You may have heard about ‘big data’ in some form — machine learning, cloud computing, predictive analytics, and more. Big data and data analytics continue to transform and redefine nearly every industry, particularly as organizations of all sizes adapt following the pandemic. Additionally, decision-makers at every level depend on big data to guide their companies toward success — from front-line workers, managers, and supervisors, to the C-suite.
Data analysts and data scientists use various tools and technical skills to understand and extract important insights for an organization. Additionally, employment as a data scientist is projected to grow 35% between 2022 and 2032, according to the Bureau of Labor Statistics.
Here are 12 skills you need to pursue a career in data analytics and data science:
1. Research
If you hope to become a data analyst, then you will require a comprehensive understanding and proficiency in research and development. You should know how to research specific information, evaluate academic studies, and develop unique strategies for your project. Understanding where to find high-quality, reliable, and factual information is necessary when you work in the data analytics field.
2. SQL
Almost all data analysts use Structured Query Language, more commonly referred to as, SQL, on a day-to-day basis. Essentially, SQL is an advanced computing tool with a powerful algorithm to input and process large amounts of data. It works more efficiently than a typical spreadsheet software, and no matter what job you take in the data analysis field, you are likely going to need to be familiar with SQL.
3. Microsoft Excel
Widely used by professionals across business industries, data analysts should also be well-versed and proficient in Microsoft Excel, a spreadsheet software offered within the Microsoft product suite. While most data analysts are skilled in working with other computing tools, such as SQL, an expert understanding of Microsoft Excel will help you, as a data science professional, work with people in all fields and at all skill levels. Ready to brush up on your Excel skills? Here are 30 Excel tips and tricks for you to try.
4. Data Wrangling
Data wrangling is the process of converting raw data into a more widely used format. For example, as a data analyst, you may take raw data and convert it into a Microsoft Excel spreadsheet for your clients to easily read and interpret the datasets. To further assist with data wrangling, familiarize yourself with Python, a tool used to clean and package data for your organization or client. Each dataset you analyze for your organization or client will require a different data-wrangling technique to leverage and interpret the data effectively — keep this in mind as you explore your career in data analytics and develop your technical skills.
As more businesses and decision-makers across organizations rely on data to inform future business decisions, big data and data analytics will continue to grow.
5. Data Prep
When you’re working as a data analyst, your daily to-do list will frequently include data prep. Often the first step involved in leveraging data, data prep requires you to compile data from your databases and break it down into easily readable, interpretable, and understandable reports. You will often need to complete data prep before you move on to the next step — data visualization.
6. Data Visualization
As a data expert, you probably love analyzing numbers and crunching the information to better understand a specific scenario. However, we’re all unique and process information differently — that’s where data visualization comes in. Data visualization requires analysts to process and compile data visually for the average reader. To create visualizations, you will often convert and present your data with graphs, charts, and infographics for your clients to easily digest and understand. Tableau is a good example of a data visualization tool you might use.
7. Data Management
In addition to collecting data and generating reports, you also may be responsible for data management. Data management is the process of collecting, maintaining, and storing data securely over time for your clients as they develop data-driven predictions and decisions. Your clients may ask you to update your datasets with new information, thus requiring you to be familiar with specific queries and programming languages.
8. Statistical Programming Language
Depending on your data analysis role or position in an organization, you may need to demonstrate expertise in a specific programming language, in addition to having a working knowledge and proficiency in many other programming languages. We previously discussed Python as a tool used in data wrangling, but data analysts should also understand R, another programming language. These programming languages assist with analyzing and evaluating large batches of data in an efficient manner for your company or client to leverage that data as quickly and effectively as possible.
9. Machine Learning
Unlike studying a programming language or improving your data-wrangling skills, machine learning is not necessarily a required skill if you want to work in data analytics. However, experience with machine learning can help you become a more attractive job candidate in a competitive market. Machine learning uses automated technology and artificial intelligence for predictive modeling and analysis. Based on the data sets, machine learning can, quite literally, identify patterns, accelerate business success by reducing manual efforts, and provide unique findings for your team to make data-driven decisions. When you know how to create an effective machine learning model, you will differentiate yourself in a growing and high-demand field that is sure to see an influx of job candidates.
10. Problem Solving
Most professionals, regardless of the industry they are working in, need to be adept and innovative problem solvers when presented with unexpected challenges. Data analysts are no exception. When presented with a data collection challenge or if your client struggles to leverage their data appropriately, you’re expected to develop a creative solution that prioritizes accuracy and efficiency.
11. Public Speaking
Most data analysts imagine themselves sitting in an office setting, pouring over the data that they have collected, and compiling it into reports and visualizations for others to grow and improve their businesses. You may not have fancied yourself as a public speaker, but data analysts have to do a surprising amount of public speaking and presenting. In many cases, you will present and showcase your data to an audience — in person and remotely using video teleconferencing tools, not simply via email or PDF. To differentiate yourself as an ideal candidate to prospective employers, speak to your public speaking and presentation skills. Be sure to communicate how you can adapt your presentation skills and your insights to different audiences.
12. Project Management
As you oversee the collection of data and manage a variety of roles to accomplish a collective goal, a thorough understanding of project management will keep you and your team aligned and informed. Project management is known as the process of leading a team toward accomplishing different milestones to complete an initiative on a timeline. This process often includes a variety of meetings with your teammates to discuss milestone statuses, challenges, and other items that may arise throughout the project. As a data analyst or data scientist, project management allows your team many opportunities to ensure the data and statistics needed by your organization or client are being collected appropriately and evaluated effectively for the desired outcome.
To develop your data analyst skills in this growing field and set yourself apart as a top candidate, consider pursuing a degree in data analytics with Johnson & Wales University. At Johnson & Wales Online, we offer an online Master of Science in Data Analytics and an online MBA in Data Analytics that provide you with the in-depth expertise you need to catapult your career in data analysis and data science.
For more information, complete the Request Info form, call 855-JWU-1881, or email [email protected].