What Is a Data Scientist? A Comprehensive Overview

Data science sits at the intersection of computer science, statistics, and domain-specific knowledge. It involves using algorithms, tools, and visualizations to extract meaningful insight from data.

Data science encompasses key techniques like data collection, model deployment, data modeling, and continuous monitoring of models. It also uses complex statistical methods such as regression, classification, and decision trees, enabling businesses to identify complex relationships and patterns from data.

In our data-driven world, data scientists help organizations navigate uncertainties and make informed choices. Through data science, businesses can predict future conditions and take steps to leverage opportunities and minimize risks.

Managers can also identify bottlenecks in operations and implement corrective measures for better performance. Organizations can analyze and understand their customers’ needs, preferences, and habits. As a result, they can produce tailored products and services that better meet their clients’ needs.

This article explains what a data scientist is, including their skills, responsibilities, and pay. We’ll also explain how to become one.

Table of contents:

• What is a data scientist?
• How data scientists help businesses
• What skills do data scientists need?
• How to become a data scientist
• Data scientist salary and hourly rate

What is a data scientist?

A data scientist’s job is to use statistical techniques to sift through vast amounts of data, derive meaningful insights, and help organizations make effective decisions. This professional typically has a background in artificial intelligence (AI) technologies like machine learning algorithms, statistical analysis, computer science, and programming.

Data scientists might perform simple tasks like looking at user behavior on a website to suggest improvements or analyzing sales data to forecast future trends like demand and supply. These professionals can also do more involved tasks like data collection, cleaning and validation, modeling and analysis, and interpretation of results. They may use machine learning and deep learning models to predict patterns and trends.

Data scientists typically have a good grasp of one or more programming languages. Python is a popular programming language because of its simple syntax, extensive libraries and data visualization tools. They might also use open-source machine learning frameworks like TensorFlow and PyTorch to analyze large data sets. Additionally, data scientists often have a solid understanding of Structured Query Language (SQL) for database management.

The data scientist role is multifaceted, requiring individuals to collaborate with different parties like data engineers and business intelligence professionals who prepare data—and stakeholders who rely on their insights to make business decisions. The goal of data scientists is to solve real-world problems using data.

Why is data science important?

Businesses increasingly rely on data scientists to transform large amounts of data into actionable insights. Data science focuses on analyzing vast data sets to identify patterns, trends, and relationships, aiding business leaders in making informed decisions.

In health care, data science plays a pivotal role—from predicting disease outbreaks to aiding in drug discovery and developing personalized treatment plans. Financial sectors use data science both for spotting fraudulent activities and also for credit scoring and algorithmic trading.

E-commerce platforms harness data science to understand customer behavior, tailor product offerings, manage inventory, and drive recommendation systems. And in manufacturing, data science facilitates risk management, predictive maintenance, quality assurance, and supply chain optimization by anticipating market fluctuations.

Is data science the same as data analytics?

While both data science and data analytics operate within the data realm, they serve distinct purposes and come with unique responsibilities.

Data analytics primarily focuses on extracting insights from historical data to identify patterns, trends, and relationships. Its main goal is to provide answers to specific business questions, often working with structured data but not limited to it.

Data science, on the other hand, encompasses a broader set of tasks. It employs a range of advanced tools and methodologies—including AI, machine learning, deep learning, statistical analysis, data mining, and programming—to discover hidden insights in both structured and unstructured data.

A data analyst often reviews past data to provide reports on trends and patterns, leveraging visualization tools. In contrast, a data scientist dives deeper into data, applying AI, mathematical and statistical techniques to address multifaceted business challenges.

For instance, while an analyst might detail which products sold best in the previous month, a data scientist could predict which products will see increased demand in the upcoming months. While there’s a certain degree of overlap in their roles, the objectives and depth of their explorations into data typically distinguish them.

How data scientists help businesses

With their technical expertise and knowledge, data scientists can help businesses in a number of ways:

• Decision-making. By analyzing past business performance, data scientists furnish critical insights to steer strategic planning. From marketing strategies to risk assessments and novel product ideation, their insights inform stakeholders’ choices. Their rigorous data cleaning and validation ensure the foundation of these decisions is accurate and reliable.
• Predictive analysis. Data scientists use machine learning models to analyze large data sets and discover patterns and trends. With this ability, data scientists can forecast things like consumer purchasing behavior and product demand and supply. Predictive models give businesses a competitive edge, enabling them to boost their performance.  
• Customer insights. Data scientists can dissect customer data, analyzing their purchasing behavior, tastes, preferences, and pain points—and collecting valuable insights. Businesses can use information from these analyses to tailor their products and services and optimize their marketing strategies.
• Operational efficiency. Beyond pinpointing areas ripe for automation, data scientists also spotlight avenues for process optimization, promoting enhanced efficiency as well as waste and cost reduction.
• Product development. Data scientists can perform intensive market research and analyze consumer behavior, tastes, and preferences. As a result, they can help organizations understand what products or services their customers want, leading to more successful product launches.
• Risk management. Data scientists steer businesses through uncertain waters by breaking down various operational, financial, and reputational risks. These analyses craft a road map for organizations to brace for impending challenges or market changes.
• Personalization. Data scientists use their statistical, machine learning, and AI expertise to analyze user interactions, needs, and preferences. By doing so, they enable businesses to deliver personalized content, ads, and shopping experiences. Data scientists can also help with segmentation—dividing customers into smaller groups based on shared characteristics—which are easier to target and satisfy.

What skills do data scientists need?

Data scientists constantly use statistical analytical techniques, machine learning, and other advanced tools for data analysis. As a result, data scientists must possess technical skills and competencies to thrive in their role.

• Programming language skills. An effective data scientist understands one or more programming languages well. Mastering languages like Python, SAS and R is preferred because of their extensive use in data science. Data scientists can use their programming experience to analyze data, build models and automate various tasks.
• Understanding of machine learning and algorithms. Data scientists should be familiar with AI technologies like deep learning, neural networks, and supervised and unsupervised learning. Data scientists use machine learning algorithms to analyze large data sets and discover patterns, relationships, and trends to help stakeholders make informed choices.
• Data visualization skills. Data scientists know how to present data visually, which helps other stakeholders gain insights. These professionals can use tools like Microsoft Excel, Power BI, and Tableau to convey information effectively to business leaders.
• Big data skills. Knowing how to process big data and manage big data processing software like Apache Spark and Hadoop is also necessary.
• Statistical analysis skills. An effective data scientist also requires a solid foundation in mathematics and statistics. Mastering these subjects helps data scientists understand complex patterns and relationships and make accurate predictions.
• Communication skills. Data scientists must know how to communicate with nontechnical stakeholders. This includes converting complex data findings into actionable and understandable insights that these individuals can use for decision-making.
• Data wrangling skills. Data isn’t always clean or organized. Data scientists must know how to clean, organize and transform raw data into a more usable format that’s ready for analysis. Data cleaning may involve removing duplicates, filtering unwanted outliers, handling missing records, and fixing structural errors.

Data scientist salary and hourly rate

Data scientists are among the most sought-after professionals, and their compensation reflects this demand. According to the BLS, the median annual salary for data scientists is $100,910. However, this figure can fluctuate based on different factors, such as experience, educational background, specialization and location.

Additionally, data scientists typically earn an average hourly rate of $25 to $50 on Upwork. Rates on the higher end may reflect the pay for seasoned professionals who’ve carved a niche for themselves in specific industries.

As businesses continue to understand and harness the power of data, the value placed on these professionals and their compensation should increase.

How to become a data scientist

Data science can be a rewarding and engaging career. In this role, you will help businesses solve complex problems and make better decisions. You will also work with exciting and emerging technologies.

With the U.S Bureau of Labor Statistics (BLS) predicting data science jobs to grow by 36% between 2021 and 2031, the future of the data science role is bright.

We explain the steps to pursue a data science career.

Steps to become a data scientist:

1. Lay a strong foundation
2. Acquire programming skills
3. Understand data science tools
4. Acquire practical experience
5. Build a portfolio
6. Apply for jobs

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1. Lay a strong foundation

Start by considering formal education, such as a bachelor’s degree or master’s degree in fields like statistics, computer science, mathematics, or related disciplines. These programs provide a deep understanding of foundational concepts and algorithms essential for data processing and analysis.

Prioritize learning mathematical and statistical techniques, including probability, regression, linear algebra, and foundational calculus. As you progress, delve into AI technologies like machine learning algorithms, deep learning, and neural networks to understand their inner workings and significance in data science.

While a degree offers structured learning and essential theoretical knowledge, remember that many crucial data science skills, like data wrangling and big data management, are sharpened through hands-on experience and tackling real-world challenges.

2. Acquire programming skills

Coding skills are essential for data scientists to analyze data, automate tasks, and develop machine learning models.

Prioritize learning Python, as it’s extensively used in data science and supports a plethora of libraries, such as Pandas and NumPy, designed for data analysis and manipulation. Additionally, a solid grasp of SQL is crucial as it enables efficient querying and management of data in relational databases.

Beyond traditional classes, online platforms offer courses tailored to various aspects of programming and data science. Udemy, Coursera and Udacity are just a few popular platforms, but there are numerous other resources available to assist in your learning journey.

3. Understand data science tools

Data scientists have a plethora of tools at their disposal, and the ones they choose often depend on the specific task at hand.

MATLAB is favored for numerical computing, while Microsoft Excel is versatile for basic data analytics. Tools like Tableau help visualize data for other stakeholders. BigML offers a user-friendly way to delve into machine learning.

To become proficient, consider downloading these tools and experimenting with personal projects. Delve into their documentation for a deeper understanding.

Platforms like Microsoft Excel and MATLAB boast expansive communities, which can be invaluable for learning. Additionally, tutorials are available online, with platforms like YouTube offering in-depth walkthroughs on many data science tools.

4. Acquire practical experience

After learning foundational theories and sharpening your programming skills, the next step is delving into the practical realm of data science.

Start your journey with personal data science projects. Consider making use of publicly available data sets to practice and hone your skills. This offers a deeper understanding of how theoretical concepts translate into real-world scenarios, and also equips you with tangible examples to showcase in your portfolio. Dive deep into these data sets, employing statistical methodologies and machine learning techniques to discover latent patterns and insights.

Consider internships at organizations that resonate with your aspirations. An internship offers the chance to observe and absorb from experienced data scientists and also provides insights into the unique challenges and intricacies of the industry. You’ll get to witness firsthand how data-driven insights play pivotal roles in shaping various facets of business.

Actively taking part in hackathons and data science competitions can also boost your practical prowess. Platforms like Kaggle routinely host challenges that can stretch your analytical limits. Engaging in these competitions allows you to tackle genuine problems, foster connections with fellow data aficionados, and potentially catch the eye of future employers.

5. Build a portfolio

As you reach a level of proficiency in data science and related AI technologies, consolidate your accomplishments in a portfolio. A well-curated portfolio serves as a tangible testament to your expertise, allowing prospective employers or clients to gauge the breadth and depth of your skills.

By incorporating real-world projects into your portfolio, you provide concrete examples of your competencies, showcasing both your theoretical knowledge and also your practical problem-solving abilities. A portfolio can be an invaluable tool during job applications or client pitches, as it offers a direct glimpse into the value and insights you can bring to an organization.

6. Apply for jobs

Armed with your portfolio and the skills you’ve honed, you’re ready to step into the dynamic world of data science. While traditional job applications are a good starting point, if you have a penchant for autonomy and adaptability, exploring freelance opportunities on platforms like Upwork could be an alternative.

Data science offers different pathways, each promising its own unique blend of challenges and rewards. Choose the path that resonates with your aspirations and professional goals.

Embrace the benefits of data science

Data scientists help business stakeholders make sense of data. These individuals can analyze and extract actionable insights from large data sets. As a result, they can help enhance decision-making, risk management, operational efficiency and product development.

If you’re looking for data scientists to help you discover patterns, trends, and relationships, Upwork can connect you to the right data science professionals.

And if you’re a data scientist looking for work, consider kick-starting your career on Upwork. With data science projects posted regularly, you can find a job that aligns with your skills and earn extra income.

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Disclaimer: Upwork does not control, operate, or sponsor the other tools or services discussed in this article, which are only provided as potential options. Each reader and company should take the time to adequately analyze and determine the tools or services that would best fit their specific needs and situation.