Big data has become an essential component in the IT world today. Creating such large datasets definitely requires an in-depth understanding and the requisite tools to parse through these data and retrieve the required information.
To help in understanding big data, data science and data analytics have also transformed from being simply academic pursuits to becoming crucial elements of big data analytics tools as well as business intelligence.
Distinguishing between data science and data analytics can sometimes be confusing, although they each pursue a completely different approach. Hence, we must thoroughly grasp these two interconnected but different concerts.
Comparison between Data Science and Data Analytics
|Parameter||Data Science||Data Analytics|
|Statistical Skills||Data science necessarily requires statistical skills||Data analytics doesn’t necessarily require statistical skills|
|Data type||Data science deals primarily with data that is totally unstructured||Data analytics is effective only when it deals with available structured data|
|Goals||Data science involves a lot of new innovations and a lot of explorations too||Data analytics involves and uses only existing resources|
|Scope||Data science has a really wide scope and deals with almost everything from a macro angle||Data analytics has a limited scope and deals with almost everything from a micro angle|
|Skill sets||Data mining activities are essential to get truly meaningful and in-depth insights||To get conclusions and results from raw data, data analytics uses Hadoop-based analysis mainly|
|Machine learning||Machine learning algorithms are regularly used by data science to gain deeper insights||Data analytics does not really make use of any machine learning at all|
|Programming skills||Total knowledge and experience in programming is required to be a data scientist||To be a data analyst just basic programming skills are required|
|Language used||While Python is the most commonly used language in data science, Perl, Java, C++, and more are also used||Data analytics almost invariably uses R language and Python|
major differences between Data Science and Data Analytics
What exactly is Data Science?
Data science is primarily about instructing meaningful information and deep insights from structured and unstructured data using various scientific methods, algorithms, and processes.
It is in great demand today and involves programming, computations, statistics, mathematics, and more to help the game inside from which a huge set of data, known as big data, is provided in various formats.
What exactly is Data Analytics?
Data analytics is principally all about processing raw data to get particular results in conclusions. By getting these results and conclusions, data analytics truly helps businesses to make decisions based on real-life data and insights.
Data analytics truly translates huge figures that come in the form of data into play in English which helps even a layman to understand the conclusion.
Contrast Between Data Science and Data Analytics
- Data Science- Data science requires that professionals in the field are adept in advanced statistics and predictive analysis.
- Data Analytics- Data analytics requires the professionals in the field are truly adept at statistics and foundational maths at least.
Skills sets required
- Data Science- Data science requires various skills in statistics, databases, programming languages, tools, and certain analytical skills as well.
- Data Analytics- Data analytics requires various skills in predictive analysis, object orientation, software tools, and data modeling skills as well.
Software and tools
- Data Science- a data scientist needs to have a thorough knowledge and understanding of Hadoop, Spark, TensorFlow, MySQL, etc.
- Data Analytics- a data analyst needs to have a thorough knowledge and understanding of Ms excel, SAS, and any software related to business intelligence, etc.
- Data Science- Data science involves advanced object-oriented programming.
- Data Analytics- Data analytics involves SQL, Python, and R.
- Data Science- Data scientists need to be experienced and knowledgeable in the fields of data modeling and machine learning.
- Data Analytics- Data analysts need to be experienced and knowledgeable in the fields of data visualization and analytical thinking.
- Data Science- Data science focuses mainly on predictive modeling and machine learning.
- Data Analytics- Data analytics focuses primarily on historical data in context.
- Data Science- Data science primarily involves artificial intelligence, machine learning, information science, statistics, computer science, mathematics, etc.
- Data Analytics- Data analytics is mainly involved with statistical analysis, mathematics, statistics, structured data gathering, structured information delivery, etc.
- Data Science- Data science actually models data in a way so that predictions can be made, strategy can be supported, and opportunities can be identified.
- Data Analytics- Data analytics helps to both spot trains and also solve various problems. It is all about taking a snapshot of what is existent at the current moment and creating an actionable report so that decision-making can happen seamlessly.
Q & A
- Data Science- Data science is all about identifying questions and determining the most plausible ways to get correct answers.
- Data Analytics- Data analytics gets direct questions and tries to find various ways of using data analysis so that answers can be provided for immediate decision-making.
Manipulation and Management
- Data Science- to improve how data supports business goals, it is essential that data science uses appropriate machine learning and algorithms.
- Data Analytics- to analyze results, data analytics is more involved in collecting, maintaining, and storing data.
- Data Science- a data scientist generally needs to work with a team of data scientists to deeply understand the business and technical needs, design and develop AI solutions, deliver deep-dive workshops, partner with radius AI service teams, solutions architects, and even marketing and business development teams, and finally also develop and support the AI-related subject matter experts and the internal community.
- Data Analytics- a data analyst generally needs to work with multidimensional databases, design, develop and maintain ongoing metrics and reports, make recommendations what strategies and techniques, and help in enabling effective decision-making by retrieving and aggregating deep insight from multiple sources of data into an actionable format.
Possible roles and designations:
- Data Science- Data scientists can expect to perform in the following roles, amongst a multitude of others:
- Pricing analyst
- Computer systems analyst
- Data Analytics- Data analysts can expect to perform in the following roles, among several others:
- Financial quantitative analyst
- Business intelligence analyst
- Management analyst
- Data Science- Data science deals primarily with unstructured data from which it pulls various in-depth insights.
- Data Analytics- Data analytics deals with structured data mainly, from which it analyzes and finds out the various situations in an answer is required for decision-making on the spot.
How it helps
- Data Science- Data science always targets extracting meaningful information and in-depth insights by using various scientific methods, processes, and algorithms on both structured and unstructured data.
- Data Analytics- Data analytics is primarily focused more on getting conclusions by processing raw structured data, which actually helps many businesses to make decisions based on the conclusions that have been drawn from the available data.
- Data Science- a data science baccalaureate actually helps to learn all that’s required in data science.
- Data Analytics- a data analytics baccalaureate essentially helps to learn all that’s required in data analytics.
Frequently Asked Questions (FAQs)
Q1. What are the primary goals of data science and data analytics?
While data science is more concerned with asking the right questions, data analytics it’s more focused on finding actionable data.
Q2. What are some of the major fields where we can find data science?
The major fields where data science and we found to perform well are corporate analytics, machine learning, search engine engineering, and artificial intelligence.
Q3. What are some fields where data analytics makes its presence more meaningful?
Some of the fields where data analytics makes its presence felt while also being more meaningful in those fields are travel, healthcare, gaming, and any industry that needs immediate data and conclusions for decision making.
Q4. How can we coin the scope of data science and data analytics in one word?
While data science has a macro angle to study, data analytics is more concerned with the micro angle.
Q5. What kind of data do data science and data analytics use?
Both data science and data analytics use big data with a large base to perform their respective scope of work in a truly meaningful way.
- 20+ Difference between C and C++
- 20+ Differences between AI And Machine Learning
- 20+ Difference Between Coding and Programming
- 20+ Difference between Scripting Language and Programming Language
- 20+ Differences Between Java and C++
“Business, marketing, and blogging – these three words describe me the best. I am the founder of Burban Branding and Media, and a self-taught marketer with 10 years of experience. My passion lies in helping startups enhance their business through marketing, HR, leadership, and finance. I am on a mission to assist businesses in achieving their goals.”