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Basic Components That Make Up Data Science

The data science domain is huge and if you want to make a career in data science, then you need to be aware of the various components that make up this widely used technology including data, programming languages, machine learning, and more. bit.ly/40uXxSu

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Basic Components That Make Up Data Science

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  1. BASIC COMPONENTS THAT MAKE UP DATA SCIENCE © 2025. United States Data Science Institute. All Rights Reserved. Data Science is a huge field encompassing various components from mathematics and statistics to domain or industry expertise. This technology is now transforming industries with a variety of applications. But what does it consist of? © 2025. United States Data Science Institute. All Rights Reserved. KEY COMPONENTS OF DATA SCIENCE These are the key components of data science: DATA These are the fuels or fundamental elements that power data science. They can be texts, numbers, images, or anything that gives information. There are two types of data: Structured: organized, formatted, searchable, and easily understood by machine learning. Example: excel sheet with name, address, contact number, etc. Unstructured: unformatted, unorganized data that cannot be processed or analyzed with traditional methods. Example: social media activity, audio files, videos, etc. PROGRAMMING LANGUAGE They are the tools to communicate with computer systems and perform all data science tasks including data wrangling, visualization, and analysis. Popular Programming languages: R, Python, SQL, C++, Java, etc. MATHEMATICS AND STATISTICS These are numerical foundations that help identify patterns and trends in data, build algorithms, and draw conclusions to find insights from data. Important mathematical and statistical concepts to learn: Descriptive statistics Inferential statistics Linear algebra, and Calculus MACHINE LEARNING This component helps computers learn from data without explicit programming. Three types of machine learning algorithms: Supervised learning Unsupervised learning Reinforcement learning DATA WRANGLING The data wrangling process cleans and prepares the raw and messy data for analysis. It includes: Cleaning data Correcting incomplete and missing values Removing duplicates Converting data into a suitable format EXPLORATORY DATA ANALYSIS It is the process of analyzing data to identify patterns, and relationships in data, and draw insights. The Data Analysis process includes: Identifying anomalies Predictive analytics Calculating descriptive statistics DATA VISUALIZATION Data visualization refers to visualizing data in the form of charts, graphs, or other visual elements to communicate complex insights in easy-to-understand visuals. It uses tools like: © 2025. United States Data Science Institute. All Rights Reserved. data science components data science career Having a strong understanding of these fundamental strong business and domain knowledge can take your along with to new heights. Are you ready to conquer this career path? fundamentals with USDSI's Master these Certified Data Science Professional (CDSP™) certification. REGISTER NOW © 2025. United States Data Science Institute. All Rights Reserved.

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