Master these top 10 Data Analytic skills in 2024 to get hired or promoted

What is a business or data analyst?

Business and data analysts gather information from various sources and interpret patterns and trends. They prepare reports that summarize their discoveries and provide recommendations that drive decision-making for their organizations. 

 

 

What skills do you need to get hired as a Data Analyst in 2024?

 

Soft skills Data Analysts need:

 

1. Critical thinking

Your company may ask you to collect data to inform a specific decision or shed light on a certain aspect of the business. As a data analyst, it’s your job to decipher which questions to ask, decide what data needs to be collected, and be able to process that data to uncover the right information. Critical thinking will play a big role in how efficiently you’ll be able to work as a data analyst.

2. Communication/Presentation

Part of a data analyst’s responsibility is to communicate their findings. This includes written communication as well as verbal communication. It’s crucial to be able to clearly outline your findings so a variety of stakeholders can understand them without having a data background. Along with written reports, presentations are often part of the process of presenting your findings. Being confident speaking in front of people and having great communication skills can set you apart from other data analysts.

3. Interpersonal skills

Strong interpersonal skills will always make you a great candidate for a job or promotion. In a role where you’ll be working with a lot of different internal and external stakeholders, it’s important to be able to interact with other people well. Someone who displays great people skills will outshine a candidate with the same technical skills but lacks people skills. To improve or practice your interpersonal skills, work on active listening, resolving conflict effectively, being a good team member, and showing empathy in the workplace.

 4. Attention to detail

Data analysis involves analyzing intricate code and technical structure, so a keen eye for detail is critical. Being able to accurately sort data and catch details is a crucial part of the job for a data analyst. Someone who has good organizational skills and is detail-oriented could have a successful career as an analyst. 

5. Collaboration

Working as a data analyst requires a lot of cross-functional collaboration among internal members as well as external stakeholders. It’s important to be able to work well in a team environment to complete a project and be seen as a valuable member of the company.

 

Technical skills required for Data Analysts:

 

1. SQL

Structured Query Language (SQL) is the standard programming language used to communicate with databases. It’s designed for handling large amounts of data with much more capability than a standard spreadsheet. Knowing how to properly use SQL is arguably the most important technical skill a data analyst needs.

2. Excel/Google Sheets

While a lot of the work you do as a data analyst will be done with SQL, it’s also important to know how to utilize standard spreadsheets in Excel or Google Sheets. Most data analytics courses will cover how to use spreadsheets, but you can also find courses solely dedicated to mastering spreadsheets.

3. Statistical programming language

Statistical programming languages can be used in addition to spreadsheets and SQL to analyze large data sets. There is some debate about which language is the best to learn, however, Python is among the most common and easiest to learn. Other languages include:

• R- helpful for statistical computing
• Java- helpful for creating software that’s compatible across different platforms or devices
• Scala- helpful for creating programming that’s free of bugs
• MATLAB- helpful for plotting data and implementing algorithms 

4. Data visualization

Spreadsheets are difficult to review in meetings, so skilled data analysts will utilize the power of visuals like graphs and charts to help explain their findings and show data patterns clearly. Tableau is the industry standard software used to create these visualizations. Luckily, it’s user-friendly and is a powerful tool that can make your job as a data analyst easier.

5. Machine learning/AI

Machine learning, or AI, is growing as technology grows and is a great opportunity for data analysts to grow their skills and set themselves apart from their competition. This skill centers around designing algorithms to find data patterns. Being proficient isn’t usually expected of a data analyst, however, more jobs are opening up in this field, and knowing how to use machine learning and AI can help propel your career in data forward. We discuss this field further below. 

 

Top Data Analytic skills for working in machine learning and artificial intelligence industries

The applications of machine learning and AI are vast and varied. They are used in a wide range of industries, including finance, healthcare, retail, and transportation, to name just a few. Some examples of how these technologies are being used include:

Data analysts who want to work with machine learning and artificial intelligence (AI) need to have a strong foundation in several key skills. These skills include:

1. Programming

For data analysts who work with machine learning and AI, proficiency in at least one programming language is required. Languages such as Python and R are widely used for data analysis and machine learning and are often the first choice for data scientists and analysts who want to build and deploy machine learning models.

2. Statistics and statistical analysis

Data analysts who work with machine learning and AI need to be familiar with statistical concepts and techniques such as hypothesis testing, regression analysis, and probability. These skills are essential for understanding how machine learning algorithms work and for interpreting the results of those algorithms.

3. Data wrangling

Data analysts who work with machine learning and AI will often be responsible for preparing data for analysis and modeling. This may involve tasks such as gathering and collecting data from various sources, cleaning and preprocessing the data to remove errors and outliers, and transforming the data into a format that is suitable for machine learning algorithms.

4. Data visualization:

Data analysts who work with machine learning and AI should be skilled in creating visualizations to help stakeholders understand the insights that have been uncovered through the data. This may involve creating charts, graphs, maps, and other visual elements to represent data clearly and concisely.

The demand for machine learning and AI skills is only expected to grow in the coming years, as more and more companies look to incorporate these technologies into their operations. If you’re interested in pursuing a career in data analytics, it’s worth considering investing in some training or education in these areas. With the right skills and experience, you could be well on your way to a rewarding and lucrative career in this exciting field.

 

 

Getting started in Data Analytics 

If after reading the skills listed above data analytics sounds like a career path you’d like to explore, the best place to start is with education. Finding a program that covers this software, lets you practice these skills, and works with your current lifestyle will be a game-changer for jumpstarting a new career in data science. 

Pathstream’s Tableau Data Analytics Certificate Program helps you build a solid technical skills foundation and prepares you with hands-on projects, allowing you to apply the learned skills in real scenarios. Our program is designed to be flexible for individuals working full-time or juggling a school or family schedule.

It yields amazing results faster, cheaper, and in less time than going back to college or starting a boot camp. The program also offers career guidance to help graduates polish their resume with their new skills, interview with confidence, stand out among their peers in a competitive field, and excel in their new role.