How to Get Into Data Science: Skills & Tools to Learn, Career Options, Salary Guide 2025

In today’s tech-driven world, data science has quickly become a game-changer. Whether it's shaping the apps you use or helping businesses make smarter business decisions, too, it’s at the heart of modern innovation. So, what exactly is data science?

In this guide, we’ll break down the essentials, from what it means to be a data scientist to the key skills you need to thrive in this growing field. If you're curious about diving into the world of data, you're in the right place!

What is Data Science?

So, what exactly is data science? It's a mix of computer science, stats, and real-world knowledge that helps turn raw data into useful insights. It involves using programming, machine learning, and other data visualization software to transform raw data into actionable information.

Data scientists use tools like programming, machine learning, and data visualization to solve problems and find patterns. To succeed, you’ll need skills in data analysis, machine learning, and clear communication—basically, making complex info easy to understand and act on.

Key skills for a data scientist include strong abilities in data analysis, machine learning techniques, visualizing complex data patterns, and effectively communicating findings. These skills help make complex data understandable and useful in real-world applications.

Why data science?

Data science plays a key role in everyday tech, from personalizing your Amazon purchases to curating your Facebook feed and Netflix recommendations. It also impacts public health by improving disease tracking and monitoring through wearable tech. Plus, it helps companies and consumers save money by optimizing profit margins and enhancing efficiency. Whether it’s improving lives or driving business success, data science powers many of the innovations we interact with daily.

Explore Career Options in the Data Science Field

The demand and job outlook for data scientists is rapidly increasing, with projected employment growth of 36% between 2023 and 2033, much faster than the job outlook for other professions. On average, around 20,800 new data scientist positions are expected to open annually throughout the decade. This surge is driven by the growing need for data-driven insights in various industries, creating a promising job market for skilled professionals in this field.

Each data science role offers distinct responsibilities and salary expectations, varying by country. Here's a look at the most prominent positions:

How to Get Started in Data Science

1) Via Official Educational Pathways

Many students begin with a bachelor’s degree in computer science, mathematics, statistics, or information technology to explore the educational pathways for data science careers. These foundational degrees help build skills in algorithms, statistical models, and data handling, which are critical for data science roles.

• Bachelor’s Degree: A solid foundation in computer science or a related field is essential. Common subjects include algorithms, databases, probability, and programming.
• Master’s Degree: Many data scientists advance their careers by pursuing a Master’s in Data Science or related fields such as Business Analytics, Machine Learning, or Artificial Intelligence. These programs focus more on advanced analytical techniques, real-world applications present data, and machine learning algorithms.
• PhD: For those who want to focus on research or academic roles, pursuing a PhD in data science or related disciplines (e.g., machine learning or computational statistics) is an option. PhDs focus on cutting-edge research, often involving complex data modeling and artificial intelligence applications.

2) Online Learning and Certifications

For professionals looking to upskill or transition into data science careers, there are numerous online courses and certifications that provide both foundational and advanced knowledge:

• Coursera offers certificates like the IBM Data Science Professional Certificate.
• Udemy and edX have extensive courses on machine learning, Python for data science, and more.

Google and AWS provide specific certifications focused on cloud-based data science tools and platforms. Some of the top certifications include:

• IBM Data Science Professional Certificate: Covers a wide range of topics, including data analysis, machine learning, and Python programming. This certification is highly regarded in the industry and helps demonstrate hands-on expertise.
• Google Data Analytics Certificate: This certificate focuses on data analysis skills using tools like Google Sheets, SQL, and R. It's ideal for beginners and provides a strong foundation in data-driven decision-making.
• Microsoft Certified: Azure Data Scientist Associate: This certification is for professionals working with Azure cloud services. It emphasizes the application of machine learning and data models in cloud environments.
• AWS Certified Data Analytics - Specialty: For those interested in Amazon Web Services (AWS), this certification focuses on designing and deploying scalable, secure, and cost-effective analytics solutions on the AWS platform.

3) Earn Industry-Specific Certifications

For those looking to specialize in healthcare data management systems and science, the Certified Professional in Healthcare Information and Management Systems (CPHIMS) is an excellent certification. It covers critical areas like clinical informatics, system implementation, and healthcare IT, equipping professionals to manage health information systems effectively​(HIMSS).

In the finance sector, professionals can aim for the Certified Financial Data Professional (CFDP) certification. This certification focuses on using data science techniques for financial analysis, fraud detection, and risk management. It helps bridge the gap between the finance industry and data science, enhancing skills in big data analysis.

4) Acquire Essential Data Science Skills:

Beyond formal education, data scientists require strong technical and communication skills:

• Programming: Proficiency in Python or R is crucial, along with knowledge of SQL for database management.
• Statistics & Probability: Data scientists, business analysts, and data architects must understand statistical methods to draw meaningful conclusions from data.
• Data Visualization: Tools like Tableau or Power BI allow data scientists to communicate their findings effectively.
• Machine Learning: Algorithms and techniques like regression, clustering, and deep learning are key.

While technical expertise is essential for data analysis, soft skills are just as crucial for a successful career in data science. Here are three key soft skills that every data scientist should master:

• Problem-Solving: Data scientists need to turn complex business problems into solvable data tasks. This requires understanding the core issues a business faces, asking the right questions, using interpreted data sources, communicating insights, and breaking down the problem into actionable insights through data analysis.
• Storytelling with Data (Communication): It’s not just about analyzing data; it’s about visualizing data and communicating findings in a way that business analysts and non-technical stakeholders can easily understand. Crafting a compelling narrative around the data helps businesses make informed business decisions and align them with broader strategies.
• Teamwork and Collaboration: Data scientists often work closely with cross-functional teams, including product managers market research, business analysts, engineers, and marketing professionals. The ability to collaborate and integrate insights into larger projects is key to driving success across various departments.

5) Get Familiar with Tools and Technologies Used in Data Science

When diving into data science, you must familiarize yourself with the key tools and technologies that help streamline data collection, analysis, visualization, and machine learning tasks. Here's a breakdown of some of the most popular tools and programming languages used by data scientists, along with their essential roles:

Programming Languages

Data Visualization Tools

Machine Learning Frameworks

Data Storage Solutions

6) Via Real-World Experience

To gain real-world experience after completing your coursework, look for entry-level positions or internships tailored to data science beginners. Entry-level roles often include junior data scientist, data analyst, data engineer, business intelligence analyst or intern positions. These positions provide valuable exposure to working with real datasets, collaborating with teams, and applying theoretical knowledge to solve business problems.

You can also consider joining hackathons or Data Science Competitions to gain real-world work experience. Websites like Kaggle offer challenges that allow you to apply your skills to real-world problems while competing with data scientists worldwide.

Another option is to work on freelance projects. Websites such as Cake and Freelancer provide opportunities to work on data science projects, giving you practical experience while building your portfolio.

7) Building a Portfolio and Network

To stand out in the competitive data science field, a well-rounded portfolio is essential. Your portfolio should showcase a variety of projects that demonstrate your skills in data manipulation, machine learning, and other data visualization software. Examples of what to include are:

• Exploratory Data Analysis (EDA): Show your ability to extract insights from raw data using statistical techniques and visual tools like Matplotlib, Seaborn, or Tableau.
• Machine Learning Models: Demonstrate projects where you’ve built and trained predictive models using frameworks like Scikit-learn or TensorFlow.
• Real-world Applications: Include projects that solve real-world problems, such as business intelligence dashboards or recommendation systems.
• GitHub: Host your projects on GitHub to display clean, well-documented code. It also serves as a portfolio that recruiters can browse through.
• Blogs and Tutorials: Write blog posts on platforms like Medium to explain your projects or teach data science concepts. This will help you articulate your thought process and position you as a thought leader.

Networking is equally important. Here’s how you can grow your professional connections:

1. Join Data Science Communities: Participate in online communities like Kaggle, Reddit’s r/datascience, or LinkedIn Groups dedicated to data science discussions and job postings.
2. Attend Webinars and Meetups: Platforms like Cake Meet or Meetup regularly host data science webinars and meetups where you can learn and network with professionals.
3. LinkedIn: Engage with posts related to data science, share your projects, and connect with professionals to stay updated on job opportunities and industry trends.
4. Mentorship: Seek out mentors or professionals willing to guide you, helping you grow both technically and professionally.

By continuously refining your portfolio and expanding your professional network, you will showcase your expertise, increase your chances of finding job opportunities, and stay updated on industry trends.

Where Do Data Scientists and Data Analysts Work?

Data scientists and data analysts are in high demand across various industries, with their analytical skills now being applied in numerous sectors to unlock the power of data. Whether it's predicting trends, solving complex problems, or improving customer experiences, these professionals work in a range of environments. Let’s break down some of the most common industries where they make an impact:

Healthcare

• Hospitals and Medical Research Centers: Data professionals help manage patient records, predict health trends, and optimize healthcare processes.
• Pharmaceutical Companies: They are involved in drug discovery and predicting treatment outcomes using vast amounts of medical data.

Finance

• Banks and Financial Institutions: In charge of fraud detection, risk management, and algorithmic trading.
• Insurance Companies: Data analysts forecast risks and create predictive models to adjust premiums.

Retail

• E-commerce Giants (e.g., Amazon): Use data for personalized marketing, inventory management, and customer segmentation.
• Brick-and-Mortar Retailers: Employ data analytics for optimizing supply chains and improving the in-store experience.

Technology

• Tech Companies (e.g., Google, Facebook): Data scientists work on AI, machine learning algorithms, and data-driven product improvements.
• Startups: Focus on building data-centric products, and rely heavily on data professionals to develop new solutions.

Government

• Public Health Organizations: Use data for disease tracking and resource allocation.
• Defence and Security Agencies: Data professionals work on intelligence analysis, cybersecurity, and national security initiatives.

Challenges and Pitfalls When Working in Data Science

Data science is a powerful tool, but it comes with several challenges and potential pitfalls that professionals who organize and analyze data often must navigate to achieve accurate, ethical, and impactful results. Here are some of the common challenges faced in the data management field:

• Data Privacy: With vast amounts of data collected from individuals and organizations, ensuring data privacy is critical. Data scientists must comply with regulations such as GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act) to protect user privacy. Mishandling sensitive information can result in legal consequences and harm to a company’s reputation.
• Bias in Data: Algorithms are only as fair as the data they’re trained on. If the input data contains biases—whether related to gender, race, or socioeconomic factors—these biases can be amplified, leading to unfair outcomes. Data scientists must actively work to detect and minimize biases in their models to avoid perpetuating inequality and discrimination.
• Data Quality: Clean, well-structured data is essential for effective analysis. However, in the real world, data is often messy, incomplete, or inconsistent, making it difficult to derive accurate insights. Data scientists must spend considerable time cleaning data mining and organizing data before applying algorithms to analyze data, which can be both time-consuming and complex.

Conclusion

Data science remains one of the fastest-growing and most sought-after careers, with employment projected to grow by 36% from 2023 to 2033. The demand for data engineers and scientists continues to outpace supply, creating numerous opportunities, with about 20,800 job openings for data engineers expected annually over the next decade​.

For individuals passionate about technology, mathematics software engineering, and problem-solving, earning a bachelor's degree or an advanced degree in data science or analytics can pave the way for a thriving career in this dynamic field. The future looks promising for those ready to harness the power of data in an evolving, data-driven world.

— Originally written by Donaven Leong —