Both data science and data analytics are flourishing professional fields with lucrative career prospects. Driven by the IT and ITeS revolution, Machine Learning (ML) and Artificial Intelligence (AI) are fast integrating into every aspect of our regular lives and businesses. As the saying goes, ‘data is the new oil’ and it is invaluable for gathering vital insights and enhancing business performance. There is a clear need to process raw data for gathering these insights. Data scientists and data analysts have vital roles to play in this regard. Knowing the difference between data science and data analytics will help you choose between the two.
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Table of Contents
What is Data Science?
It is a multidisciplinary area emphasizing the discovery of actionable findings/insights from both structured and raw data sets. This field majorly focuses on finding answers to several aspects with several techniques fusing ML, computer science, predictive analytics, and statistics. The main objective of data scientists is asking questions and finding solutions/answers. They usually achieve this through the prediction of prospective trends, exploring disconnected or disparate data sources, and finding newer ways to decode information.
What is Data Analytics?
If you are pondering over the question, “What is data analytics?” you should know that it focuses on processing current data sets and conducting statistical analysis of the same. Analysts emphasize building new methods for processing, capturing, and organizing data for finding the actionable insights towards current issues and presenting the information in the best possible manner. The field works to solve problems for queries that do not have any answers as of yet. It is also result-based and aimed at bringing about instant improvements.
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What is the difference between data science and data analytics?
Goal-Based Differences
The difference between data science and data analytics boils down to the fundamental objectives of both disciplines. Analytics is inclined towards analyzing and mining business-oriented data. Data science is tuned for finding suitable business questions and their answers.
Analysis focuses on answering questions for swifter and improved decision-making at businesses. It makes use of available data for unearthing actionable insights. Analytics has a specific area-wise focus with defined objectives.
Data science, on the other hand, focuses majorly on finding new questions that may not have come up till now and then finding their answers. Data science attempts to build links for shaping new questions and answering them for posterity. This is a unique aspect of this field. It has a broader field of operation.
Usage-Based Differences
If you are still wondering what is the difference between data science and data analytics, there are some subtle aspects as well. Analytics is focused on putting historical information into context while data science emphasizes predictive modeling and machine learning. It is a multidisciplinary practice covering inference and algorithms alike. Data analytics, however, covers some broader branches of analysis and statistics.
Scientists and analysts tap data through diverse methods. Data scientists use a fusion of statistical, mathematical, and machine learning techniques for processing, cleaning, and interpreting data to gather insights. They tailor highly advanced procedures for data modeling with prototypes, models, ML-based algorithms, and custom analysis. Data analysts scrutinize data sets for picking trends, coming to conclusions, and collecting large data volumes for organizing and analyzing the same. They identify patterns that are relevant and present findings through suitable visualization such as graphs and charts.
Skill-Based Differences
Skills required for data analytics:
1. Intermediate Statistics
2. Excel, SQL database
3. Python, SAS, R, and other statistics tools
4. BI tools like Power BI for reporting purposes
Skills in modeling, databases, statistics, and predictive analytics will always benefit aspiring data analysts. They need not always have an engineering background.
Skills required for data science include the following:
1. Advanced statistics
2. Mathematics
3. Machine learning
4. Predictive modeling
5. Programming
6. Big data tools like Spark and Hadoop
7. Data Visualization with D3.js, Tableau, QlikView, and other tools
8. SQL, NoSQL databases like MongoDB and Cassandra
9. Programming languages like Scala, R, and Python
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Career Prospects in Data Science and Data Analytics
While there are differences between the two fields, they are two of the hottest profiles in the business world at the moment. The career progression is similar for data analysts and data scientists. The latter should have an educational background in software engineering, data science, or computer science. The former should ideally pursue undergraduate courses in IT (Information Technology), computer science, statistics, and mathematics.
Job Roles for Data Analysts:
1. Data Cleansing
2. Exploratory Analysis
3. Developing KPIs and Visualizations
4. Finding Patterns with Statistical Tools
Job Roles for Data Scientists:
1. Cleansing, processing, and verifying Data Integrity
2. Gathering business insights via algorithms and ML techniques
3. Exploratory Analysis
4. Identification of new trends with future predictions
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Which one should you choose?
While it is tough to choose between data analytics and data science, there are some things to keep in mind. Data analytics is better for those professionals with 2-5 years of work experience and with an interest in building data models and expertise in data warehousing. They should ideally have capabilities for using this expertise with tools like Python, Excel, SQL, Tableau, and Power BI for performing analytical tasks and creating dashboards. Professionals in areas like data warehousing, database administration, sales/marketing/finance, Ops, QA engineering, and SCM should consider data analytics.
Data science is a better pathway for those professionals with 1-10 years of work experience and a desire to learn Python programming. It is a good option for those working as business analysts, BI engineers, IT application engineers, data analysts, and architects. Aspirants in this space with a desire to boost analytical skills may expect a rewarding career in data science.
