Data Analyst Competencies You Need To Get Hired In 2020

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Modern-day businesses run on data, and data analysts worldwide process around two and a half quintillion bytes of data every day. This metric is likely to increase given the rapid pace of technological innovations and data-driven solutions. 

Business analytics and Big Data is expanding and innovating at high speed, and the past decade has witnessed dramatic transformations. This heightened pace of innovation has opened up scores for building an adequate skill set as a data analyst. Organizations are increasingly reliant on data to support their decision-making, making them rely on talented and skilled data analysts.

Data analysts are in demand across all industry sectors, including banking, healthcare, manufacturing, retail, finance, and more. In this article, we will walk you to the data analysis skills that are highly in demand in 2020. 

Here, take a look:

Data Visualization

Data visualization is the ability to present key findings and insights with immersive graphics and comprehensive illustrations. As a data analyst, a skilled professional’s core responsibility is to introduce data-driven insights to executive management. Data visualization techniques allow complex data sets, broken down into recognizable patterns and bright ideas.

It is a significantly important skill that allows data analysts to be useful for an organization. It is a skill that empowers the data analyst, giving him/her a more robust command over data-driven insights and key findings. It allows them to process complex information and present decision-makers with actionable results and data-driven solutions.

Data visualization is likely to transform how data analysts work with data and process complex findings. A bachelors of data science is focused on data visualization to tackle risks and crises with a speedy and pragmatic response. It empowers the analyst to dig deeper for actionable insights, and adopt creative data processes approaches.  

Data Cleaning

Cleaning is instrumental for paving an obstacle-free road towards achievements and accomplishments. Cleaning has a similar impact on data analysis; therefore, data cleaning is a practical and indispensable skill.

Data cleaning is one of the most necessary steps in designing and implementing a useful machine learning model. A large portion of a data analyst’s day is devoted to data cleaning. We often believe that data scientists are always developing new machine learning models and AI-based tools. But in reality, they are cleaning data to support the functions of a highly sophisticated machine learning algorithm. 

Data analysts require thoroughly cleaned sets of data that allow even the simplest ML models to provide clear insights. On the other hand, de-cluttered and uncleaned data sets can give misleading findings and intricate patterns. All data analysts require competent data cleaning skills to be able to perform their duties.

MATLAB 

A programming language, MATLAB is also a multi-paradigm numerical computing environment. It is crucial for implementing algorithms, plotting data, gaining insights, and manipulating matrices. MATLAB is indispensable for analysts, particularly Big Data companies, as it allows speedy data processing and cleaning. 

MATLAB is a dynamic tool for data cleaning and visualization, and it empowers data analysts with superior capabilities. It organizes data with immense efficiency and provides a wide array of visualization tools. More importantly, MATLAB allows analysts to build machine learning models in their environment and implement them across multiple platforms.

While being proficient in MATLAB is not a necessary skill, it does have numerous high-powered applications and functions. It can empower a data analyst with the skills and talent required for career advancement and leadership positions. Understanding MATLAB  is a marketable skill that makes a data analyst more useful for an organization. 

Python

Python is a high-level programming language that has countless applications and purposes. It is a high-priority skill for businesses focused on Big Data and artificial intelligence (AI). Young data analysts and students strongly need to learn Python, as it is highly in demand and offers various specializations. 

Experts believe that Python is a new-age skill because it is crucial for AI-based developments, the future of business. It is a skill that allows data analysts to remain relevant and up-to-date with industry trends and the evolving AI environment.

Machine Learning

Machine learning is not considered a skill in the same manner as programming languages or data cleaning. However, understanding machine learning models allows analysts to develop a highly competitive and marketable resume. 

It is essential to understand that presently, artificial intelligence and predictive analytics are rapidly expanding and innovating. Understanding machine learning concepts and tools will allow their skill set to remain relevant and competitive. It does not imply that all analysts are working on machine learning models and algorithms. But having a firm grasp over machine learning makes analysts more useful and coveted for organizations. 

Advanced Mathematical Skills 

Algebra and calculus skills are indispensable for the field of data analytics and data sciences. Many competent analysts come from an academic background in statistics and mathematics. Advanced mathematical skills allow a superior understanding of analytical theories and dataset patterns. 

Linear algebra and calculus have numerous applications in deep and machine learning, especially while building objectives and functions in algorithms. However, analysts don’t require an extensive mathematical background for data sciences. 

Conclusion

It is essential to realize that there is no correct or incorrect way to become competent and dynamic data analysts. If you’re passionate about data sciences, you need to experiment with various elements and things. Start by learning how to code and become friendly with programming languages. Focus on working on some real-life projects and take help from YouTube tutorials and literature. Ultimately, you will have to keep exploring and carving out your educational journey. 

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