This term was first used in 2001, and it essentially means preparing data to be studied and analyzed, using scientific methods, algorithms, and other techniques. This field of study is interdisciplinary, meaning that it pulls from many other fields of study, such as business intelligence, coding, data structures, machine learning, statistics, and other mathematical disciplines.

All industries use data science in some way since all industries collect and store data that needs to be analyzed and interpreted to improve business performance. Though data science sounds complicated, it’s actually a fairly easy subject to learn, and data scientists make good money in their profession. Here are some quick facts about data science.

How is Data Science Used in Daily Life?

How is Data Science Used in Daily Life?

Data science is applied to virtually every part of our everyday lives in the form of helping businesses make better decisions to appeal to their customer and clientele base. When companies and other entities suddenly change how they deliver their services, it’s done so because their findings suggest that these new strategies, policies, etc. are a smarter business move.

The same goes for businesses selling products: machine learning in data science allows these businesses to create products that will sell. This innovative field of study has allowed companies to come up with solutions that positively impact their business. If you’re a business owner, your business will thrive with the help of a data scientist on your team.

How It Works: The Life Cycle of Data Science

How It Works: The Life Cycle of Data Science

There’s a series of steps that data science must go through for these businesses to get the most accurate results in order to make the best decisions.

1. Business Requirements

Before any analysis can begin, a company’s objectives need to be clearly defined. This will help experts make more informed decisions.

2. Data Discovery and Exploration

Data discovery is the process of gathering data, whereas data exploration is the process of organizing the data that was collected. Exploration is a rather lengthy process, as it requires removing unnecessary information in order to create useful data.

3. Predictive Modeling

This phase of the data science life cycle involves creating a data model that interprets the information found. This involves using several types of coding languages, such as SQL.

4. Testing Model

As the name suggests, this phase tests the model. Tests are performed to ensure that the models will run correctly.

5. Results

At the end of the data science life cycle is interpreting the findings. This summarizes what was found and what was a success/failure.

Ways to Get Started in the Data Science Industry

This may sound like a very complex task to achieve, but it makes more sense once you learn the coding languages and understand how they apply to data science. Data scientists are in high demand, and they can easily make a yearly salary near $100,000. To become employed as a data scientist, you must have one of the following:

  • A Bachelor’s degree in information technology (IT), computer science, business, mathematics, or another related field.
  • A Master’s degree in data, any of the above-mentioned fields, or a related field.
  • Certification from an online data science bootcamp.

Data science bootcamps aren’t degree programs like Bachelor’s and Master’s programs, but they can land you in entry-level positions working with data. These bootcamps are best suited for adults interested in a career change and may not have the time or resources to go back to school full-time. Data science bootcamps are also significantly less expensive than college degree programs, though some bootcamps can cost as much as an actual degree program.

This is just a brief overview of data science, as it’s a very complex field. If you’re interested in learning more about data science and potentially starting a new career in this field, definitely look into a bootcamp that will teach you everything you need to know about data science.

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