Data Science Vs Data Analysis ! What’s the Difference
As the world gradually becomes progressively more connected, the growth of internet-based services today has its day in the sun. More than 3.7 billion people are actively using internet services at today's time, creating more than 2.5 quintillion bytes of data—nothing wrong to assume; it's already in heaps. Did you know? These numbers are expected to grow to 180 zettabytes by the next 4 or 5 years. Such huge numbers indeed! What's more, people are showing great interest in this career and pursuing data scientist training. Good for all!!! Well, data keeps on growing! In addition, people and technology advancements never stop and wait for someone else to reap its advantages. However, by not making use of the technology available at your fingertips, you're risking way too much. Odds are, you or maybe your business is risking and limiting their growth, success, profits, and a lot more. To manage this massive amount of data with adequate consideration and efficiency, there are domain professionals like data engineers, data analysts, and data scientists to do that job. Let's explore the differences between data analysis and data science. Not so often, two of these sounds a lot similar, but there are quite separable, having dissimilar implications and meanings.
This write-up tends to shed light on what data analysis and data scientist means from a professional point of view. Let's jump to it!
Data Science vs. Data Analysis – Differences
Data Science Just like science is a significant term including different things and fortes, data science is a broad term that includes using different methods and data to get some or other type of information. Onto the umbrella of this broad concept, this data science serves as a field of automated statistics, having diverse models that aid in analyzing and predicting certain outcomes. It is much like delving into the world of heaps of data and trying to find new insights and certain data patterns. Skills and knowledge prerequisite to work under this domain – • • • •
Python or R SQL Machine Learning Skills Jupyter Notebook
• •
Algorithms/Modeling Business acumen
Constructively altogether, data science and its professionals can do sundry things, but the core roles are to: ➢ ➢ ➢ ➢
Analyzing heaps of data to define business problems Discovering valuable data from it To do explanatory data analysis practicing different methods and logics Compiling code and coming up with likely outcomes/anticipations
It would be best to pursue data scientist training to acquire complete knowledge and hands-on training in this domain. Data Analysis If we see data science as a home that has diverse methods and tools, then data analysis is a specific room (that is valuable and quite important) part of that home. Nonetheless, it is much similar and alike to the data science concept, although somewhat more specific and constrained. The idea behind the data analysis concept is to visualize the landscape of data and come up with predictive and useful inputs. The job is to do predictive analytics without making use of any algorithmic and automated method and then, later on, passing it to some nontechnical providers and sharing their insights. Skills and knowledge prerequisite to work under this domain – • • • •
SQL Spreadsheet Tools (Excel) Tableau Data visualization tools such as tableau
Constructively altogether, data analysis and its professionals can do sundry things, but the core roles are to: ➢ ➢ ➢ ➢
Meeting stakeholders and analyzing different business problems Discovering valuable data from it EDA, trend analysis, and conceptions (Excel and Tableau) Considering the workplace, giving discoveries and actionable visions those same stakeholders
Job Responsibilities – Data Scientist vs. Data Analyst Differences In Data Science Domain • • •
Be a smart and considerate leader Finding different and unique features to unlock the value of data ahead Organizing data in the right means for analysis purpose
• • • •
Identifying value-driven data Developing diverse smart methods and models Doing experiments using different ways to come up with desired end-results Data visualization
In Data Analysis Domain • • • • • • •
Writing different SQL queries to know outcomes to diverse business operations Filtering out data problems and partialities during acquisition Analyzing data to find associations and identify patterns of data Implementing metrics to come up with helpful data patterns Tracing data from different data sources and solving a business problem with accuracy and efficiency Doing statistical analysis Creating data reports using different methods to come up with great decisions
Even though two of these terms share a common name, that is (data), but the difference shall not be overlooked in any case. Data Science Or Data Analysis - Which Can Give You A More Promising & Prosperous Career? Since by now you've got a firm knowledge of the difference between two of them and know what each of them entails, you will be in the position to evaluate the right path for you. However, in order to further determine, here I'm jotting down three aspects that you must consider. Consider the area of your interests
Know what drives you and your passion. See whether numbers and stats excite you more, or are you someone who is more inclined towards computer science stuff. This answer is going to be an extremely influential yet key factor in deciding your career path.
Data analyst professionals are surrounded by numbers, programming, and statistics. They're the ones who work on databases from disparate sources and come up with valuable insights. While data science professionals prerequisite mixture of computer science, math, and strong business acumen. Choosing a career that has more interest will help you excel in the forthcoming. Hence, make sure to make this decision cautiously. Two of them have a lot to offer in this ultra-technological digital era. Consider your background study, experience, and knowledge Since two of these professions are somewhat identical, their dissimilarities are about – educational background solely. Therefore while making your final-call, it is vital to review educational requirements before investing in your career. Go with the one that seems to be more exciting and fascinating for you. Greatest of all, pursue the career that has your interest more and kick-start your learning journey ahead. Final Thoughts Job titles for two of these professions are deceivingly similar given various differences in their educational background and job responsibilities.
Once you have considered every important aspect with adequate consideration, you will be able to come up with a better decision – must say a choice that is going to decide your career and path to success in the coming years! Personally, I'm a data scientist by profession and have pursued data science certification training to excel in my professional life. So, it's all up to you, whatever you choose, make sure to put all your sweat and dedication into it! Got some concerns and felt like talking with real experts who can guide you better and give answers to all of it? Feel free to comment or contact us directly at Learnow.