Data Science Vs. Data Analytics
For the last two decades, the World is booming with big words such as Artificial Intelligence(AI), Machine Learning(ML), Deep Learning (DL), Data Science, Data Analytics and many more. In this article, I would like to explain Data Science & Data Analytics and the difference between them. Additionally, the application of each field and the required skill set for working in each field are discussed as well.
Let us see why the world has given so much importance to this innovative field.
Here are famous quotes from the data science or related field:
“Data is the new oil.”
This one is quoted by a great Mathematician Clive Humby in the year 2006, and also recently The Economist released a report in 2017 with the title “The world’s most valuable resource is no longer oil, but data”.
‘‘Data is the most valuable asset in the world.’’
Former business development director for Cambridge Analytica, Brittany Kaiser quoted in the movie ‘The Great Hack’- Availble on Netflix.
Data is all around the world. The quantity of data is increasing at an expeditious rate, nearly doubling every one or two years, and constantly affecting the living way of humankind. Research shows that till the year 2020, 1,7 Megabytes of the data containing new information will be generated by every second for every human being on the planet, which makes it totally worthy to familiarize with fundamentals of the field at least. Ultimately, here is where our future lies.
Why Data Science Vs. Data Analytics?
At the same time, data analysts and data scientists both works with humongous amounts of data are generated by users, the main difference lies in what they do with it. It can be very confusing to discriminate between Data Science and Data Analytics. Both fields can be considered two sides of the same. Even Though both terms are highly interconnected, they give contrasting results and pursue different approaches.
Data Science
Data science has wider scope compared to data analytics. Data science is a versatile field that uses scientific methods focused on extracting knowledge and insights from structured and unstructured data. It primarily directs on unearthing answers to the things that we don’t know. Data scientists use a different combination of techniques to obtain answers by using computer science, mathematics, programming, statistics and ML to interpret over a massive chunk of datasets in order to establish a solution to problems that haven’t been thought of yet.
In the field of Data Science, input data is raw or unstructured which is then (by data scientist) cleaned and organized to be sent for data analytics. Exercising data science leads to connecting information and data point to find the connections that can be made helpful for the future of any business.
It also involves search engine exploration, AI and ML. The main agenda of Data science is to find & define new business problems by discovering them and converting data into business use cases which will help a business to push into an innovative direction.
Applications of Data Analytics
It serves very well in the field of Internet search, Digitial Advertisement, Banking, and Manufacturing. It also used for recommendation systems (for example, Netflix- Movie recommendation, Spotify — new track recommendation, etc.,), image recognition, speech recognition, and digital marketing. The below-given picture will provide an overview of Data science applications.
Skillset required for Data Scientist
In-depth knowledge of programming in R and Python, SQL database/coding and Hadoop platform, working with unstructured data are the basic requirements to become a data scientist.
Python, R, SAS, SQL are the most used programming language for Data science applications.
Data Analytics
If we consider data science as a highrise building that holds tools and methods, in which data analytics is a one specific floor in that building. The scope of data analytics is considered on the micro-level compare to data science. Here the problem is already known and by using analytics techniques, the analyst tries to find the best solution to the problem.
It is a very influential part of data science where data is mainly organized, processed and analyzed to solve problems. Data analytics involves processing and performing statistical analysis of certain existing and structured datasets. Data analytics is often time automated in order to present insight into certain problems. To conclude, Data analytics defined as a science of analyzing raw input data to make an outcome about that information.
There are 4 main types of Data analytics processes: Descriptive analytics, Diagnostic analytics, Predictive analysis, and Prescriptive analysis. But this does not come into the scope of this article. so let’s move ahead!
In Figure 6, the workflow of the data analysis process has been shown. In order to perform each step, the required tools are also mentioned. For Example, Data cleansing can be done by using MS Excel, SQL database and Hadoop.
Applications of Data Analytics
Mostly used in domain areas like healthcare, travel, finance, gaming, and Energy Management (Exclusively for smart-grid management, energy optimization, energy distribution, and building automation in utility companies).
Skillset required for Data analyst
In-depth knowledge of SQL Programming skills in R and Python, Hadoop/ Spark are extremely important to data analysts. In addition to SQL programming also statistics & mathematics knowledge, ML, Data visualization skills are necessary to become a data analyst.
Python, R, Hadoop/Spark are the most used programming language for Data analytics applications.
Additional Information
After comparing Glassdoor number for the average salary of a data scientist and Data analyst results are like:
For Data analysts and Data scientists, average salaries are 56672 Euro & 99773 Euro per year.
To conclude, Pick a Data science career over others if you want to make more money!!!
