming skills in multiple languages
Data scientists in the tech-development industry will work closely with AI systems which they will use to power their machine learning models. This makes it useful for future data scientists to consider taking courses dedicated to machine learning.
How to Be a Successful Data Analyst or Data Scientist in 2021
Regardless of which position you actually make a career out of, there are a few key factors that make up the recipe for success in the data field.
Here are some of the things that hopefuls can do to succeed in either position:
- Attain certification from a reputable online institute. Online courses are different from advanced degrees in that they're vocational in nature, and lean more towards developing a specialized skill set instead of a broader one. Online courses in applied data analysis or data science will most likely directly reflect the prevalent skill requirements in the data field in 2021.
- Develop supplementary skills. These skills will function as support for the primary skills and will help the individual adjust to the position better. Furthermore, they will help build trust in various stakeholders and employees they collaborate with. Valuable supplementary skills include business acumen, sharp observation, efficient problem-solving, and communication skills.
- Network while looking for a data analyst or scientist job. Instead of simply going through job postings, try to connect with industry leaders, subject matter experts, as well as people on the same professional level as you. This will provide fresh insights about what's going on in the data sphere currently and expert tips for a more rewarding data science career.
Additionally, try to learn as much as you can about the skill trends in the industry. Skill requirements change with new technology and methodologies. Make sure to always stay up to date by being regular on professional networking and blogging sites such as .
Data is the most precious resource for any , especially in 2021 when it's responsible for almost all the business success stories that we see today.
in fact, according to the Harvard Business Review, data science has been regarded as the sexiest job of the 21st century. This is not surprising, seeing as almost all the case studies for the biggest business growth in recent years involve leveraging the right data at the right time.
In light of this, a job in data is almost guaranteed to be financially and professionally rewarding, given that the candidate has the right qualifications and skills for it.
In conclusion, make sure to upskill yourself beyond what companies require from a specific position. this will help you achieve quicker progress while also making it easier for you to understand what the job demands of you at any given time.