What is data science?

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Contents

  1. What is data science?
  2. What is a Data Scientist?
  3. Data science vs software engineering
  4. Why does data science matter?
  5. What is the data science process?
  6. Data science tools
  7. Data science use cases
  8. Data science skills
  9. What do data scientists make? Data scientist salary in the US
  10. Data science courses: The best way to learn data science
  11. Learn data science

Data Scientist is one of the best career choices for 2024(opens new window), both in terms of hiring demand and salary. But what is data science, and why is it such an attractive field?

Data science seeks to collect and analyze information to detect patterns, trends, or discrepancies. Businesses use data science to understand their clients better, make strategic decisions, and address challenges.

Companies in all industries have embraced data science, leading to a surge in demand for skilled data professionals. The Bureau of Labor Statistics (BLS) predicts the overall employment of Data Scientists(opens new window) will increase by 35% between 2022 and 2032.

Many entry-level Data Scientists earn six-figure salaries. But is it the right career for you? There are many factors to consider, including responsibilities, salary, and skills.

What is data science?

Data science is the study of data sets to gain new knowledge and generate actionable insights. This approach typically involves mining, organizing, analyzing, and visualizing data.

Companies use data science to answer questions and improve their operations. Data science also allows businesses to create predictive models that forecast future trends and anticipate possible outcomes of decisions.

Data science is a multidisciplinary field that combines many methodologies and tools, including:

  1. Artificial intelligence
  2. Cloud computing
  3. Computer science
  4. Machine learning
  5. Mathematics
  6. Statistics

Data Scientists often collaborate with other professionals to complete projects that require multiple areas of expertise. For instance, a Data Scientist could work with a Business Analyst to examine a company’s financial transactions and detect fraud. A Data Scientist might also collaborate with healthcare professionals to create a predictive model that uses health data to identify patients at risk of cardiovascular diseases.

What is a Data Scientist?

A Data Scientist leverages analytical tools to interpret complex or large datasets. They use the insights they gain from data to help companies make data-driven decisions, improve efficiency, and solve problems.

The type of information analyzed by Data Scientists depends on their industry and employer. For instance, Data Scientists in the finance industry often work with investment portfolios, credit scores, and other financial data. Data Scientists in veterinary care may analyze genetic data, information from clinical trials, or patient records.

The daily responsibilities of a Data Scientist vary by company, experience level, and project. Typical tasks for entry-level professionals include:

  1. Selecting the most appropriate source of data for the project
  2. Using data mining, algorithms, and other approaches to gather information
  3. Cleaning datasets by removing duplicate, incomplete, and corrupted information
  4. Creating data visualizations
  5. Presenting results to managers and stakeholders

Data science vs software engineering

Both data science and software engineering use technology to develop solutions and help businesses achieve their goals. But they’re distinct fields with different purposes, skills, and career paths.

The main goal of data science is to study datasets and gain meaningful insights. Software engineering focuses on building and maintaining computer programs, websites, and other software.

Below, we break down other differences between these fields.

Why does data science matter?

Organizations use data science to explore and improve every aspect of their business operations. This discipline allows businesses to:

  1. Assess risk: Businesses analyze historical and present data to anticipate future risks. For instance, an insurance company can evaluate car accident reports in a city and adjust their rates based on the level of risk.
  2. Drive innovation: Data science enables companies to develop ground-breaking products and solutions. Say a clothing company analyzes customer feedback and realizes that its clients want more sustainable products. Based on this insight, they could develop a new clothing line made from environmentally friendly materials.
  3. Improve processes: Organizations can leverage data science to identify inefficiencies and optimize workflows. For example, an urgent care office could evaluate patient wait times and rearrange staff scheduling to meet demand.
  4. Increase personalization: Data science lets businesses better understand each client’s needs and preferences. They can use information like browsing and purchase history to offer personalized services, such as product recommendations and targeted coupons.

What is the data science process?

The data science process is a series of steps that Data Scientists use to transform raw data into actionable insights. This methodical approach increases accuracy and consistency.

The stages in this process vary depending on the complexity of the project and the data source. Typical steps include:

  1. Identify the problem: Define a specific challenge to solve with data. For instance, an organization may want to understand why their sales have declined or learn how to improve employee productivity.
  2. Acquire data: Data mining software, customer focus groups, and other tools enable Data Scientists to collect data from relevant sources.
  3. Clean and preprocess the data: Organize the data in a standardized format, fill in missing information, and eliminate data outliers. Data Scientists may use techniques such as Extract, Transform, Load (ETL) to standardize data between sources and processing locations, such as data warehouses.
  4. Conduct exploratory data analysis: Data Scientists use data visualization software and statistical tools to spot patterns in datasets quickly.
  5. Develop machine learning models: Professionals use frameworks like TensorFlow and PyTorch to train machine learning models on datasets. These models analyze data, detect patterns, and predict future behavior.
  6. Create data visualizations: Represent data visually as charts, diagrams, and other graphics.

Data science tools

Data Scientists often work with vast, complicated datasets that would take many hours to process and analyze manually. Data science tools speed up workflows and improve accuracy.

Big Data Processing Platforms

These cloud-based platforms distribute data processing across multiple nodes or servers. These tools allow Data Scientists to process and analyze enormous datasets more efficiently. Well-known big data processing platforms include Apache Spark and Apache Hadoop.

Programming languages

The best programming language to learn depends on your career goals and the types of projects you want to work on. Data Scientists typically use Structured Query Language (SQL) to build, organize, and locate information in databases. Many data science professionals also use Python and R to manipulate, interpret, and visualize data.

Data Visualization Software

Data visualization software transforms datasets into interactive or static graphics. These visualizations make it easier for Data Scientists to notice patterns and inconsistencies. They can also use these images to share their findings and tell visual stories about data. Popular data visualization tools include Matplotlib, Microsoft Power BI, and Tableau.

Machine Learning Frameworks

Data Scientists use frameworks to design and deploy machine learning models. These frameworks contain pre-built algorithms, functions, and other tools. Professionals incorporate these parts into their own predictive models instead of starting from scratch. The best-known machine learning frameworks include PyTorch, scikit-learn, and TensorFlow.

Data science use cases

Businesses in diverse industries leverage data science to guide decision-making and solve problems. Here are three use cases that highlight this discipline’s versatility.

Fraud Detection

Data science allows banks, credit unions, and other financial institutions to evaluate transactions for signs of fraud. For example, the online payment platform PayPal(opens new window) uses data science to analyze failed payments. If the platform determines the transaction is legitimate, it uses machine learning algorithms to predict the best time to resubmit the payment.

Predictive Maintenance

Manufacturing equipment can fail or need service at inconvenient times. Data science enables companies to gather performance data with sensors and software and predict when machinery will need maintenance. The oil companies BP and Shell(opens new window) use artificial intelligence and data analytics to monitor their equipment in real time and detect faults.

Supply Chain Management

Numerous organizations use data science to increase the efficiency and reliability of global supply chains. The delivery company FedEx(opens new window) uses predictive analytics to anticipate and prevent disruptions that delay package deliveries. The FedEx Dataworks platform also gives customers real-time updates about their package’s location, temperature, and other information.

Data science skills

You can prepare for a field in this fast-growing field by earning foundational data science skills. Aspiring Data Scientists need a combination of technical and interpersonal skills to get the job.

Communication

Data Scientists must often explain their findings to collaborators, clients, and stakeholders. Strong presentation and writing skills allow you to share your knowledge in plain language. You’ll also use communication skills to tell stories with data and help leaders make decisions.

Collaboration

Data science is a highly collaborative field that often involves team projects. For instance, you may need to work with Business Analysts, Data Engineers, and Project Managers. Robust collaboration skills enable you to delegate tasks, exchange knowledge with people from diverse backgrounds, and resolve conflicts.

Data Visualization

Data Scientists use graphical representations to communicate their findings with clients and decision-makers. Understand how to use color and design principles, choose the best graphic for the project, and annotate your visualization.

Mathematics and Statistics

You don’t need to be a math genius to become a Data Scientist, but you’ll need to understand basic statistical concepts like regression analysis and time analysis. Data Scientists apply these methods to discover patterns in data and build data models.

What do data scientists make? Data scientist salary in the US

Data science can put you on the fast track to earning a six-figure salary, even if you don’t have a college degree.

According to Indeed, the base salary for Data Scientists in the United States ranges from $82,000 to $190,000. The average base salary is $124,000.

Entry-level Data Scientists with less than one year of experience earn an average base salary of $105,000. This pay quickly increases to an average of $143,000 for professionals with three to five years of experience.

Many employers also offer bonuses and other benefits, which can significantly increase your compensation package. According to Levels.fyi(opens new window), Data Scientists earn an average of $8,000 per year in stocks and $3,000 in bonuses.

If you want to live on the coast, you’ll find plenty of lucrative career opportunities in data science. Based on data from Indeed, these are the highest-paying cities for Data Scientists:

The average Data Scientist salary can also vary drastically between sectors. According to the BLS(opens new window), here are the top-paying industries for Data Scientists:

  1. Clothing and Clothing Accessories Retailers: $164,000
  2. Computer and Peripheral Equipment Manufacturing: $158,000
  3. Semiconductor and Other Electronic Component Manufacturing: $148,00
  4. Automotive Parts, Accessories, and Tire Retailers: $143,000
  5. Securities, Commodity Contracts, and Other Financial Investments and Related Activities: $134,00

Data science courses: The best way to learn data science

The demand for Data Scientists is high, so many employers consider candidates with diverse backgrounds and experiences. Here are three ways to break into this growing field.

College degree

Some aspiring Data Scientists earn a bachelor’s degree in computer science, mathematics, statistics, or a related field. A degree can help you gain foundational knowledge and skills.

However, college is an expensive and time-consuming investment. Also, many employers don’t require a degree for data science positions, so a college education isn’t necessary.

Online courses

Websites like Coursera and edX offer online courses and certificates in data science. These classes allow you to study independently and gain new skills. However, they don’t provide hands-on experience or career guidance.

Professional apprenticeship

A professional apprenticeship combines on-the-job training with a structured curriculum. Apprentices develop essential skills and build their professional network as they work for top companies. They also receive personal mentorship and career support.

Learn data science

As businesses increasingly rely on data to make decisions, data science has become one of the hottest careers. Professionals in this field can enjoy six-figure salaries, remote work arrangements, and other benefits–without a college degree.

Develop the skills you need to launch your career with Multiverse’s advanced analytics apprenticeship. You’ll earn a competitive salary as you build a strong foundation in data science. You’ll also create a personal portfolio, participate in hackathons, and make industry connections. Complete our fast application(opens new window) today to get started.


Team Multiverse

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