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5 Essential Skills Necessary for Data Science Jobs

Data is king. Every business need and depends on data for each movement, either in production, operation, marketing, or sales. Data has got ingrained strongly in every business aspect. This has led to jobs related to data science and enormous courses, degree, or certification programs facilitating young aspirants toward success in their data science career.

However, extracting true business value requires a unique combination of technical skills, soft-skills, mathematical skills, storytelling, intuition, and creativeness. Almost all of the best data science certifications teach the technical skills, the professionals need to jump-start their career in data science.

The technical skills every data analyst, data engineer, data scientist, or machine learning engineer should know is well-known and is taught in their respective learning programs. The most common ones include –

• Programming skills – knowledge of statistical programming language such as Pythion or R and SQL, the database querying language.

• Statistics – Basic understanding of statistics like tests, distributions, estimators, regressions, optimization, clustering, etc.

• Big data technologies – Spark, Hadoop, MapReduce, sentiment analysis, extracts entities, text mining, etc.

• Visualization tools – Knowledge to use tools like Dash, D3, Tableau, Power BI, and QlikView

• Data wrangling – know how to deal with imperfections in data like missing values, inconsistent string formatting, date, etc.

• Multi-variable calculus and Algebra – Basic knowledge is essential for predictive performance and algorithmic optimization.

As you level up your data science jobs, you can become specialized in core skills while learning other secondary skills related to data science. This can be easily done by enrolling oneself with the respective skill training courses or programs online or offline.

Apart from this technical knowledge and skills, there are certain essential skills that one needs to learn through experience or by tuning up the existing skills. Most of the programs might not cover these skills as part of their syllabus, though there is a hint for it.
 
Let us dig deep on these essential skills any data science professional must develop during the course of his studies and career. The experience will make one excel in these skills. Take a quick rundown of these essential skills here.

Checklist: Essential skills for data science jobs


They are enlisted as follows -

Communication skills:


Organizations look for data science professionals who can translate their technical findings to non-technical team members. Generally, data science professionals are surrounded by non-tech teams including the marketing team or even the business leaders. One should be well-versed to communicate to the business persons to make decisions with quantifiable insights.

Storytelling is important and it is crucial to create a storyline around the data and make it understandable to everyone. Presenting a long data sheet will not convince non-tech people unless they are communicated in their language by sharing the insights, impact of data on business, and value it creates. Building a long-lasting relationship with teammates and clients is essential.

Intellectual curiosity


They say curiosity kills the cat. But in the data science field, it is one of the important skills. It is appreciated to be curious enough to update knowledge through regular reading. Make sense of data available and make data work for you is the adage for data science jobs. Curiosity enables a professional to sift through the data, find answers, and more insights with the available data or limited data.

Problem-solving nature and Business acumen


This is an essential skill. Being a data professional, one must have a solid understanding of the industry, know the organization’s problems, discern the problems based on its importance, and identify new ways to leverage data for business benefits.

It is recommended to understand the business operations to direct efforts in the right direction. Business acumen will help to do so.

Team player

This is a recommended skill for every employee including data science. Any employee cannot work alone. One has to work with executives, product managers, designers, marketers, clients, software developers, and customers. One has to work with everyone in the organization.

It is necessary to understand the right approach, address the use cases, translate the data into insights that everyone can understand. One should have the maturity to deal with people from various technical backgrounds.

Risk analysis and process improvement skills

One must be sharp enough to understand and analyze business risk, make improvements in processes, and how systems work. These skills work hand-in-hand.

Building risk analyses at the start of a data science project or a model will help to mitigate risks or avoid risks. This leads to greater customer satisfaction and business improvement. Connecting budget to process, organization risk, the impact of data, the result of a proposed model with advantages and limitations bring success in any of the project(s) undertaken.

To summarize…


Technical skills can be earned through learnings and hands-on-training. But essential skills can be earned through experience. One can hone the existing skills, while most of the essential skills will get ingrained as personality traits.

Dedication, efforts, and sincerity toward the job will lead to success always.
 


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