Trending Data Science Skills for 2020

on November 15, 2019

Although the international popularity of data science as a career only caught up across the world recently, its echoes found its way long back in 2010. The latest explosion of data science opportunities, especially among the leading business capitals has made it one of the most booming and sought-after fields. According to an IBM report, there is going to be 110,000 fresh job roles for data science masters by 2020 with 15% growth.

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There is, no doubt, a huge demand in data science skills on a global technology podium which comprise of statistics, math, Python programming, machine learning, NLP and data visualization. Hence, if you are thinking to splash like a dilettante into data science or would like to be a committed data scientist specialist, you must first familiarize with top trending data science skills for 2020.

A career-driven mindset should keep an eye on the following data science skills and techniques:


1)Data preparation skills


data preperation skills


The first job of any data scientist before starting with data models is to prepare and organize appropriate datasets that meet business objectives. This process typically consumes a good amount of time because of the length of data recognition tasks. However, the time spent sizing up the right datasets is significantly minimized by recent developments in innovative data preparation methods. The riotous business of data filtering and data cleansing goes through different stages: additions, transformations, imputations. Thanks to modern data science frameworks, now you can eliminate writing and rewriting the code seen in conventional method and just automate different data preparation processes.


2)Data modeling skills


data modeling skills


At the core of Data science and analytics is the whirlwind job of data modeling which involves how effectively you model the given data. In this role, data scientists perform many rigorous tasks to create business-optimized data models that contribute to business objectives. The model is interpreted in the form of data analytics or data-processing applications. The goal should be to create self-service platforms embedded with advanced machine learning and natural language capabilities. The data modeling role majorly revolves around:

  • Efficient, dynamic data model creation
  • Testing a data model
  • Discovery of right quality of useful data
  • Machine learning capabilities
  • Operational analytics
  • Data model deployment

3)Applied AI and machine learning specialization


applied ai and machine learning


What earlier started as a latent futuristic consideration has now become the key attribute contributing to the evolved, automated operational model in various industries. AI and machine learning capabilities are demands of modern data-driven industrial systems. Data science professionals therefore need to possess a thorough understanding of how AI and deep learning work hand in hand to perform gamut of operational and business tasks. You will get accustomed to working in AI ecosystem, AI capabilities and impact as well as possible opportunities for AI-driven applications in enterprise systems.


4)Preemptive and proactive imputations


Preemptive and proactive imputations


By inserting missing links to existing datasets, organizations can prevent certain issues from harming the final outcome. With imputations, competent data scientists can insert missing information or values to data formats. They can take the average of present values, for instance, and add the missing numbers to complete the rather sketchy dataset.


5)Feature engineering skills


engineering skills


Featuring engineering skills is another key aspect of data science where data specialists find out what factors and attributes in datasets can drive expected outcomes of fully mature analytics models. Feature engineering prowess can be best explained by predictive analytics used for fraud detection in which certain ominous events are read as fraudulent activities. A machine learning algorithm of advanced data science incorporates feature engineering techniques which is more of a futuristic leap. It often seems to deal with multidimensional data that poses massive challenges.


6)Predictive Modeling techniques


predictive modeling


To generate the automated model of customer-centric operations, specialists need to ensemble a series of dynamic data models with predictive capability. Part of a mature data science skill set, this competency is helpful when stacking and combining an array of models or layers of neural networks to form a more accurate functional data model for effective multi-ensemble feature engineering.


7)Algorithm and interpretation without coding


algorithm and interpretation


More of an introductory data science skill, this side of data science have engineers understanding and applying predictive algorithms to real-time inputs of given datasets. With this ability, data experts can interpret and evaluate processed data output without involvement of any level of programming knowledge. Learners will gain expertise to design and test predictive data models.

Read Also: Is Data Science Difficult to Learn


8)Geographic location mapping and Spatial Analysis techniques


Geographic location mapping and Spatial Analysis techniques


The big part of big data analysis also comes from location-sensing services which includes GIS information and mapping systems (like Geo fencing). In this process, data science enthusiasts will learn techniques and tools along with new modern concepts to build accurate geographic maps. This part of data processing skills will explore the practical side of work and require you to learn using advanced GIS-focused software.


9)Python data statistics and programming


python data statistics


This also falls in the category of standard beginner-level skills that data science aspirants can gain from available academic courses. This kind of academic training takes you to the core concepts of data science including interpretation, statistics, mathematics, data model engineering and Python-based programming. At the end of the course, you feel prepared to move to the intermediate or advanced level in data science.


10)AI applications and neural networks


ai neural networks


In the intermediate or advanced scale of learning, you will have great opportunities to build AI applications. You will gradually understand how to create and train neural networks to identify images and interpret textual data of human world while improving network performance. In the process, you will also learn to instill NLP (Natural Language Processing) system and train machines to analyze and respond to human speech. You will have a total control over text processing, image recognition and neural network input data.


11)Clinical Data Interpretation skills


clinical data


This specialization stands for encouraging you to learn interpreting various electronic health record data structures of complex clinical settings by applying certain fundamental statistical and mathematical methods. Clinical data science skills enable you to present medical science information in the most interpretable fashion while eliminating barriers that block the implementation of problem-solving tools in clinical settings.


12)Python-based data-driven predictive analytics


python predictive analytics


If you have gone beyond the basic understanding of Python and have enough confidence to explore more of Python, this specialization is for you. In Python-based programming techniques, you will gain an upper hand in creating data strategy, statistical models for workflows and most relevant and precise predictions for an enormous variety of business activities. You will use assorted data sources to design data-based predictive model and leverage data science methods to drive meaningful insights. This is the most sought-after and highly rewarding talent you will cultivate to master the industry space.


13)Statistics with SAS


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Data science Software developers working on or familiar with SAS are often asked build solutions for statistical analysis. Software of this statistical nature usually involves dealing with numbers, facts and figures that ultimately lead to meaningful insight for businesses. It might fall in introductory course and specialization category and has SAS/STAT software users performing statistics such as t-tests, linear and logistic regression, ANOVA, etc.

Read Also: How to Switch to a Career in Data Science


Final remarks: Master your way into Data Science

Although there is a terrible shortage of data science skills clashing with boosts in data science roles, 21st century millennials are taking great interest in trending skills like data engineering, automated data models, feature engineering and more.

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These are not just trends, but a systematic opportunity to approach a promising career in the data science domain. Highly accessible, the domain inspires enterprise users to enable environments where data science aspirants can actively learn a great deal about statistics and data analytics techniques. This open-minded thinking to absorbing fresh talents in data science will possibly help bridge the talent gap in 2020.