What is Data Science & How Does It Works?

 What is Data Science & How Does It Works?

Data science is the hottest topic in today’s time, and it has all the reasons to be. Data is expanding at a tremendous rate, and if you believe a report, 90 percent of the data was generated in the last 2 years. Global data creation is expected to reach 180 zettabytes by 2025.

What Is Data Science?

Data science involves collecting, storing, organizing, and visualizing data to decipher useful information. Broadly, it is defined as the study of data, where it comes, what it signifies, and how it can be transformed into valuable insights to create business strategies.

Data science involves various disciplines and expertise areas to get a holistic, thorough, and refined view of the raw data. It also relies heavily on artificial intelligence, machine learning algorithms, and deep learning for creating models and make predictions.

A data scientist is skilled in data engineering, mathematics, advanced computing, programming, statistics, and visualization to help you sort through information and communicate relevant things.

How Does Data Science Works?

As a data scientist, you have to figure out the solution and communicate it to the stakeholders when a data problem is presented to you. The steps involved here are called the data science process. These processes form the basis of all data science training in California.

Steps In Data Science Process

Step 1: Frame The Problem: In order to provide a solution, you need to understand what your client is asking you to solve. Then, you should translate their request into a problem, which you’ll be solving.

Step 2: Collection Of Raw Data: The next step is to collect the raw data from different sources in the form of clicks, text, images, etc. You have to sort the data too to see which part helps solve the problem. Also, calculate the time you would need to collect the data in usable form.

Step 3: Data Processing (Data Wrangling): There are many errors in the raw data, such as corrupt records, duplication, missing values, and more. To make this data usable, raw data has to be cleaned first to perform the data analysis.

Step 4: Data Exploration: After cleaning the data, you have to comprehend the info contained within at a high level. You need to see the trends, patterns, and correlations in the data. Besides, you need to see the high-level characteristics and other significant information.

Step 5: In-Depth Analysis: This step is where you will be applying machine learning algorithms, statistical models, etc., to analyze data and extract valuable insights and make predictions.

Step 6: Communicate Results: The analysis and technical results are of no use if not presented in a readable and comprehensible way. For this, data visualization is an important step.  Data storytelling is an important skill you should learn during your data science training to communicate the results. Data science is rampant across all the industries from healthcare, finance, education, cybersecurity, entertainment, and more. Businesses use data science to improve their processes, innovate, create new products, and make things efficient.

Source: https://buzztum.com/what-is-data-science-how-does-it-works/

Location: United States

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