The key factors that contribute significantly to the world we live in today are technology and collaboration. Technology offers us the means to get where we want, be it as individuals or as an organization, while collaboration enables us to get there faster. New technologies are throwing up data, while collaboration is helping us use the data meaningfully.
In a world that is accelerating its pace every minute of the day, trendspotting and parsing data becomes a significant part of every industry.
Three main factors influence the availability of big data and the need for it to be repurposed for better deployment:
i) Enormous amounts of digital data is coming in from every sector: private companies, government, social service and health for example.
ii) The quality of data coming in is varied as it is both structured (products, electronic devices) and unstructured (human input). Sources such as the Internet collect data for various reasons and not all data received may be in the form required for further use through correct interpretation.
iii) The past decade has seen a rise in the development of algorithms that can be used in unfathomable ways to extract intelligent solutions for real world problems.
Little wonder, there is a boom in the data science industry in terms of employment opportunities too. However, when job titles or roles like data analyst and data scientist crop up everywhere, it is but natural to wonder how they are different, what role each of them perform and what their key functions are.
Here is an easy break-down of the data science and analytics functions in an enterprise. The roles shouldered, responsibilities performed and skills required by the professionals who handle the two functions are also explained to help clear confusion over data scientist vs data analyst definitions. This is important as there are many data analytics courses online even as many demand a good data science online course.
What is Data Science?
Data science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
Data science revolves around data mining and big data and involves use of the most powerful hardware, the most powerful programming systems, and the most efficient algorithms to solve complex problems.
Data science may also be defined as a field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data.
Who is a Data Scientist?
A data scientist is one who applies machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems that perform tasks otherwise handled by human intelligence to produce actionable data.
The term “data scientist” was coined by a former LinkedIn employee, D J Patil, in consultation with Jeff Hammerbacher, co-founder of Cloudera at a time when humongous data started pouring in from a highly digitalized environment. The highly technical work coupled with inventive intelligence, lead to the title, data scientist.
Simple Data Scientist Definition: A data scientist is one who organizes, packages and delivers data.
What is Data Analysis?
By definition, data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, reaching conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social domains.
In today’s business world, data analysis facilitates processes like measuring, recording, and tracking performance metrics so that management can set new goals and improve efficiency.
Who is a data analyst?
A data analyst is a professional who works with data tools to collect and analyze raw data and present it to decision-makers in an organization, in a form that is easy to understand and relate to in the business world. Think of a data analyst as a detective with a magnifying glass, sifting through all the data that comes from various sources and identifying common patterns, getting fresh insights, spotting trends and analyzing problems that are thrown up to come up with solutions.
Simple Data Analyst Definition: A data analyst is one who interprets data using statistical techniques.
So it is clear from the preceding paragraphs that the debate around data scientist vs data analyst is about who does what. While the data scientist creates a technical system which produces data and insights, the data analyst redeploys it to generate further action reports which when implemented can lead to tangible business benefits. Let us now go to the main roles and responsibilities of a data scientist vs roles and responsibilities of a data analyst.
Responsibilities of a Data Scientist
The main responsibilities of a data scientist are as listed below:
- Extract knowledge and insights from structured and unstructured data.
- Evaluate statistical models to judge how valid is the analysis of the models.
- Develop improved predictive algorithms using machine learning.
- Apply SQL queries in databases to gather complex data.
- Recognize business requirements and devise measurement plans like instrumentation, data collection and so on.
- Apply improvements in internal data processing by automating manual procedures
- Refine delivery and presentation of the data
- Use analytic tools and languages like Python and R to capture actionable insights that influence important business metrics like revenue, customer acquisition, product enhancement, etc.
- Constantly test and enhance the competence of machine learning models.
- Create data visualizations and summarize findings in the form of advanced analysis.
Responsibilities of a Data Analyst
Here are the responsibilities of a data analyst:
- Mining, collecting, dissecting and interpreting data
- Analyzing results for deep insights with the use of statistical tools and methodologies
- Reporting the results back to the relevant members of the business
- Identifying patterns and pinpointing trends in data sets
- Working with teams to improve processes within an organization
- Working with the management team to correlate data report findings with business need
- Designing, creating and maintaining databases and data systems
- Writing comprehensive reports with charts, graphs and tables
- Fixing code problems and data-related issues
- Defining new data collection and analysis processes
Data Scientist Vs Data Analyst Roles
In contrast to a data scientist who generates data from different sources, the data analyst interprets and recommends how the data may be put to use to meet organisational goals. While there may be some overlap in the nature of some functions, there is no clash in data scientist vs data analyst responsibilities, as the data sets are in the raw and refined forms respectively.
The data scientist vs data analyst role may be derived from the above explanation on responsibilities, to further delineate the two job titles. The role of a data scientist is that of a wizard who mops up data from everywhere to derive value from it. As generating usable data is the main task, a data scientist can be seen as a change maker.
On the other hand, the role of a data analyst involves examining data closely to gain insights and spot patterns. As utilising the data in a manner that helps the organization is the main goal, a data analyst can be seen as a change catalyst.
To put it differently, the data scientist vs data analyst role is like that of prospecting vs mining of ore for the end goal of conversion into pure metal. There is no ‘versus’ in reality, it is just an aligned and continuous process. In fact, it is also accepted that data analysis is a subset of data science.
Data Scientist Skills Vs Data Analyst Skills
When the role and responsibilities differ, it follows that the skill set of a data scientist vs data analyst will also vary.
A data scientist will need to be equipped with certain core skills that focus around technologies like Hadoop, Python, Java, Tableau and SQL to drive efficient analytics. They need to be proficient in computing languages like R, SAS, Pig, Spark, Hive, and Matlab too. Knowledge of distributed computing, predictive modeling, storytelling and visualization are a must. Obviously then, a data scientist needs to hold higher educational qualifications, including a Ph.D. With a clear understanding of mathematics, statistics and machine learning, a data scientist must be able to lead a team with collaborative communication skills, ability to simplify complex concepts and a knack to resolve disputes.
On the other hand, data analyst skills include deductive reasoning, intelligent analysis and interpretative logic. An important skill that also has to be tailored to the needs of the industry segment or domain they work in. is the ability to translate complex algorithmic data to customer-centric insights. A good data analyst will also be able to present these insights through easy-to-follow presentations. Going a step further, the data analyst should also give recommendations on how to adapt the findings into an actionable plan. Knowledge of statistical tools and good communication skills are a must.
Differences between a data scientist and data analyst
There are many differences in the career roles, responsibilities and skills of a data scientist and a data analyst. What is similar in the role of data scientist and data analyst however, is that they both work with data. How they work with the data and what kind of data they work with brings in the data scientist vs data analyst discussion again. Data scientists work with both structured and unstructured data obtained from a range of sources such as customer transactions, click streams, sensors, social media, log files and GPS plots.
Whereas a data analyst works with data collected, organised and shared by the data scientist.
It is the responsibility of the data scientist to construct and design new processes to gather larger pools of data and carry out data modeling with the use of algorithms, prototypes, and custom analysis.
The data analyst uses the large data set given by the data scientist to find trends and to create visual representations to help businesses devise a better strategy to attain goals. This provides competitive advantage to the enterprise.
The difference between a data scientist and a data analyst is also described as one forecasts the future by accessing the data, while the other improves the present by attributing meaning to the data. If a data scientist transforms big data into actionable data sets, a data analyst converts insights into impact.
Some of the other differences are listed in a tabular form below:
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Data Scientist
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Data Analyst
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More technical role including programming
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More analytical role including giving recommendations
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Collects, organizes and presents data
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Processes, analyzes and, interprets data
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Seen as technical brains
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Seen as solution-finders
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Understands coding, languages and ML, AI, analytics
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Understands mathematical statistics, R, Python & spreadsheet tools
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Gives structure to data
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Gives meaning to data
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Ability to use predictive modeling
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Ability to use data visualization
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Throws up questions with data sets
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Finds answers from data sets
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Inquisitive but open-minded
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Learner with strategic perspective
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Works with data from multiple sources for macro business goals
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Works on data pool from single source for specified objectives
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Is paid higher salary and can progress to chief data scientist role
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On a lower pay scale but can aspire to become data scientist
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Conclusion: While there are many differences between data science and data analytics, they are also interdependent functions and there is a great demand for talent in both disciplines. You have to choose which career path suits you best, before embarking on a data science career: a data scientist provides the recipe, while a data analyst cooks and reviews the dish!
Also, you can always switch or progress to other roles like data architect, data statistician, data engineer, data consultant and data manager that may prove to be more attractive or lucrative for you in future..
