Top 5 Most In-Demand Data Science Skills You Need to Succeed

Top 5 Most In-Demand Data Science Skills You Need to Succeed

Data has been a significant catalyst for business growth in the digital age. Organisations rely on data usage to gain deeper insights that are necessary for efficient decision making. Every robust strategy is backed by data findings which gives a clear roadmap for a business. 

Data in itself is a vast and complex field that is currently overflowing with unstructured data from emails, social media platforms, productivity applications and more. This has led to several changes including the advent of AI-powered data analytics techniques. 

Analysing such vast data is the job of data scientists who are increasingly becoming a vital component of every business. A career in this field is a great choice for students who are looking for stability and success. Hence, gaining a bachelor’s in data science can be your ticket to professional growth. 

Popular data science skills of 2022

Students looking to study data science need to focus on important skills that will be essential in the working environment and help them climb the ladder to success. Imbibing these skills will improve your job prospects and greatly boost your work proficiency. 

Listed below is a list of fundamental skills that are high in demand for 2022 and vital for all aspiring data scientists. The popularity and usage of these skills will continue to grow in coming times.

1. Data Wrangling

Data wrangling is not a new terminology for those interested in the data scientist career path. In fact, it is the focal point of the entire field as data wrangling refers to cleaning and unifying complex data. To get the best insight out of data, it is essential to organise it and only then can it be analysed. 

The process of data wrangling transforms data by manually converting and mapping it into a convenient form that can be analysed and organised easily. Due to the voluminous amount of data present, this process is essential as it produces accurate and actionable data. This information can be used by a business analyst who doesn’t have to spend much time wrangling data and can focus on the analysis part. 

2. Data Visualisation

Data visualisation is another popular skill that is an intricate part of data science. It continues to dominate the field in 2022 as, without data visualisation, a data scientist will struggle to reach any constructive conclusion.  

In short, the massive amount of information derived through data analysis cannot be comprehended without using visual elements e.g. charts, graphs and more. The human mind can easily understand such a massive amount of information through graphical representation and this is necessary to identify trends and patterns in a data set. 

Without data visualisation, organisations will struggle to use all the available information for any kind of decision-making. 

3. Building Pipelines

Simply speaking, a data pipeline is a process of moving data to another system. For this process, data is extracted from various sources. This allows all important data to be in one uniform place and format. 

In situations, where a data model or data project needs to be viewed that does not already exist, the data scientist can step in and create a robust pipeline instead of roping in a data engineer for the job. 

Data pipelines have many advantages as they offer quick access to necessary information which helps in boosting the decision-making process for the organisation. Along with this, pipelines offer flexibility that is needed for meeting demand during peak business periods.

4. Model Deployment

Model deployment is a term that refers to the application of a model to make predictions with new data. Its core purpose is to get maximum knowledge out of the data that can be used by the organisation and also the customers. 

The deployment phase can completely vary as per the requirements which means you can simply generate a report with it or can use it to implement a data science process. A good example of model deployment can be a credit card company that deploys a training model to identify fraudulent transactions.   

5. Problem Solving

Problem-solving is a transferable skill that is part of the business landscape and for a data scientist, this skill is of utmost importance. All other technical expertise will fall flat if you are unable to figure out immediate solutions to a problem. 

In data science, every day comes with a new set of issues that need to be assessed analytically. To come up with effective solutions, you should know how to address the problem and turn it into a durable and production-ready code.

In the digital age, a data scientist career path remains one of the most sought-after and popular fields. If you are interested in pursuing a career in data science then check out the BSc Digital Business & Data Science programme offered by The University of Europe for Applied Sciences (UE). 

The programme will help you understand the technological changes in the digital landscape and  educate you on current data trends. Students who enrol in this programme can also enjoy the possibility of specialisation in eSports Studies along with a chance to do an internship with a partner company of UE Germany


Is there good job scope with data science?

Currently, data is considered the lifeblood of every organisation and each business depends on insights drawn from data to make decisions. Hence, the scope of data science is very robust with an increasing number of emerging job roles.

This dynamic field is undergoing various new developments as new methods to use data effectively are being generated. In Berlin, the growing number of tech hubs and international technology companies are continuously on the lookout for skilled data scientists.

Is English the language of instruction for BSc Digital Business & Data Science?

English is the language of instruction for the BSc Digital Business & Data Science programme at The University of Europe for Applied Sciences (UE).

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