Why do you think that Data Science is a dying career? On the contrary, Data Science is one of the fastest-growing industries in the world right now. How so? Well, you’ll have to dive deeper into this article to know-how:
First and foremost, what is Data Science?
Data Science refers to a multifaceted approach in order to extract knowledge and insights from structured as well as unstructured data with the use of scientific methods, algorithms, processes, and systems. It provides a wide range of domains of application with the help of insights and knowledge.
Data Science, we can say, is based on data mining, big data, and machine learning to unify statistics, information, along with data analysis and related methods. To learn more about data science in-depth, check out this article-
Who are Data Scientists? What is the difference between Data Scientists and Data Analysts?
Basically, the practitioners of data science are known as Data scientists.
Their abilities include computer science as well as pure science along with statistics and maths. They collect, store, and analyze large amounts of data, both organized and unstructured.
A data scientist’s job description may be summarized as follows:
- Using scientific methodologies, as well as mathematics and statistics.
- To examine and prepare data, a wide range of instruments and approaches are used.
- Predictive analytics and AI (Artificial Intelligence), as well as machine learning and deep learning models, are used to derive data insights.
- To create software that automates data processing and computations.
- Create narratives that clearly communicate the findings and their significance to stakeholders, decision-makers, and others.
What is Data Analytics?
More importantly, what is the difference between Data science and data analytics? We briefly discussed Data Science in the above section, coming to data analytics- Data Analytics refers to the process of obtaining insights from data that will help in making better decisions related to business. This process of analyzing data customarily involves 5 frequentative phases:
- Identifying the data to be analyzed
- Collecting the data
- Cleaning the data for analysis preparation
- Analyzing data
- Interpreting the analysis results
Depending on the subject you’re attempting to answer, data analytics can take various forms. In a nutshell, descriptive analytics explains what happened, diagnostic analytics explains why it happened, predictive analytics generates future forecasts, and prescriptive analysis generates actionable recommendations.
Who is a Data Analyst?
In order to answer a query or solve an issue, a data analyst gathers, cleans, and evaluates data sets. Business, finance, criminal justice, science, medical, and government are just a few of the fields where they can operate.
The basic difference between the two is, the scope of Data Science is wider than Data Analytics. The former is more focused on mining larger datasets in comparison to the latter, i.e.- data analytics.
Now you are familiar with the basic difference between the two. When we talk about Data Science as a career, and whether it is a dying field in the industry, thankfully it isn’t. In fact, it is one of the fastest-growing industries, it’s not just futuristic, but an ongoing one.
Where can you learn Data Science?
In this GenZ generation, there are multiple platforms from where you can learn Data Science(thanks to the internet). But if you are looking for the best one, you might want to check out the Free Resources on Data Science, available on NADOS. NADOS is a social media platform via Pepcoding, built with the intention to bring peers, experienced working professionals along with industry experts to one place. This platform provides over 1500+ Free resources, basically self-explanatory videos on multiple topics and niches including Programming, Data Analytics, Business Analytics, and more.
It is a great space to build your skills from scratch, as well as enhance your existing skills. Furthermore, you can get placement assistance and career opportunities as well in the career section of NADOS. Sounds cool? You should definitely explore NADOS by yourself for a better experience.- nados.io
Thanks for reading this far.
Author: Sejal Shaw
Also read:
