Hackernoon logoTop Data Analyst Skills in 2021 by@albertchristopher

Top Data Analyst Skills in 2021

albertchristopher Hacker Noon profile picture


According to a report from the International Data Corporation (IDC), the global big data and business analytics market has been expanding at a fast clip over the last several years, leaping from US$122 billion in global revenue in 2015 to US$189 billion in 2019 and driving towards a projected US$274 billion for 2022.

As industries store and assess a huge amount of data online, they need professionals who can take care of managing, organizing, and assessing the information. Many companies are hiring data analysts to meet their data needs. Data analysts need certain skills to excel in their positions.

For a rewarding career as a data analyst, it is essential to master the skills. These skills can also be learned by enrolling in a data science certification. Following are the important technical and non-technical skills which will help an individual to get hired in 2021:

Key technical skills data analyst require

An individual can master following technical skills by doing a big data certification:

• Programming languages

> Python – This is a high-level, general-purpose programming language, which is mainly used in data science, it also offers a remarkable number of specialized libraries. A few libraries of Python are listed below:

a) Numpy

b) Pandas

c) Scikit Learn

d) Matplotlib

>R – This is among the highly used programming languages in data analytics. Its structure and syntax are developed to take care of the analytical tasks. It is one of the most appealing programming languages for businesses as it possesses the capacity to manage huge and complex quantities of data. It consists of packages that ensure to manage the analysis more easily. A few of the packages are listed below:

a) Ggplot2

b) Dplyr

>MATLAB – It is a programming language and multi-paradigm numerical computing environment that offers quick data cleaning, organization, visualization, matrix manipulations, algorithm implementation, and data plotting. It can also execute any machine learning models created in its environment across multiple platforms.

>SQL and NoSQL - SQL or Structured Query Language is used for querying and handling huge sets of data and processing information in relational databases. The unstructured data is stored in a NoSQL database in the format of the document. The NoSQL database querying requires a proprietary language that varies from SQL.

• Data visualization

Data visualization is the representation of the data in an easy-to-digest manner, which includes charts, images, maps, and graphs. It offers better insights into data-driven details. The data analysts also facilitate the business’s decision-makers to identify patterns and understand complex ideas at a glance.

• Data warehousing

In this process, the creation of virtual storage and organization systems of an enterprise takes place. The role of a data analyst is to connect the databases from multiple sources to develop a data warehouse by using the querying languages to manage, monitor, and safeguard the data.

• Data mining, cleaning, and munging

A data analyst gathers information from one or more sources and prepares the data so it is helpful for numerical and categorical analysis. If the data is not stored properly in the database, the data analysts use certain tools to clean the unstructured data and process it through programming. In the munging process, the data is identified, extracted, and integrated to get a dataset, which is helpful for exploration and analysis.

• Machine learning

Machine learning offers computers to automatically learn and perform without the requirement of explicit programming. There are many firms that use highly technical machines to process works. The data analysts should have knowledge on how to build, apply, and train the most appropriate models and algorithms to get solutions for certain problems.

Key non-technical skills data analyst require

Apart from technical skills, there are other non-technical skills that are important for data analysts to be successful. An individual could be a stellar data analyst by doing a big data certification, which assures to provide the soft skills.

• Communication

Communication is a key skill for data analysts. They are needed to explain their findings to various stakeholders, other data analysts, and IT representatives.

• Problem-solving

While performing their tasks, data analysts encounter many technical issues. They are required to effectively problem-solve and come up with better solutions.

• Teamwork

Data analysts should be able to perform tasks with various teams so that they can collaboratively achieve the business objectives.

• Domain knowledge

Domain knowledge is an understanding that is relevant to the particular organization that one works for. This domain knowledge allows resourceful insights into the data and gives data analysts useful information.


By putting an effort and time into a data science certification to learn the aforementioned skills can set up an individual for a successful career as a data analyst.

If an individual wants to get hired in 2021 by a top firm then he/she must enhance their skills accordingly. Always list only those skills on your resume, which you can answer well in the process of the interview.


Join Hacker Noon

Create your free account to unlock your custom reading experience.