Key Takeaways
- The ability to work with data and understand the story it’s telling is critical for modern businesses.
- Data science is applicable across a variety of different industries.
- Data governance and ethics are key pieces of modern data science.
- It can take as little as six months to get started.
Data Science Matters Now
As organizations discover new ways to leverage the often massive amount of data they collect, the demand for data science professionals continues to grow. The U.S. Bureau of Labor Statistics predicts the employment rate for data scientists will increase by 36% between 2023 and 2033, leading to an estimated 20,800 job openings each year during that time.
If you’re confused about data science vs. data analytics, you’re not alone. It helps to think of data analytics as a subfield of data science – similar to how specializing in cakes is a subfield of baking. A data scientist takes raw data, performs data wrangling, and converts the data into machine-readable form. Data analysts then analyze and interpret the data to identify trends and patterns. These insights can help businesses better serve their customers through personalization, increase automation and efficiency within the business, and even predict emerging trends in the market.
People enter the field from a variety of starting points, often computer science, mechanical engineering, or mathematics. Having a handful of the following skills can kick-start your career:
- Advanced math skills
- Programming skills
- An understanding of Structured Query Language (SQL)
- Machine learning, deep learning, and artificial intelligence basics
- Statistics
Six-Month Learning Roadmap
While being a data science professional is a career-long educational journey, with the right planning, you can get started in as little as six months. Here’s what that path might look like:
| Month # | Core Areas of Focus | Learning Opportunity |
| 1. Programming Languages | Start by learning Python and SQL. These programming languages form the foundation for working with data. Learning R programming can also come in handy. | What Is Structured Query Language (SQL)? |
| 2. Data Visualization | In month two, you’ll move on to transforming raw data into visual representations (like charts and graphs) to better understand the story the data is telling. | Data Visualization Fundamentals Learning Plan |
| 3. Machine Learning Basics | Starting in month three, you’ll establish a strong foundation in machine learning and learn how to work with the data. | Machine Learning Essentials Learning Plan |
| 4. Big Data Technologies | Dive into big data for month four and learn how to develop scalable analytical solutions using massive datasets. | Data Warehousing and Business Intelligence Management (Including Big Data) |
| 5. Data Engineering | Month five is all about understanding the importance of improving the correctness, accuracy, completeness, and relevancy of data assets. | Introduction to Data Engineering and Data Enablement |
| 6. Real-World Project Portfolio and Continuing Education | Create a portfolio using sample datasets to demonstrate hands-on projects and continue to learn more about data science. | Building a Career in Data Management Learning Plan |
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Choose the Right Learning Path for Your Role
Whether you are an experienced data science professional or new to the field, we have courses for you. The learning path you choose will depend on your specialization.
| Your Role | Areas of Focus | Recommended Training |
| Data Analyst / Business Intelligence Analyst | Develop an understanding of business data and systems, and how to communicate insights to shareholders. | Business Analytics in Action Learning Plan |
| Data Manager | Learn how to harness data insights to improve business functions across the entire organization. | Data Science for Business Professionals Learning Plan |
| Data Engineer | Master the key data concepts and skills needed to acquire quality data. | Data Engineering for Machine Learning and Data Science Learning Plan |
| Data Scientist | Gain a solid understanding of the ethical considerations necessary for responsible data science. | Data and AI Ethics Learning Plan |
Data Governance and Ethics: The Often-Missed Skill Set
Data governance and ethics play a bigger role in data science than you might realize: Effective data governance ensures that data science teams have access to secure, reliable, and ethically sourced data. One of the main benefits of data governance is data quality – a critical component for accurate data analysis and meaningful insights.
Recent research from DATAVERSITY finds that while nearly 75% of organizations have implemented data governance programs, more than half struggle with data governance challenges and data quality issues. Learning the latest data governance best practices and AI governance best practices – and validating your skills through our Applied Data Governance Practitioner Certification – will give you a competitive edge as a data science professional.
Data Science Learning Paths for Key Industries
Data science pulls people in from various careers. You can take the skills you’ve accumulated in, say, healthcare, and apply them to a data science job role in the same industry.
Healthcare
Hospitals produce 50 petabytes of data per year, but amazingly, 97% of that data goes unused. This goldmine could provide insights into disease trends, treatment, diagnostics, and disease surveillance. Data governance and ethics need to be front and center to ensure patient privacy.
Banking and Finance
The financial industry similarly generates a massive amount of unstructured data for data analysis. Data scientists must keep compliance top of mind for most practical applications in the constantly changing landscape of banking and finance.
Cybersecurity
As cyber attacks become more complex and prevalent today, data scientists in the cybersecurity industry need stronger problem-solving abilities. Studying incident-related datasets can, for example, help identify potential insider attacks based on suspicious activity.
Energy and Utilities
In the energy and utilities industry, data science is used to help predict demand and ensure there is enough power to meet that demand. This can also be useful in preventing blackouts or brownouts during times of high need, like snowstorms or heat waves.
| Industry | Career Options | Skills Needed |
| Healthcare | Healthcare provides a wide range of options, such as mapping the spread of disease, diagnoses, medical insurance, and cybersecurity. | * Strong understanding of data ethics and data privacy * Data visualization techniques * Ability to develop machine learning models |
| Finance | Career paths within finance include risk management, fraud detection, customer service, and trading automation. | * Data ethics skills * Financial know-how * Compliance monitoring * Predictive modeling * Statistical analysis |
| Cybersecurity | Data science and cybersecurity go hand in hand. Career options include security analyst, forensic analyst, and incident responder. | * Strong understanding of cybersecurity * Pattern recognition and fraud detection skills |
| Energy and utilities | Specialized opportunities within this sector include focusing on grid data, building energy data, and energy storage data. | * Predictive analytics skills * Deep understanding of forecasting, increasing efficiency, and working with customer data |
Start Your Data Science Journey with DATAVERSITY
Data science is in demand right now and offers a variety of data management career paths. If you’ve got a desire to learn, a passion for statistics, and a love for the stories data tells, now is the ideal time to start your journey into data science. Getting involved today – with DATAVERSITY as your partner in training – is your chance to help define a growing field and build your expertise.
Applied Data Governance Practitioner Certification
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Data Science FAQs
We’ve outlined a six-month path that uses a hands-on approach to help you learn the foundations of data science, but ongoing education is a must.
Absolutely. Computer science skills are an integral part of being a data scientist, but people come to the career from a range of backgrounds. As long as you’re willing to learn programming, you’ll be fine.
You need to know more than basic math in data science. We recommend skills like calculus, linear algebra, statistics, and graphing to start.
Our Data Science for Business Professionals Learning Plan provides you with all the materials and guidance you need to gain a solid foundation in data science principles.
It depends. You can use everything from free online tutorials to structured learning pathways like we offer that start at around $100 per course to learn data science.
