How to Become a Data Scientist: Steps, Skills & Salary

Every day, our digital world expands by an astonishing 402 million terabytes of data, underscoring the relentless pace of the digital age's expansion.

With this immense volume of data, the demand for skilled data scientists has never been higher. Data scientists are crucial in turning massive datasets into valuable insights that drive decisions across industries. In this guide, we’ll explore the essential steps to becoming a data scientist and the exciting opportunities that await in the field.

Key Takeaways

• Becoming a data scientist requires a degree, practical experience through internships, and specialization in areas like AI or machine learning.
• Key skills include programming, statistics, machine learning, and data visualization, with continuous learning necessary to stay competitive.
• Choosing an accredited program ensures quality education and better career prospects, with institutions like UND offering recognized standards.

What Does a Data Scientist Do?

Data scientists have a variety of responsibilities, which include, but are not limited to:

• Collecting, cleaning and processing data to ensure it is accurate and trustworthy
• Analyzing data to find patterns, trends and irregularities
• Developing and implementing machine learning algorithms to forecast and predict data
• Creating reports and visualizations to share insights and findings with stakeholders
• Working with other teams to integrate data-driven solutions into business operations
• Continuing to learn and stay up-to-date with new technologies and methodologies in data science
• Handling data ethically and adhering to data privacy regulations to maintain confidentiality and security

Key Steps on How to Become a Data Scientist

Becoming a data scientist is a path that unfolds through a series of essential steps. From diving into formal education to specializing in a specific area and gaining hands-on experience through internships, each phase is a building block, shaping and refining your expertise, and helping you reach your full potential in data science.

Earn a Bachelor’s Degree in Data Science

If you're wondering how to become a data scientist, the first step is to earn a degree in Data Science, Computer Science, Statistics, or a related field. This step lays the foundation for building proficiency in the field, offering a student-centered curriculum that introduces you to the core concepts, methodologies, and tools that are essential for navigating the complexities of data science.

As you progress through the program, you'll dive into subjects like data analysis, statistical modeling, and database management. The coursework blends theory with practical application, ensuring you gain both a deep understanding and hands-on skills necessary for success. Many programs even provide real-world learning opportunities, allowing you to apply what you've learned to actual data science challenges.

Gain Practical Experience Through Projects and Internships

Internships are a pivotal experience on the path to becoming a data scientist, offering hands-on experience with real-world data science projects. Over 66% of interns secure full-time jobs afterward, with salaries averaging $15,000 higher than those who didn’t intern. 

Working alongside seasoned professionals, interns gain insights into best practices, receive mentorship, and contribute to meaningful projects that align with organizational goals. Additionally, internships provide valuable networking opportunities, helping individuals build industry connections and increase the likelihood of securing future employment.

Specialize in a Specific Area of Data Science

Specializing in a specific area of data science allows professionals to sharpen their expertise. Whether it’s artificial intelligence (AI), machine learning (ML), natural language processing (NLP), data engineering, or business analytics, each area offers unique opportunities.

• Artificial Intelligence (AI): AI focuses on creating systems that mimic human intelligence. Applications range from self-driving cars to virtual assistants.
  • Tools: TensorFlow, Keras, PyTorch, OpenAI, Scikit-learn
• Machine Learning (ML): A subset of AI, ML enables systems to learn from data and make predictions. ML is essential for applications like predictive analytics and automation.
  • Tools: Scikit-learn, XGBoost, TensorFlow, Keras, Jupyter Notebooks
• Natural Language Processing (NLP): NLP allows computers to understand and generate human language. It’s key in applications like sentiment analysis and chatbots.
  • Tools: NLTK, spaCy, Hugging Face Transformers, GPT
• Data Engineering: Data engineers build systems to collect, store, and process large datasets. They ensure that data is clean and accessible for analysis.
  • Tools: Apache Hadoop, Apache Spark, SQL/NoSQL databases, Apache Airflow
• Business Analytics: Business analytics focuses on interpreting data to guide business decisions. It combines statistical analysis with business insights.
  • Tools: Tableau, Power BI, Google Analytics, Excel, Python/R

Consider a Data Science Bootcamp

If you're eager to begin your career in data science, a bootcamp can be a quick way to gain some of the foundational skills, such as programming, machine learning, and data visualization.

In addition, many bootcamps offer career support, connecting you with potential employers and industry professionals. However, for those seeking long-term career growth and a broader skill set, it's important to consider earning a comprehensive data science degree.

Pursue a Master's Degree in Data Science

If you're looking to become a data scientist, pursuing a master's degree in Data Science can be a strategic move to elevate your career and deepen your expertise in this field. UND programs offer a specialized curriculum, diving into advanced topics such as data mining, machine learning, predictive analytics, and artificial intelligence.

Unlike undergraduate studies that provide a broad foundation, a master's degree allows you to hone in on data science, developing a deeper understanding and specialized skills. This expertise enhances your proficiency and positions you as a subject matter expert.

Recent data reveals that about 34% of data scientists hold a master's degree, underscoring the importance of advanced education in securing a strong foothold in the field.

Skills of a Data Scientist

To excel in this field, data scientists need to master a blend of technical, analytical, and communication skills. Here are the key skills every data scientist should have:

• Programming: Proficiency in languages like Python, R, and SQL for data manipulation, analysis, and building models.
• Mathematics and statistics: Strong foundation in statistics, probability, and linear algebra to make accurate data-driven decisions.
• Machine learning: Knowledge of algorithms and techniques for developing predictive models, including supervised and unsupervised learning.
• Data visualization: Ability to communicate insights through visualizations using tools like Tableau, Power BI, or libraries like Matplotlib and Seaborn.
• Data wrangling: Skills in cleaning, transforming, and organizing raw data to ensure accuracy and consistency.
• Big data technologies: Familiarity with frameworks like Hadoop, Spark, and cloud platforms to process large-scale datasets.
• Problem-solving: Critical thinking and the ability to approach complex business problems and derive data-driven solutions.
• Domain knowledge: Understanding the specific industry or field to apply data science techniques effectively.
• Communication: Ability to explain complex technical concepts and data insights to non-technical stakeholders.

Aspiring data scientists have a multitude of avenues to hone these skills, ranging from online courses and workshops to immersive boot camps and self-directed learning resources. These platforms offer flexible learning opportunities tailored to different learning styles and levels of expertise, allowing individuals to progress at their own pace and delve deeper into specific areas of interest.

Salary of a Data Scientist

As organizations increasingly rely on data to inform important decisions and improve business processes, the volume of available data and its potential applications are expected to skyrocket. As a result, the need for data scientists to extract, analyze and interpret this vast amount of information continues to increase. 

The projected growth rate for this occupation from 2022 to 2032 is 36%, far surpassing the average growth rate for all occupations. Over the next decade, this growth is expected to result in around 20,800 job openings annually.

Data science is also a highly lucrative career path, with a median annual wage of $108,020 . While entry-level salaries may start at around $61,070, top earners in the field can command more than $184,090 annually. This reflects the high value placed on skilled data scientists in today's data-driven economy.

Why Choosing an Accredited Degree Program Matters

When considering a career in data science, one of the most important decisions you’ll make is selecting the right institution. With the growing demand for skilled data scientists, many aspiring professionals wonder: Is a degree in data science worth it? The answer often lies in the quality and credibility of the program you choose. Enrolling in an accredited degree program ensures you receive a high standard of education, setting the foundation for a successful career. 

Accreditation is a hallmark of academic excellence, and the University of North Dakota (UND) stands as a prime example. UND has been continuously accredited by the Higher Learning Commission (HLC) since 1913, demonstrating its commitment to maintaining rigorous academic standards. This longstanding accreditation reflects the quality of education students receive at UND and ensures that their degree holds significant value in the job market.

The Institutional Actions Council (IAC) reaffirmed UND’s accreditation in 2024, with the next Reaffirmation of Accreditation scheduled for 2033-34, ensuring ongoing quality and reliability.

In addition to HLC accreditation, UND is recognized by various other accrediting agencies at the college and school levels, which further solidifies the university’s standing in the academic community. These accreditations ensure that students gain expertise in data science, preparing them to excel in a rapidly evolving industry. With an accredited degree from UND, you're not only investing in your future but also securing the credibility and recognition necessary to thrive in the competitive data science field.

Conclusion 

While there may be various paths to becoming a data scientist, the approach emphasized in this article, which prioritizes formal education and continuous learning, offers a structured and reliable method to ensure students acquire the essential skills and knowledge required for success. 

UND recognizes the significance of this approach, offering data science degree programs at both the bachelor's and master's levels and providing support to help students build valuable connections in the industry. So, join us and unlock the power of data to shape the future.

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