TOP 9 DATA ANALYST SKILLS YOU NEED FOR YOUR NEXT PROMOTION IN 2023

“Data will talk to you if you’re willing to listen.”

Jim Bergson

Data analysts are leading the pack in collecting and analyzing vast data notes to make key business decisions. Every company needs an experienced data science professional, which automatically takes the stakes very high with the rising demand. Data analysts are translating business findings daily and have ample time to read and dissect information for the larger good of the businesses.

How else does a company know what products they should order or produce more of?

Or how would they know which customers are dissatisfied with services?

2023 is not going to be just another year for data science as the mastery of key data analyst skills along with your educational portfolio is going to make a critical shift in your career trajectory. It’s time one should be aware of the leading market-centric data science skills that are pivotal in making an aspirant all the more attractive and fit for a meaty data science role in 2023.

An in-depth understanding of the following most in-demand data science skills is projected to be the game-changer in your future data science career:

1.Programming Languages

Anyone as a data science aspirant understands the necessity of investing your time in learning core programming languages such as Python, Java, R, and C++. Renowned Cloud Professionals have named C#, Go, Node, Linux, and Terraform as some of the hottest languages expected to be most popular and must-haves in your toolbox in 2023.

Python is hugely in-demand for text analytics and creates a solid foundation for big data support. If you as an aspirant or already a data professional work with or develop big data problems, you’ll no doubt be involved with scripting. This key skill could be mastered by earning the top data science certifications around the world that work wonders by validating and elaborating your skill set to the employer with higher credibility. This is why it is useful to have at least one major scripting language in your armor and Python is the strongest contender, being ranked as #1 in the TIOBE Index as of May 2022.

2. Machine Learning, Artificial Intelligence, and Neural Language Processing

Machine Learning, an integral part of AI is the most important development in data science, as it focuses on building algorithms designed to find patterns in big data sets; thereby improving accuracy. Data analysts earn that competitive edge in their data science career with expertise in ML and NLP, as the Global ML Market Statistics forecast the $209 billion mark by 2029.

3. Quantitative Analysis

Numbers are what runs the data science industry, as is evident. This makes Quantitative analysis skills a critical aspect of a data science professional as a strong background in math and statistics will put you ahead of your competitors. Competence in this aspect makes you a quick pick in front of recruiters.

4. Data cleaning and mining

Data scientists are known for their claim that “80% of building an ML model is preparing and cleaning the data.” Data cleaning is important as uncleaned data can produce misleading patterns and mistaken conclusions. Data mining as good experiences high demand across the tech landscape and investing time in learning data mining kits inclusive of favorites- Rapid Miner, KNIME, or Apache Mahout.

5. Problem-solving

Possessing an analytical bend of mind will take you a long way to becoming a big data bigshot. Solving puzzles, playing chess, or enjoying video games aid in challenging and building your problem-solving capabilities.

6. SQL and NoSQL databases

SQL and NoSQL database competency gives you easy access to an entire range of job opportunities anywhere in the world. Once you are comfortable with database structures and writing SQL to get the data represented in the way you want, your efficiency will enhance with the quality of your work.

7. Data Structure and algorithms

As the data is notoriously complex and isn’t streamlined, learning about data structures and algorithms will make you familiar with data types; which calls for a big portion in key insight building.

8. Data Interpretation and Visualization

The ability to visualize and interpret data is an essential big data skill that brings creativity and science together. This helps in deriving key business insights so that businesses can function uninterrupted.

9. Critical Thinking and Communication

Being the hardest to ace on the block, critical thinking is a conscious effort to challenge your automatic mental processes. This key skill elevates your capabilities of answering questions such as: What factors might matter in measuring a certain situation? What could be overlooked? What is the goal of the task at hand? As a data analyst, you straddle between addressing technical points with peers and translating business decisions to the stakeholders. This makes effective communication an integral part of your skill set.

Here are some quick tips to learn data analytics skills the easy way:

• Keep working on your skills consistently

• Learn from your mistakes

• Get hands-on experience with real data projects

• Be a part of an online data community

• Build your skills every day

The year 2023 is envisioned to witness rapid growth in data science job openings and professionals equipped with top data science certifications will be at a greater advantage in landing a meaty role in this ever-evolving industry.