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Let’s dive into the nuances of data analytics vs data science, certifications that seems to be booming. Let us help you learn what is data science and what is data analytics, along with difference between data science and data analytics.
The study of data in order to extract meaningful insights for businesses is known as data science. It is a multi-disciplinary approach that employs various concepts from a wide range of disciplines such as artificial intelligence (AI), mathematics and statistics along with computer engineering, in order to analyse large amount of data.
Naturally, you may wonder what data science is used for. After churning data with the help of concepts from above mentioned domains, scientists can use this wisdom to gain knowledge about actions and processes, write algorithms that reuse large quantities of information efficiently, increase security and sequestration of sensitive data, and lead to better data driven decision making.
Data analytics is the wisdom of assaying raw data to make conclusions about that information. Numerous ways and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption.
Businesses can optimise their performance with the help of data analytics. Enforcing it into the business model can help reduce costs effective ways of doing business. A company can also use data analytics improve business opinions and help dissect client trends and satisfaction, which can lead to newer and better products and services.
Data science and data analytics deal with principles on a wide spectrum.
Feature | Data Science | Data Analytics |
---|---|---|
Coding Language | Python, C++, Java, Perl | Python and R Language |
Programming Skills | In-depth | Basic |
Machine Learning | Required | Not Required |
Scope | Large | Small |
Other Skills | Data mining activities used | Hadoop based analysis used |
Data Type | Unstructured data | Structured data |
Statistical Skills | Necessary | Minimal or not necessary |
Goals | Deals with innovations | Uses existing resources |
A simple Data Science Course can help you land your desired job profile.
Data science vs. data analytics: Roles & responsibility
The following are just a few examples of the roles & responsibility data scientists vs Data analyst can fill. They work in multiple industries and are responsible for driving an organization’s strategy and decision-making.
Now that we are clear about what is data science and what is data analytics, here’s what a data scientist and a data analyst does. Choosing the best data science certification program to boost your career is essential.
Data Scientist | Data Analyst |
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A common curriculum for data science and data analytic includes math, statistics, computer modeling, programming, and foundational courses in big data and data science.
The following are some of the skill requirements for a career in data science vs data analytics.
To get yourself on the fast track of data analyst career, keep these skills at your disposal
The following are some of the skill requirements for a career in data science vs data analytics.
Tools and technologies employed by data scientists are mainly to facilitate seamless processing of massive chunks of data. A good data scientist must know the right tool. We will now have a quick glance at some of the tools used by data scientists –
Given the complex nature of data analytics job, one must be well aware of common tools used in the field.
The career pathway for Data Science and Data Analytics is quite similar. Typically, Data scientists are much more technical, requiring a mathematical mindset, on the other hand Data Analysts take on a statistical and analytical approach. However, both required a strong educational foundation in Computer Science, or Software Engineering, or Data Science.
The following are the Career Perspective & Future Scope in data science vs data analytics.
So far, having a closer understanding of what is data science and what does a data scientist do, we have a fair idea about its scope. In a developing country such as India, the arena for data related operations is massive. Since the demand to harness data is rising rapidly, one can only expect an upwards trend.
India has a promising future for data analytics since there are so many young, skilled people, and new businesses and entrepreneurs coming up every day. Due to the evolving technical landscape and more recent business challenges, companies are currently seeking for strategies to boost revenues while lowering operating expenses. As a result, data analytics' potential in India is steadily expanding. Due to an increased demand and improved pay, it is currently a desirable employment alternative.
To conclude the data analytics vs data science debate, there is no competition between the two. While data analytics seems like working on ground level, the output is far more versatile. Similarly, the value addition done through data science to an organisation is far greater.
While data analytics is a better career choice for people who want to start their career in analytics, choosing Data science is a better career choice for those who are interested in creating advanced machine learning models and algorithms.
For data analyst a bachelor’s degree in maths, statistics, economics, computer science or in a similar field will suffice. And, for data scientist, a degree in computer studies, economics, finance, business studies, statistics, and mathematics will provide an edge.
The job opportunities are vast with rising demand from the government agencies, finance and technology sector, healthcare and business needs.
For data scientist, the salary ranges from 3.8 lakhs to 26 lakhs with an average of 10 lakhs. For a data analyst, entry level is at 3.5 lakh and mid level is 8.5 lakh.
The industries with high demand for data science and data analytics are technology, healthcare, finance, government agencies, telecom, auto and retail.
Since the background and skill levels are similar, one can choose any of the two careers they wish for.
With high demand from technology, healthcare, finance, government agencies, telecom, auto and retail sector, data science and data analytics are quickly becoming an aspirant favourite.
In simple words, big data refers to any large and complex collection of data, whereas small data comprises of smaller quantities of data involved.
By providing valuable statistics and insights, data science and data analytics help organisations make informed decisions. They have tremendous positive impact on the organisation.
To explain, data engineers build systems for collecting, validating, and preparing high-quality data, while data scientists and data analysts analyse the data to extract knowledge and insights.
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