“Data is a precious thing and will last longer than the systems themselves” - Tim Berners Lee (Inventor of the World Wide Web)
The way in which the world operates, has only resulted in the generation of huge amounts of data on a daily basis. Big Data has evolved as a buzzword for our generation.
Both the disciplines of Data Science as well as Data Analytics, primarily deal with data. This has resulted in interchangeable use of the two terms. However, there are certain fundamental differences between Data Science and Data Analytics.
The difference between Data Analytics and Data Science can be understood along different aspects. In this blog, we will try to look at some of these criteria.
What is Data Science?
Data Science focuses on structured as well as unstructured data. In simple words, it is the science of data which uses technology, statistics and algorithms for the purpose of Data Analysis, Data Mining, Predictive Modelling, Machine Learning Algorithm and so on.
It can be seen as a combination of more than one discipline: Computer Science, Mathematics, Information Science, Statistics, Artificial Intelligence and Machine Learning.
What is Data Analytics?
It is a discipline which deals with the management of raw data through its collection and storage as well as, with the techniques, processes and tools, which help in analyzing it. Data Analytics focuses on discovering patterns, valuable correlations, unseen trends, and extracting meaningful actionable insights from it. It happens to be primarily concerned with Statistics and Statistical Analysis and Mathematics.
To know more about this field; check out our blog on "What is Data Analytics"
Data Science vs. Data Analytics: Fundamental Differences
The two terms: Data Science and Data Analytics are often used as substitutes for one another. This implies that many people believe that they are interchangeable. However, the difference between Data Science and Data Analytics is important to account for, especially as they differ along several key aspects.
Imagine the structure of a house. If Data Science can be regarded as the entire house; then Data Analytics will be one room within that house. This implies that the former is an umbrella which covers multiple fields, while the latter has a narrower and more focused approach as it is primarily concerned with providing answers to questions which can help in data driven decision making.
Thus, the difference between the two fields is in terms of the fact that for a Data Analyst, the question is out there and his job is to make use of existing information to answer that question and derive actionable data.
On the other hand, Data Scientists try to discover new questions which can push innovation. Thus, Data Science is not concerned with providing solutions to specific queries; while Data Analytics happens to be concerned with solving those queries.
Know more about the striking distinctions between the two job roles of a Data Analyst and a Data Scientist; Check out our blog on "Data Analyst vs. Data Scientist: Understanding the Two Positions"
It is clear from the above paragraphs, that the difference between Data Science and Data Analytics is essentially a difference of scope between the two fields.
The task of a Data Analyst is largely concerned with routine analysis of data, requiring them to produce reports regularly. A Data Scientist, in turn, might be involved in designing the model for the Storage, Manipulation, Analysis and Management of data.
Thus, while both are data professionals, a Data Analyst tries to understand data at hand; a Data Scientist works at a larger level as they try to develop new strategies for collection and analysis of data.
Difference between Data Science and Data Analytics: Differing Job Roles
The job role of a Data Analyst concerns itself with the maintenance of Databases and Data Systems, usage of Statistical Techniques and tools for the Interpretation of data sets and the preparation of reports through Data Visualization techniques.
The main purpose is to identify trends, discover patterns and make predictions. In contrast, the job role of a Data Scientist is predominantly concerned with the designing of data models, along with creation of predictive models and algorithms which could help in the process of data extraction and its analysis.
It is important to remember that the differing job roles of a Data Scientist and a Data Analyst differs from industry to industry and location to location. Their roles and responsibilities within a business enterprise can be summed up in the following manner:
- Data mining (building ETL pipelines or using APIs)
- Performing statistical analysis using different machine learning algorithms like Random Forest, logistic regression, Decision Trees and so on
- They might spend considerable amount of their time scrubbing data
- Involved in the development of Big Data infrastructures using Spark, tools such as Pig and Hive and Hadoop
- Data cleansing with the help of programming languages like R and Python
- Involved in creation of automation techniques and programs which could help in simplifying the routine activities
- Collection and interpretation of data
- Analysis and forecasting of data using Excel
- Executing different types of analytics such as descriptive, predictive, diagnostic or prescriptive analytics
- Carrying out Data Querying using SQL
- Producing dashboards with the help of Business Intelligence software
Aspiring to initiate a career shift towards the job role of a Data Scientist?, do read our blog on "Data Analyst to Data Scientist: A look at this Career Transition"
While each profile seeks to acquire valuable insights through the analysis of data which could help in decision making; the difference between Data Science and Data Analytics is essentially in terms of the tools used for carrying out this activity.
While Data Scientists largely use JAVA, machine learning and Python for analyzing data; Data Analysts largely use SAS, SQL and business intelligence software for the purpose.
Data Analytics vs Data Science: Skill Comparison
The issue of Data Science vs. Data Analytics can be understood in terms of a comparison between the core skills required of a Data Scientist and that of a Data Analyst.
In general terms, a Data Scientist is required to be an expert in statistics and Mathematics, with skills in programming languages such as SQL, R and Python; Machine Learning and Predictive Modelling.
On the other hand, a Data Analyst is expected to show skills in Data warehousing, Data mining, Data analysis, Data modeling, Database management, Data visualization and Statistical analysis.
Accordingly, the skills of a Data Scientist must include
- Expertise in programming languages such as Python, Scala, R, Julia, MATLAB, Java and SQL
- Knowledge of Big Data platforms like Hadoop, Apache Spark and so on
- Experienced in Statistics and Probability, Linear Algebra and Multivariate Calculus
- Expertise in Machine Learning, Database management and Data wrangling
The skills of a Data Analyst must include
- Expertise in using tools like Tableau, Power BI, SAS and so on
- Knowledge of Data Visualization
- Experienced in SQL and Excel database
- Knowledge of Python or R programming
Difference between Data Analytics and Data Science: Career Prospects
Many are of the opinion that the difference between Data Science and Data Analytics comes from the difference in the educational and professional background of an individual. The skill set required and the job responsibilities expected of a Data Analyst has been highlighted above.
In order to ensure that their education is such that they are able to fulfil these activities; they generally pursue an undergraduate degree in technology, math (STEM) major, engineering or science and might even aim for an advanced degree in analytics. Apart from that, they may also try to improve their skills in the field of Programming, Modeling, Math, Predictive Analytics, Science and Databases.
On much the same note, the job responsibilities of a Data Scientist is such that they are required to apply different techniques like Machine Learning and Data Mining for scrubbing data. This generally requires an advanced degree such as a master’s in Data Science.
On the issue of Data Analytics vs. Data Science; Data Scientists are held to be more mathematical and technical than Data Analysts. Thus, Data Scientists are largely required to have a more rigorous training in Computer Science.
Another important point of difference between a Data Scientist and a Data Analyst is in terms of the estimated salary guaranteed within the two fields. This gap in the salary offered to a Data Scientist and a Data Analyst, is also because of the fact that different levels of experiences are required of them.
As per the report by Robert Half Technology (RHT)’s 2020 Salary Guide, the earning potential of a Data Analyst is generally held to be in the estimated range of $83,750 and $142,500. Additionally, they have the option of increasing their market value by acquiring further new skills.
Moreover, Data Analysts who have over ten years of experience, often move into more profitable domains. In such a situation, the role of a developer and a position of a Data Scientist are quite attractive to them.
On the basis of the same report, the earning of a Data Scientist is generally held to be in the estimated range of $105,750 and $180,250. They too, have an extremely positive career path with numerous opportunities for improving their skills and advancing to senior roles such as that of a Data Engineer or a Data Architect.
Data Science vs Data Analytics: Two Sides of the Same Coin
In spite of the disciplinary difference between Data Analytics and Data Science, the two fields are closely overlapping and interconnected. Both the domains deal with Big Data, but each follows a unique approach.
Data Science lays the foundation of data models and looks at big datasets for arriving at observations, deriving insights and understanding trends. The information so generated can be useful especially in the field of Artificial Intelligence and Machine Learning.
However, even when Data Science discovers new questions, it does nothing to provide concrete answers to those questions. It is here when Data Analytics comes into the picture.
Data Analytics adopts a narrow approach as it zooms into the details of the derived insights. This helps in answering questions which were raised by the Data Scientists.
Thus, while Data Science drives innovation through new queries; Data Analytics completes the circle by providing solutions to those queries. Hence, it would be wrong to understand the issue of Data Science vs. Data Analytics in terms of two rooms which are completely separate from one another.
Instead, it is important to understand that the boundary between the two fields is quite flexible and there is much mixing which only helps in improving the process of data management.
The modern world is the world where Data is the King. So, if you are looking for a long term career potential; Data Science and Data Analytics are definitely some of the best choices.
Analysis of Data has been accepted as important for business growth and productivity. However, the task to usefully utilize that data lies in the hands of Data Scientists and Data Analysts. Thus, it is a known fact that the two disciplines have emerged as two of most in-demand domains in modern times.
It won’t be surprising that many of you, who are getting started with your career, find yourself unsure and confused about the right choice between Data Science and Data Analytics. But, when you are in such a state, it is important to remember certain important points.
It is important to be clear of the differences between the two fields. Additionally, you should take three points under consideration: your educational background, your interests and your desired salary.
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