Data science plays an integral role in many organizations. Data Scientists use their technical skills and training to create complicated quantitative algorithms to organize and synthesize huge volumes of data. These professionals use a range of tools to do so, such as Microsoft Excel, Python, SAS, MATLAB, and Tableau. Their findings are then used to answer questions and inform strategies for their organization. If you’re still deciding whether learning data science is right for you, consider reading on to discover the myriad uses of data science tools and techniques. Not only will learning data science open up a number of new career opportunities, but it also has many potential personal uses. 

What is Data Science? 

Data science is a broad field that encompasses the disciplines of mathematics, computer programming, and artificial intelligence (AI). Data professionals such as Data Scientists and Data Analysts use advanced techniques like machine learning algorithms to find patterns in a vast amount of information. This process can provide actionable insights to stakeholders, from Product Managers to C-suite executives.

Today nearly every sector requires data science expertise, whether public or private. Among the top sectors where data science is critical are health and wellness, retail, web and application development, banking and finance, and governmental agencies. The field continues to project dramatic growth over the next decade; Glassdoor even listed Data Scientist as number three in its 50 Best Jobs in America in 2022.

Read more about what Data Science is and why you should learn it.

Professional Uses for Data Science 

If you’re interested in entering the field of data science, there are two main career paths you may explore: working as a data science professional as a Database Developer, Data Scientist, or Data Analyst, or a job more focused on analytics in a role like a Business Analyst. Regardless of which path you wish to pursue, careers in the data sciences require that you have a background in a range of skills, such as foundational training in data analytics and programming, as well as data mining, management, analytics, and visualization.

Learning data science can prepare you for a range of high-paying jobs. The following are just a few of the most common jobs in data science you may wish to explore:

• Data Scientists work with processed and raw information to uncover patterns in these numbers. They then present their findings to their organization to help them make more informed business decisions. These professionals’ role is typically more technical than a Data Analyst.

• Data Architects create data solutions and new database systems. These professionals also search for ways to improve how an existing system performs.

• Machine Learning Engineers design data funnels and software solutions. They use their training in programming, software engineering, and statistics to create machine learning systems.

• Infrastructure Architects ensure that various business systems are performing optimally and are able to support new system requirements and technologies. 

• BI Developers devise strategies for those at their business to quickly locate the information they need to make smarter business decisions. They work with BI tools and apps so the end-user can easily work with their system.

• Statisticians gather, analyze, and interpret data. They search for relationships and trends in these numbers that can be valuable for decision-making within their organization. Statisticians also may create new data-collection processes, as well as share their data findings with stakeholders. 

• Data Analysts work with large datasets to uncover valuable information for their organization. These professionals may also track web analytics, prepare reports, and create accessible data visualizations that communicate what they found in their analysis.

• Data Engineers are involved with real-time processing and batch processing of data. They also create and maintain data pipelines, ensuring an organization’s data ecosystem is interconnected. 

While each of these professions uses data science in a slightly different manner, there are some core responsibilities these professionals share and some common ways they use data science for their professional needs:

Mine & Analyze Data

One of the most common tasks a Data Scientist performs is data mining. Data mining is the act of using automation and computers to find information in datasets. This component of the data analytics process involves searching large datasets to find relationships, patterns, or trends. Data Scientists create advanced algorithms for the data to find these patterns. The results of these algorithms are then applied to create analytical models. Data mining typically extends beyond preliminary searching into using the data to analyze future probabilities and create actionable analyses.

Create Algorithms

Data Scientists work with a range of algorithms when searching data for patterns. The following are some of the most common algorithms these professionals rely on:

• Linear regression algorithms, which allow them to use the independent variable’s value to predict the dependent variable’s value.
• Data preparation and processing algorithms. 
• Logistic regression, which helps Data Scientists work with discrete values rather than continuous values.
• Optimization algorithms designed to estimate parameters, such as Least-Squares, Stochastic Gradient Descent, and Newton’s Method.
• Machine learning algorithms.
• Decision trees, which are useful for solving prediction and classification problems. 
• KNN, or K-Nearest Neighbors. This algorithm allows Data Scientists to scan a dataset to locate the K most similar or nearest neighbors to a particular data point. This information can then be used to predict the outcome.

Write Code

Most Data Scientists use several programming languages to quickly perform tasks like finding value in a dataset and designing models that can offer accurate predictions. These professionals often work with several coding languages since some languages are better than others at performing specific tasks:

• Python is a popular coding language for Data Scientists because of its wide range of data science libraries, as well as its versatility. This general programming language offers a simple syntax and is easy to read, which makes it a great language for those who are new to data science.

• SQL, or structured query language, is another popular programming language. It allows users to communicate with databases and create queries to find information within the data. In data science, SQL is a powerful tool that allows users to collect data, then perform edits on them later.

• R is a programming language commonly used for data manipulation, statistical analysis, and data visualization. This language is growing in popularity with Data Scientists because of how flexible it is in handling complicated analyses on vast datasets. This language also has a range of packages devoted to machine learning algorithms. It is a popular coding language for organizations that wish to incorporate predictive analytics solutions. 

Other Uses for Data Science

In addition to the many professional applications for data science, this skill set can also be used outside of the job context. The following are some real-world applications of data science that can help you in your personal life:

Education 

Data science has applications for helping various stakeholders in the educational system, from students to teachers to administrators. Data science can help students improve their academic performance by allowing them to analyze educational factors like the effectiveness of study materials, books, and other learning strategies. Data science also can help teachers design and update curricula based on students’ performance. Even teacher performance can be analyzed with the help of students’ reviews and test scores.

Filtered Internet Searches

Search engines like Google perform searches that display the keyword and the most relevant topics related to it. Google is able to find the most relevant and searched word that’s related to this keyword. This sort of filtered internet search is just one application of data science. Google gathers and saves search history data, which can then be analyzed and visualized. It then applies filtering techniques that filter the data to see how often that keyword was searched for, as well as related topics, so that it can provide you with the most useful search results. Most Google users don’t realize that even their internet curiosity is facilitated by data science.

Entertainment

Data science technologies are integral in educational platforms like Netflix and Spotify. Data science is responsible for studying the music you prefer and the shows you’re interested in watching and storing this information. By studying customer behavior patterns and preferences, Netflix can create and curate targeted shows that will likely appeal to its customers. Data science techniques study customers’ data so that Netflix can customize users’ watchlists based on their viewing habits. Spotify uses customer data to offer tailored weekly playlists based on each user’s musical preferences and behavior patterns.

Learn Data Science Skills with Noble Desktop

Because data science is a broad field, targeted training can prepare you for a data-centered position or even help you choose a specific role. You might think you’ll need a four-year data science degree, but this isn’t necessarily so. The bootcamp or certificate educational model has become increasingly popular for data professionals, thanks to features like small class sizes, hands-on training from industry experts, and individual mentoring. Noble Desktop offers a wide range of data science programs to help get you started.

• Data Science Certificate - The comprehensive Data Science Certificate provides all the skills required for entry-level data science, data analytics, or software engineering roles. Students learn how to write complex queries and build machine learning models while preparing a portfolio on a real-world basis. Skills covered include Python, SQL, NumPy, Pandas, and Jupyter Notebook, to name a few.

• Data Analytics Certificate - The comprehensive Data Analytics Certificate program offers the perfect training ground for Data Analysts, Business Intelligence Analysts, and Marketing Analysts. With a heavy emphasis on Tableau data visualization software, you’ll learn skills like Python programming, SQL, and machine learning, among others. Registrants of the Data Analytics Certificate or Data Science Certificate can also attend Noble’s Power BI Bootcamp at no additional charge.

• Python for Data Science Bootcamp - The Python for Data Science Bootcamp covers everything from programming fundamentals to data visualization. Students can save by taking this course as part of Noble’s Data Science Certificate, Data Analytics Certificate, or FinTech Bootcamp.

Check out all the Noble data science classes and bootcamps for additional options, like the Python Data Science & Machine Learning Bootcamp, Python for Data Science Bootcamp, or Python Machine Learning Bootcamp.

Key Takeaway

• Data science involves creating quantitative algorithms that allow users to organize and synthesize vast datasets to find answers for their organization.
• Data Scientists use a range of tools, such as data mining and algorithms, to search data. They also apply programming languages like R, Python, and SQL when analyzing vast volumes of data.
• In addition to its professional applications in many sectors, data science is also a powerful tool that can help educators connect with students, improve search engine results, and even provide more customized entertainment options on platforms like Netflix or Spotify. 
• Noble Desktop offers comprehensive data science training in the in-person and live online format.

How Does Learning Data Science Compare with Other Skills?

Data science is the act of extracting clean information from raw datasets to find actionable insights. This information can then be shared with organizational stakeholders and used to answer questions and drive changes. An important factor that goes into deciding to learn a new skill is researching complementary and related skills. Doing so will help you make more informed decisions about what you want to learn or whether learning a specific skill is the right decision for you. You will also find out what kind of skills you may want to acquire to complement your training later on. Read on to find out more about related data science training options such as machine learning, Python, SQL, and data visualization.

What is Data Science?

Data science is a broad field that encompasses the disciplines of mathematics, computer programming, and artificial intelligence (AI). Data professionals such as Data Scientists and Data Analysts use advanced techniques like machine learning algorithms to find patterns in a vast amount of information. This process can provide actionable insights to stakeholders, from Product Managers to C-suite executives.

Today nearly every sector requires data science expertise, whether public or private. Among the top sectors where data science is critical are health and wellness, retail, web and application development, banking and finance, and governmental agencies. The field continues to project dramatic growth over the next decade; Glassdoor even listed Data Scientist as number three in its 50 Best Jobs in America in 2022.

Read more about what Data Science is and why you should learn it.

What Can You Do with Data Science Skills?

Data science has so many applications in different industries that a comprehensive review could fill a book. Professionals as diverse as Business Analysts, Machine Learning Engineers, and Enterprise Architects use data science in their day-to-day activities.

Top sectors for data science include banking and finance, marketing and advertising, and healthcare:

• Banking, Financial Services & Insurance (BFSI) - Business Analysts and Data Scientists use data for everything from fraud detection to customized financial advice. Machine learning algorithms can assist with risk analytics, stock trading, and other tasks.

• Marketing & Advertising - Data Analysts and Marketing Analysts use data science in advertising to create targeted ad copy, recommend products and services, and leverage social media platforms. Programming languages like Python and R, often key to data science positions, help experts analyze data and make recommendations.

• In healthcare, Data Scientists create algorithms to create care plans and improve patient services. Using data analysis in medical imaging can help care providers with diagnoses and treatment decisions.

Data science has proven crucial to many other sectors, from retail and manufacturing to the public sector. If you want to combine challenging work with job security, start with data science.

Skills Related to Data Science

Data science is a broad category of related skills, programming languages, and other tools for managing and manipulating data. If you’re interested in studying data science, you likely will need to be proficient in several programming languages, such as SQL and Python. A background in machine learning is also essential to succeed in data science. In addition, because the insights discovered in data aren’t actionable until they are communicated to a larger audience, data visualization plays an integral role in data science.

Machine Learning

Machine learning involves creating computer systems that can adapt and learn using statistical models and algorithms. These models allow the computer system to make inferences from data patterns to learn without requiring explicit instructions. With the help of machine learning, Data Scientists can automatically examine and analyze huge chunks of data. They can also automate the data analytics process and offer real-time predictions without relying on human input. The data can also be trained to provide real-time predictions.

If you’re interested in learning more about machine learning, Noble Desktop’s Data Science Certificate provides hands-on training on creating machine learning models.

SQL

SQL, or structured query language, is a popular computer programming language used to communicate with and query databases. SQL is the standard language for working with relational database management systems, such as Oracle, Access, or Microsoft SQL Server. Microsoft SQL Server is one of the most widely used SQL databases by Data Scientists because of how helpful it is for managing databases and creating data warehouses. Another reason SQL Server is popular in data science is because it’s compatible with various BI tools, like Microsoft Power BI.

Those who know SQL can design statements that perform tasks like retrieving or updating the data from a database. Even though the majority of database systems use SQL, many of these have unique proprietary extensions that are specific to their system. Core SQL commands like “delete,” “update,” or create” can be used to accomplish most database tasks. Noble Desktop’s SQL Bootcamp is a great way to learn more about how to aggregate data, write SQL queries, and join tables.

Python

Python is a high-level, open-source language used for object-oriented programming. It’s one of the most popular languages in data science due to its functionality for statistics, science, and math tasks. Python is widely used in research and science communities because of how easy its syntax is to understand and use, as well as its capacity to perform rapid prototyping. In addition, the deep learning frameworks and scientific packages offered with Python’s APIs help to make this language versatile for a range of uses. 

Machine learning is an important aspect of data science, and Machine Learning Scientists also gravitate to Python. This language helps them create algorithms for network security and fraud detection. Python’s extensive collection of libraries helps Data Scientists easily and quickly solve complicated business problems. Noble Desktop’s Python Developer Certificate is a great learning option for those interested in studying object-oriented programming, APIs, and sorting algorithms.

Data Visualization 

Data visualization involves designing visual representations of data so that a general audience can see and understand trends, patterns, or variations in the numbers. Data visualizations are a great tool for helping to communicate any meaningful insights contained in data. Data Scientists use data visualization for a range of tasks, such as cleaning and checking data, exploring it, and conveying results to business stakeholders. Most Data Scientists focus more on the numerical calculations driving data visualizations than the graphs and charts used to impart results.

Effective data visualizations tell visual stories. They break down abstract data and findings into a form that’s easier to understand, one that showcases the important findings. This often involves removing any noise or distractions from the data and drawing attention to the more pertinent aspects of the information. Data Scientists who create data visualizations are able to do more than just communicate data findings; they can also use visualizations to monitor how a particular model performs at the evaluation stage, as well as to adjust any hyperparameters. Effective data visualizations show correlations between various dataset features and can be used to validate model assumptions.

If you want to learn how to visualize data, Noble Desktop offers a Tableau Bootcamp that provides hands-on training from experts.

Learn Data Science Skills with Noble Desktop

Because data science is a broad field, targeted training can prepare you for a data-centered position or even help you choose a specific role. You might think you’ll need a four-year data science degree, but this isn’t necessarily so. The bootcamp or certificate educational model has become increasingly popular for data professionals, thanks to features like small class sizes, hands-on training from industry experts, and individual mentoring. Noble Desktop offers a wide range of data science programs to help get you started.

• Data Science Certificate - The comprehensive Data Science Certificate provides all the skills required for entry-level data science, data analytics, or software engineering roles. Students learn how to write complex queries and build machine learning models while preparing a portfolio on a real-world basis. Skills covered include Python, SQL, NumPy, Pandas, and Jupyter Notebook, to name a few.

• Data Analytics Certificate - The comprehensive Data Analytics Certificate program offers the perfect training ground for Data Analysts, Business Intelligence Analysts, and Marketing Analysts. With a heavy emphasis on Tableau data visualization software, you’ll learn skills like Python programming, SQL, and machine learning, among others. Registrants of the Data Analytics Certificate or Data Science Certificate can also attend Noble’s Power BI Bootcamp at no additional charge.

• Python for Data Science Bootcamp - The Python for Data Science Bootcamp covers everything from programming fundamentals to data visualization. Students can save by taking this course as part of Noble’s Data Science Certificate, Data Analytics Certificate, or FinTech Bootcamp.

Check out all the Noble data science classes and bootcamps for additional options, like the Python Data Science & Machine Learning Bootcamp, Python for Data Science Bootcamp, or Python Machine Learning Bootcamp.

Key Takeaway

• Data science is a field that draws from disciplines such as artificial intelligence, math, and computer science. 
• Data science has professional applications in a range of industries. It’s used in the banking, financial services, and insurance sector, marketing and advertising, and healthcare.
• Data Scientists use a variety of tools and programming languages to perform tasks. If you’re interested in studying skills related to data science, you may wish to learn SQL, Python, machine learning, and data visualization.
• If you want to learn more about data science, Noble Desktop’s in-person and live online courses are an excellent learning opportunity.