Data science is a relatively new entrant to the business world. The rise in data collection and processing technology over the past decade presents a unique opportunity to harness the power of the masses to visualize trends, examine relationships between variables, and predict future market behavior and events.
Having the ability to use data to drive decision-making can set you apart as a business professional. But where should you start? Without a background in data science, how can you break into the field, build your skills, and drive change in your organization? Here’s an introduction to data science and six steps to learn it from square one.
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DOWNLOAD NOWWhat Is Data Science?
Data science is a field that encompasses gathering, wrangling, visualizing, and analyzing data and communicating the insights it provides.
Data scientists often use coding and machine-learning algorithms to answer questions using languages such as R or Python.
In business, having data science skills can allow you to gain insights about your customers and protect their privacy, predict market trends, forecast financial movement, and streamline manufacturing processes using machine learning.
Being data literate and understanding data science can help you make data-informed decisions and answer your organization’s most pressing business questions. If you’re unsure of where to start, here are six steps to learn data science from the ground up.
Related: What Is Data Science? 5 Applications in Business
6 Steps to Learn Data Science From Scratch
1. Embrace the Challenge
The first step on your journey to learn data science is to confront any mental barriers surrounding your ability to take on the challenge, learn the material, and develop data science skills.
“Data science isn’t scary, and it shouldn’t be intimidating,” says Professor Yael Grushka-Cockayne, who teaches the Harvard Online course Data Science for Business. “Combining your business know-how and intuition with data science can set you and your company up for success.”
Although data science has a reputation for being code-based and complex, its concepts are accessible if you have the desire and drive to learn and put in the work.
“Some folks are under the impression that unless they were trained as a data scientist and have been coding for years, they don’t stand a chance,” Grushka-Cockayne says in a recent webinar. “But that’s not accurate. It’s never too late.”
2. Start with the Basics
Next, familiarize yourself with data science fundamentals. You can do this by reading blog posts and articles, watching videos, talking with colleagues in the field, or taking an introductory data science course like Harvard Online's Data Science Principles. The key is to establish a foundation in data concepts and best practices so that you can take on more complex topics over time.
Here are some blogs posts to help you get started:
- 5 Key Elements of a Data Ecosystem
- Data Literacy: An Introduction for Business
- Data Governance: A Primer for Managers
- Data Privacy: 4 Things Every Business Professional Should Know
- What Is Data Integrity and Why Does It Matter?
- 8 Steps in the Data Life Cycle
- Data Wrangling: What It Is & Why It’s Important
Once you have a firm grasp on key data science concepts—like the data ecosystem and life cycle, data governance and privacy, data integrity, and data wrangling—you can move on to the tools and frameworks needed to use data science in your organization.
3. Familiarize Yourself with Tools and Frameworks
There are various data science tools and frameworks that can be beneficial when using data science at work. One is the data-driven decision-making framework, which Grushka-Cockayne explains in Data Science for Business.

This framework presents six steps for using data to drive business decisions:
- Understand the business problem: What are you looking to understand or accomplish?
- Wrangle data: Clean, validate, and organize the data.
- Create visualizations: Present the data in a way that shows trends and relationships of interest.
- Generate hypotheses: Formulate predictions based on emerging trends.
- Conduct analysis: Run statistical tests to determine if your hypotheses are correct.
- Communicate results: Present your findings in the context of the original business problem.
It’s also useful to gain familiarity with programs and tools that can help you throughout the process. For instance, Excel and Power BI are both statistical software from Microsoft that allow you to organize, visualize, and analyze data. Other tools, such as Google Analytics and Tableau, can be used for further analysis and creating dashboards to present and monitor changes in your datasets.
Understanding data frameworks and tools gives you the capability to take a raw dataset, interpret the story it tells, and use it to answer relevant business questions.
Related: Top Data Visualization Tools for Business Professionals
4. Learn From Real-World Examples
When learning data science, real-world examples can be a useful resource. By exploring how other business professionals use data science to solve problems, you can imagine what you’d do in their scenario, evaluate the impact of their actions, and put that knowledge into practice.
“You need to make it real,” Grushka-Cockayne says in a recent webinar. “You need to make it relevant and ask, ‘Why do I care about this?’ or ‘Why do I want to look at a summary statistic?’ or ‘How is this going to be meaningful for a specific decision?’ By exposing yourself to cases from various industries, the richness of the variety allows you to put yourself in a decision-maker’s position and understand how actual decisions get made.”
5. Find a Community
On your path to learning data science, a community of fellow professionals with the same goal can be a motivating, supportive force. You can turn to online forums, social media, affinity groups within your organization or geographic area, or a cohort of learners in an online class.
For instance, taking a Harvard Business School Online course like Business Analytics grants you access to the HBS Online Community, a global network of learners dedicated to furthering their business education. Having this kind of community allows you to ask for feedback and advice, engage with others on new concepts, and cheer each other on as you work toward your goals.
6. Ask Big Questions of Your Data
Finally, to expand and deepen your skills, continually ask big questions about your data. With each question comes a new opportunity to uncover more insight and gain skills. These questions can lead you to learn a new coding language, analysis method, regression, or visualization tool to help answer a specific business question. Here are examples of questions to ask yourself when handling data:
- What am I hoping to understand?
- What do I need to know to make a certain business decision?
- What story is this data telling?
- What do the relationships between variables mean for ____ at my company?
- What if ____ changed? Which variables, trends, or forecasts would be impacted?
- What needs to change in the data to get the desired outcome?
- Why does the data trend in this direction, and what does that mean for the future?
- How can I further analyze the data to get the answers I need to make important decisions?
Make the data work for you by determining what you need to know and the best way to answer that question using available data. Improving your data science skills is an ongoing process, and each experience offers a chance to learn more.
Demystifying Data Science
No matter your title, learning data science can prove to be a worthwhile investment in your career and company.
“All of us in the business world today have a responsibility,” Grushka-Cockayne says in a recent webinar. “We are all part of the data science process.”
Armed with data basics, tools, frameworks, real-world examples, a strong community, hard-hitting questions, and confidence, you can communicate and drive impactful, data-backed decisions at your organization.
Are you interested in furthering your data literacy? Download our Beginner’s Guide to Data & Analytics to learn how you can leverage the power of data for professional and organizational success.
