There’s never been a more exciting time to launch a career in data. As companies in various industries continue to invest in establishing their data operations, Data Analysts have plenty of opportunities to find a role where they can apply and develop their skill sets. But what are the core skills every junior Data Analyst needs to get started?
Understanding the skills and strengths that are most important for the job is necessary both for those starting out in their first data analysis role and for leaders building a data team at their organization. Like with any role, people who are just getting started in data analytics might not tick every box on the skills list — and that’s perfectly fine. The important thing is making sure there’s an alignment between a person’s skills, experience, and potential and the needs of the company.
Top 8 skills every junior Data Analyst should learn
We’ve rounded up the 8 most important skills for a junior Data Analyst to develop, and tips on how to learn them. So if you’re taking on your first data job or you just hired someone, keep reading to understand the factors for success in this expertise.
First and foremost, learning SQL (Structured Query Language) is an essential skill for any data role. As the gold standard data language for most relational database management systems like MySQL, Oracle, and Microsoft SQL, SQL is something Data Analysts need to accomplish their day-to-day tasks. SQL is used to extract and organize data from a database, making it usable for analysis.
2. Microsoft Excel
One of the most popular spreadsheet solutions, Microsoft Excel is another tool Data Analysts use frequently in their work, so learning the functions of Excel is an important skill for anyone taking on a data analysis job. It’s especially helpful for making data more accessible to non-technical specialists and keeping things organized once some baseline extraction and analysis has been done, or when you’re working with smaller datasets. Excel is a super useful tool for basic data visualization and some simple manipulation.
3. Data management
Data management is the structures and processes through which data is stored, accessed, and used by various systems, people, and software. It’s the tools and connections (sometimes called a data stack) that move data in and out of your data warehouse, keep it secure, give it structure, and make it usable. Especially for a company who wants to hire a Data Analyst as their first data hire, they should look for someone with exceptional data management skills.
For Data Analysts launching a company’s data efforts or organizations that have yet to make a data hire, setting up a modern data stack using a platform like Weld can reduce costs and speed things up significantly. Weld handles all parts of data management, from sourcing and extracting data, to storing and transforming it, and even activating data through reverse-ETL pipelines, it’s a complete solution for companies that want to get data-driven fast. Try Weld free today →
4. Data governance
Complementary to data management is data governance. Data governance is like a layer on top of data management — it builds on the technical, practical side of how data is managed to define how data is accessed, owned, used, and accounted for. At a company level, this might mean defining core metrics and establishing ownership of them, and maintaining alignment on KPIs across teams.
Creating data governance is a form of leadership, so depending on a junior Data Analyst’s professional history, experience, and strengths, this might be a big ask for their first role. That being said, there are lots of systems and tools that can support data governance at your business. With Weld’s Metrics Store, there’s a single source of truth for how metrics are defined, clear ownership of core KPIs, and built-in governance through change logs and approval flows.
Alongside being one of the most flexible computer programming languages, Python is also a crucial language for data analysis work. Learning Python is an important skill for junior Data Analysts because not only is it used in many different areas, it’s also fairly easy to learn. From structuring and analyzing data sets to complex data visualization and developing machine learning algorithms, Python is the multi-purpose language every analyst needs to know.
6. Data visualization
Another foundational skill for Data Analysts getting started in their career is data visualization. This is the ability to take raw data (columns and rows of numbers and variables) and present them as pie charts, bar graphs, and even 3D visuals. It might sound like a less technical skill, but creating great visuals requires design capabilities and a knack for communicating complex information to a non-technical audience.
7. Machine learning
Companies are relying more and more on forms of artificial intelligence and machine learning to effectively make use of their business data. And although Machine Learning is a bit beyond the scope of traditional data analysis, it’s important for beginners to get to know the basics. Junior Data Analysts will benefit from a foundational understanding of algorithms, statistics, and the concepts of how machine learning works.
8. Excellent communication
Finally, every Data Analyst must be an exceptional communicator. From understanding the company needs to sourcing the correct information to sharing it effectively and accurately, communication is an essential skill for every junior analyst. This includes written and verbal communication as well as narrative development, storytelling, supportive visuals, and critical thinking. It’s very important that data experts can share their hard work and knowledge with the business and the stakeholders.
How to learn Data Analyst skills as a junior
Professional development is ongoing in today’s workforce. From enrolling in online courses or bootcamps to on-the-job training and learning opportunities and even virtual communities and groups, there are lots of different ways to grow and develop your professional skills. Here are our top tips for learning new skills and strengthening your expertise.
- Be focused: You can’t do everything at once, so choose a focus area that makes sense both for your personal aspirations and the needs of the business.
- Join your community: Whether it’s through an employer program, a virtual community, or attending industry events, connecting with people on the same career path helps you find support and get inspired.
- Find a mentor: Having someone who’s been on the same journey who can coach you is especially important in the data world, where your manager or employer might not totally understand what you do.
- Get the right tools: Supportive tools make it easier to focus on your development. You’ll want to build or buy a modern data stack, get an effective project management platform, and establish clear processes and workflows.
- Set clear goals: Set goals for yourself on a monthly or quarterly basis with your boss, a mentor, or on your own, and track the milestones of your growth and development.
Skills development is an ongoing effort
No matter how far along you are in your career, learning new skills is an important part of professional development and growth. For junior Data Analysts, these skills are a great place to get started. Remember that everyone starts somewhere, focus on 2-3 skills at a time, and you’ll be a pro before you know it.