The post Demystifying Data Science appeared first on Magnimind Academy.
]]>1- What is data science?
The world is becoming a digital space day-by-day and businesses across the globe are dealing with massive amounts of data every day. Businesses have started to realize that this massive amount of data is of no use until they become able to use it to their advantage. Data science is the field that offers a perfect blend of various high-end tools and technologies that help businesses discover patterns hidden in that data. So, you can consider data science as a field with the objective of making business predictions and decisions by analyzing massive amounts of data.
The entire data science process can be distributed in the following stages:
- Data capture
- Maintenance of data
- Processing of data
- Data analyzing
- Communication of the findings
2- Components of data science
When it comes to demystifying data science, it’s important to understand the key components of data science. Let’s have a look.
- Various types of raw datasets
- Computer programming language
- Statistics and probability
- Machine learning
- Big data
3- What is a data scientist?
Probably you’ve already heard that the job role of data scientist has been declared as the hottest one of the 21st century. These days, data scientists have become crucial assets for almost any organization. These professionals are data-driven individuals with robust technical skills and are capable of working with large amounts of information to derive actionable insights that help to build business strategies in their companies.
4- Different roles in the data science field
Over the last decade, a variety of job positions associated with data science have emerged. Some of the most common ones include data scientist, data analyst, and data engineer. Let’s have a quick look at the skills required to get into these positions to help you chalk out your future career path.
- Data scientist: Strong mathematical and statistical skills, programming skills, data visualization, storytelling, among others.
- Data analyst: Mathematical and statistical skills, programming skills, data wrangling, data visualization, among others.
- Data engineer: Databases, programming languages, frameworks, among others.
Final Takeaway
If you’re interested to step in the data science field, it’s important to understand that to become a successful professional in this field, you’d need to acquire a diverse skillset. This is an ever-evolving field and it’s not possible to learn everything. But, if you can master the basics, you’ll surely be in a position to learn anything as and when required. And that’s probably the best way to succeed in fast-developing field like data science.
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To learn more about data science, click here and read our another article.
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