Top Data Science Skills Data Science Professionals Must Know

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Article Body: The field of data science has undergone tremendous changes and with competition rising every day, the aspiring data scientists are in the race to acquire either new skills or upgrade the existing ones to stay relevant and competitive.

While the traditional skills like mathematics, statistics, Python, and R among others are still relevant, they are no longer enough for modern data science professionals to succeed in the highly competitive field Data Science has become in the recent years.

The library of data science skills has made some space for new skills like –

  • Deep learning algorithms
  • Cloud Computing platforms
  • Agile way of working based on Scrum method

Some of these skills are completely new for data science professionals and are more on managerial track. For instance, the agile way of working that is based on Scrum method. Considering that agile way of working is more of a human resource team, it can be little surprising to see that agile is being practiced by some of the development teams all over the world.

Currently the data science roles are being filled by individuals whose original skills include software development, which has given rise to the role of Machine Learning Engineer. And as organizations are becoming data driven, it is interesting to note that numerous data science professionals or machine learning engineers are being managed as developers – making continuous improvements in existing machine learning algorithms.

The new role that emerged for data science professionals ensured that the professionals were well-versed with the Agile way of working that is based on the Scrum method.

But this is just one of the new data science skills. There are several more that have come up in the last one year.

Industry experts have divided the data science skills into eight categories, thus adding five more crucial data science skills to the already existing three skills as mentioned in the classical Conway Venn diagram. So now the data science skills were divided into eight categories including –

Categories 

Skills

Programming Languages

Python, R, Java, Java, C++, MATLAB, SAS, Scala, Julia

Mathematics & Statistics

Algebra & Calculus, Probability & Stats, Survival Analysis, Epidemiology

Business & Communication

Business Understanding, Critical Thinking, Communications Skills, Excel, Data Visualization, Tableau, PowerBI

Data Science / ML Tools/Methods

Data Cleaning / Prep, ML Algorithms, Scikit-learn, Text Processing, XGBoost, Unstructured Data, Kaggle, Reinforcement Learning

Software Development

Github, Software Engineering, Docker, DevOps, Kubernetes


SQL / Databases

SQL/Database Coding, No-SQL Databases, Graph Databases

Big Data / Cloud

AWS, Apache Spark, Dask, Microsoft Azure, Google Cloud, Hadoop, Other Big Data Tools, Other Cloud Computing Platforms

Deep Learning

DL algorithms, Keras, NLP, TensorFlow, Computer Vision, PyTorch, Other DL frameworks

The above list is just a gist of what all data science skills, modern data science professionals must know and master. Interesting thing to note here is that the modern data science is not a work of a unicorn but is managed by a team of people that include individuals with a different skill set of various job profiles like Researcher, Data Scientist, Data Analyst, Machine Learning Engineer, Business Analyst among others.

So depending on these profiles, there are about 13 core data science skills currently, which every data science professional must know. And the skills include 

Skill

Category

Percentage (Have skills)

Percentage (want the skills)

Percentage Have Vs Want

Python

Programming Language

78.8%

43.1%

0.55

Probability & Statistics

Math & Stats

73.4%   

38.7%   

0.53

Data Visualization

Business & Communication

71.6%

37.7%

0.53


Math (Algebra & Calculus)

Math & Stats

70.7%   

28.6%   

    0.40

Critical Thinking

Business & Communication

70.3%   

28.8%

0.41

Data Cleaning /Data Preparation/ ETL   

Data Science & ML Tools

70.1%

31.9%

0.45

Communications Skills

Business & Communication

69.4%   

33.4%

0.48

Excel   

Business & Communication

69.4%   

15.0%

    0.22

SQL

SQL/Databases

69.2%   

29.1%   

0.42

Machine Learning Techniques

Data Science & ML Tools

61.9%   

42.2%

0.68

Business Understanding

Business & Communication

60.9%   

34.9%   

0.57


Github

Software Development

54.2%

41.1%   

0.76


Scikit-learn

Data Science & ML Tools

52.3%

37.6%

0.72



Careful analysis of these skills will reveal that while there have been new skills added to the library there are certain skills that have diminished in their popularity. As data science professionals, you have to figure out which skills you need to master, and which skills you can let go off in the long run.

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