Understanding Data Science Under 1000 Words

Data is the new oil. In 2006, Clive Humby, UK-based mathematician and the man behind Tesco Clubcard, coined this phrase and changed the face of the world. According to him, data is vital for survival, but unrefined data is of no use like oil. Tech companies are trying to filter and utilize consumer data to create better services for their consumers. And who is filtering out the data? It’s the data scientists.

The new age of engineers and professionals is joining a Data Science Bootcamp to upgrade their skills and future-proof careers. HBR’s revolutionary article ‘Data Scientist: The Sexiest Job of the 21st Century,’ published in 2012, brought data scientists into the limelight. The report labeled them a new breed of professionals who could analyze, track, and predict consumer behavior by filtering and investigating the data.

Here, we will try to explain data science and the fundamentals of the subject in less than 1000 words.

What Is Data Science?

Data Science is a multi-disciplinary field combining machine learning, computational science, and business knowledge. In this field, data scientists sieve through a vast volume of data using analytical tools and algorithms to derive meaningful patterns that interpret consumer behavior.

Data science has revolutionized our world, making it better and more efficient. It helps predict and identify the root cause of a disease or epidemic, improve healthcare for patients, or optimize traffic routes to prevent traffic congestion.

Why Become A Data Scientist?

Data science is a lucrative field. With companies becoming more data-driven, there is a rising demand for skilled professionals who can interpret unfiltered information and unearth underlying patterns. And these agencies are willing to pay a handsome amount to recruit them.

According to Glassdoor, a data scientist is the third most promising position among America’s top 50 jobs in 2022. The median base salary is approximately $120,000. It is an exciting career with stability and high job satisfaction.

Apart from that, a data scientist learns various skills, including programming, statistics, and business analytics, that makes their career future- and industry-proof. These professionals can work in multiple industries with the given set of skills. Also, a data scientist does not require a physical office to work. They can handle the task remotely, working on the deliverables from the comfort of their home.

Prerequisites for Data Science

Data science is an in-demand skill among recruiters. Every organization is trying to be more data-driven and actively hiring skilled professionals to help them achieve this goal. It is a lucrative field and offers excellent pay.

Candidates planning to enroll in a data science program or a coding bootcamp do not require a prerequisite degree in computer science. A degree in the STEM disciples can surely help in sailing through the course, but it’s not a deal-breaker. Ideally, a preliminary understanding of machine learning, programming, and statistics can make the program easier to grasp.

Must-Have Skills For Becoming A Data Scientist

1. Mathematics: In data science, numbers hold a particular position. Anyone planning to become a data scientist must have a strong understanding of linear algebra and quantitative arithmetic.
2. Programming: Data scientists sieve through years of data with the help of complex algorithms and programming tools. Proficiency in programming languages like Python, SQL, and Java is expected from a data scientist to analyze, catalog, and translate the information into a decipherable pattern.
3. Project management: Managing and handling the project is crucial for deriving the results by the assigned deadline.

Data Science Life Cycle

The data science life cycle is roughly divided into five phases, accumulating the data to communicate the finding. It is a lengthy process and can take a few months or more to reach the end-stage.

Five stages are:

1. Capture: The data scientist gathers raw, unfiltered information in this phase.
2. Investigation and sorting: In this phase, data is cleaned, sorted, and cataloged for analysis
3. Processing: Here, the data is processed, analyzed, and clustered to uncover the hidden patterns in the pool of data
4. Analyze: It is the most critical step of data mining. Here the data scientists study the clean, sorted data and patterns to provide predictions or models for companies to run
5. Evolution and Deployment: The model is tested and verified for its accuracy and error margins in the last stage.

Tools Used By Data Scientists

These professionals use multiple tools and algorithms to go through a massive chunk of data. MS Excel is preferred by freshers starting with data science before progressing to high-end analytic tools.

Some of the standard tools used by data scientists include

• Tableau
• Azure ML studio
• SAS
• Informatica/ Talend

Conclusion

Data science is an in-demand sector among companies and recruiters. You, too, can join the Best Data Science Bootcamp by SynergisticIT and join this budding field for a prosperous, satisfying career. During the program, you will learn the critical principles of the subject like AI, Machine Learning, and data visualization. And at the end of the course, you will receive an industry-recognized certificate that can boost your career.

Source: https://datasciencetrainingusa.wordpress.com/2022/02/09/understanding-data-science-under-1000-words/