Data Analyst Skills for Career Success

Data analysis can offer you job security, the potential for remote work, high pay, and interesting challenges… intrigued?

Careers in data analysis have become very popular in the last 5-10 years for all of these great reasons. While analysts have existed in organizations for years, new tech tools and a major influx in data collection in organizations are reigniting serious interest in this career path. If you're interested in how to become a data analyst, check out our article dedicated to the subject. 

From tracking sales and orders to customer and stakeholder management, data is an integral part of every organization. “The use of analytics by businesses can be found as far back as the 19th century;” however, as more of the world gets online, making data meaningful has grown to become a critical part of most modern businesses' survival.

Ref: Data Science Job Market Shrinking?

This new environment, combined with the rise of Massive Open Online Courses (MOOCs) and other open courses, has created a new market for data analytics careers. While graduate programs and professional certifications are rapidly appearing, professionals still have the chance to make their way into this field from diverse disciplines, using open courseware and self-paced material available online.

The Educational Makeup of Data Analysts in the US in 2021:

Source: zippia

Specialists from all backgrounds have found lucrative opportunities in this field by focusing on the core skills needed to solve business problems and support non-technical managers.

While this role is still rapidly evolving, many things have remained the same: the need for critical thinking, the ability to collaborate with and present to diverse audiences, subject matter expertise, and an understanding of the fundamental need for each step of the data analytics process. We will cover all of these areas in detail in the rest of this article. If you're looking for tips on how to prepare for data analyst interview questions, you can check out our separate article. 

From 9 to 5: A Day in the Life of a Data Analyst in 2022

Choose your own adventure! Most modern data analyst roles fall into one of two categories: you’re either the “data person” in a non-technical department, or an analyst in a technical data-focused department, working alongside other analysts, engineers, and scientists. Your days will look very different depending on the type of data analyst you are.

When you are “the data person” in a non-technical department, you will collaborate with your manager and colleagues every day. From gathering, cleaning, and analyzing data, you will support their efforts to make data-driven decisions. These roles usually heavily focus on reporting and visualizations, so you’ll likely manage a dashboard or present regularly to a manager or non-technical colleague. The goal of these roles is to optimize existing data flows to augment subject matter expertise.

While this role has been represented for decades in business and other organizations, increasingly, management has been prioritizing these hires. Where an experienced manager's intuition used to reign, demonstrable proof is now the standard for important decisions. The more you can understand your organization’s motivations, the more success you will have in this role.

Critical thinking, technical skills which focus on managing visualizations for large datasets, and knowledge of basic statistics are all key skills for this type of role. This is also one of the most collaborative types of data analytics roles, so diplomacy, strategic thinking, and project management skills are also beneficial.

Your days will be filled with these conversations and collaborations, with your priority being ownership and maintenance of the relevant core datasets which inform your department’s actions. This type of data analyst role is great for learning more about how an organization functions and deepening your specific subject matter expertise. Since it leans heavily towards a more traditional business or managerial function, these roles will follow organizational trends: expect to leave when your colleagues do (5pm), and relationships and management knowledge will be more critical to your career growth than increased technical skill.

When you're on a core data team, life will look very different. These teams tend to manage the data for an entire organization and report directly to executive leadership, who likely have a forward-thinking, tech-savvy perspective.

Often, these teams act as consultants for the rest of the organization: gathering requirements, developing data pipelines, and serving dashboarding tools for analysts on these non-technical teams to manage. Work will likely be a mix of accommodating legacy systems, along with experiments with cutting-edge technology. Your work will focus on understanding data storage, pipeline, and analysis best practices so there can be some consistency throughout the entire organization. SQL skills will be indispensable in this type of role since you’ll be treated as an expert on specific datasets and the overall system across the organization. Being able to quickly verify a non-technical stakeholder’s ideas or concerns will make you a standout on this team.

If you’re in a large organization, expect to find yourself learning a lot about databases, and the challenge of uniting legacy tools with modern systems and expectations. On these teams, your key stakeholders will be: solution architects, who manage the organization’s overall data storage; systems analysts, and software engineers; along with SCRUM or other types of project managers. Since this role errs on the technical side, your career growth will largely depend on advanced skills in a specific technology (most likely SQL), and excellent people and project management skills.

While these two descriptions cover the experiences in most data analyst roles, there are still a few niche data analysts whose lives are even more different: marketing data analysts, financial data analysts, and product data analysts.

Most Common Tools for Data Analysts

In most organizations, advanced Excel skills will never go out of style. Regardless of how high-tech or innovative your company is, a spreadsheet will never stop being a part of your workflow. It’s an easy way to share your data with non-technical peers, and do some meaningful analysis on the fly. Microsoft offers certifications for advanced Excel skills, and there are online courses to prepare for the required exam. Online courses and certifications are a great way to learn the most important and common functions of Excel.

Next, structured query language (SQL) is the most important code you will write as a data analyst. SQL has stood the test of time, and been the core tool for data retrieval for many years. Some methods to focus on when you’re just starting to learn are SELECT, JOIN, and HAVING.

Ongoing reporting and visualization is key in most data-related jobs. Tableau, Looker, and Power BI are some common tools that can be used for the most common KPIs across organizations. They all have powerful technical capabilities but are user-friendly enough for non-technical managers. They’re listed from greatest to least in terms of price; you’ll often find Looker and Power BI in departments or organizations where budgets are smaller.

When data gets too big and messy, Python & Jupyter Notebooks are particularly useful tools for data analysts. We’re nearly entering data science territory with these tools - most data analyst jobs will not center on this approach because it takes a high level of skill to be able to make python more valuable than the tools listed above. However, by using Jupyter notebooks or Google Colab, you’re more able to collaborate with others, do more complex analysis, and demonstrate an entire narrative of analysis and visualization in one place. You’re likely to see these tools required for marketing data analyst roles, which tend to focus on A/B Testing.

While many claim there is still debate over which language is the best for data analytics, Python has consistently outstripped R as the most popular language both overall, and in the private sector.

Ref: Which Programming Language Should Data Scientists Learn First?

However, if R seems more appealing, or you have experience with it from work in academia, you still can’t go wrong. If you’re fluent in one language, being able to program in the other is a small challenge. What’s most important to consider when choosing a programming language for data analysis is that you feel comfortable and able to dedicate yourself to it entirely. For these tools to make sense in a workplace or your career, you need strong proficiency and know-how to navigate results.

Where You Can Learn These Skills

DataCamp is an excellent place to start.

DataCamp has both skill and career tracks that can guide you through all the tools and best practices necessary to break into a career in data analytics: data analyst with R, data analyst with Python, and data analyst with SQL.

Otherwise, here are a few other places you can start your learning journey:

• SQL
• Python
• R
• Advanced Excel
• Tableau

Other Important Skills for a Successful Career as a Data Analyst

Despite all the tech that comes with this career, data analytics takes much more than specialized computer skills to be successful. Being able to successfully integrate within an institution through organizational knowledge, relationship building, and business best practices will help you avoid stagnation. A few key areas include:

• Product management
• Project management
• Collaborating with stakeholders
• Becoming a subject matter expert (SME)
• Business administration/organizational awareness for public, NGO, and academic work

Resume & Interviewing: Data Analyst Job Search Prep

Here are some of the most common themes in interviews for data analyst roles:

• SQL skills
• Prepare to be tested on the fundamentals in many technical interviews
• Understand JOINs, HAVING, etc.
• Tableau
• Prepare to be asked about past dashboards you’ve created - having a Tableau Public profile with a few examples will be sure to impress your interviewer
• Questions about your experience:
• Describe a project where you had to collaborate with non-technical stakeholders
• Describe a situation where there was low data quality, but a stakeholder still wanted analysis completed/a decisive outcome that you couldn’t give
• Describe a data workflow you’ve worked with in the past (tools, process, stakeholders, visualizations, etc.)

Like all interviews, the STAR method is a solid approach to all your responses. As it is a technical role, you will have to continuously educate yourself on new tools and processes. Admitting you don’t know something, but are willing to find an answer, demonstrates an understanding of what’s required for the day-to-day work of a data analyst.

Many major companies hire and pay well for experienced Excel and SQL developers. These are some core technologies that will never go out of style. There are data analysts who’ve become experts in these two tools alone and built highly successful careers for themselves.

Regardless of your approach, be sure to list all technologies you have knowledge of or experience with, so your interviewer knows to ask you about anything relevant. Just because they are not using a specific technology now, does not mean they won’t in the near future, and vice versa. Be transparent about your skills and show them what you can do.

Building a Career

Choosing to build a career right now as a data analyst is a great choice. Our world is in the middle of a great AI “arms race” between major economies, and the need for data-savvy professionals is only going to grow.

If working in the private sector doesn’t interest you, there are plenty of other opportunities to apply your skills to something more meaningful. Here are a few examples:

• Data for social good
• ESG/sustainability reporting
• Digital media strategy used in Obama’s presidential campaign

Get Started Today

If the idea of solving problems, collaborating with diverse people, and making a difference in any organization you’re a part of motivates you, consider becoming a data analyst.

From critical thinking to database knowledge and strong people skills, you’d be investing in a high-value career with endless potential. Learning these skills will take commitment and focus. New technologies are developed constantly, but focusing on the core skills mentioned in this article will ensure that you have a strong foundation and a long career.

Our best advice is to use the resources that are available online today. There are excellent programs such as DataCamp, which are developed by experts and experienced professionals who have faced these challenges firsthand. Trust them and the learning process, and you’re sure to find your version of success. We look forward to seeing you in the data analyst community!