10 Key In-Demand Skills in The World of Data Science


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“If you wanna do data science, learn how it is a technical, cultural, economic, and social discipline that has the ability to consolidate and rearrange societal power structures.” – Hugo Bowne-Anderson

Data Science is a trending discipline that has been in the limelight because of its modern-day features and the huge advantages that it offers to humankind. There are increasing many who are keen to develop and master the art of data science. Organizations are wanting to adapt to this technology for a real-time and optimum value of their data chunks. For this, they seek the appointment of expert data scientists who can, with latest techniques, help manage and extract valuable information.

Data scientists are professionals who must have expertise in multiple areas like data analysis, scientific methods, statistical techniques, data-driven technologies, handling structured and unstructured data, trend predictions, etc. Apart from these general skills, they must possess certain specialized technological skills with which they can garner the best of business benefits for organizations and raise the bar of Data Science high.

Before we move on to understanding those skills in detail, let us glance through what is data science and its related benefits.

Data Science – An Overview

Data Science is a trending technology that focuses on getting data ready for analysis, cleansing, aggregation, and manipulation for enhanced data analytics. Data scientists are then supposed to watch the outputs to extract the current trends and patterns for better and more insightful decision-making.

It is a part of statistics and applied mathematics that makes use of a huge amount of complicated information or Big Data to offer valuable information. It merges various areas of expertise and with the help of computational statistics, it helps in analyzing loads of data. The basic non-technical characteristics of data science and soft skills that data scientists must possess are business understanding and acumen, curiosity, problem-solving, critical thinking, communication, collaboration, and intuition.

Key Advantages of Data Science

Insightful information from historical data through effective tools
Business optimization and more revenue generation
Better marketing and development of products
Data-driven recommendation and personalization
Real-time intelligence and business predictability
Increased data security and interpretation of complicated data

10 Important Skills Data Scientists Must Possess

Machine Learning & Deep Learning
Python and Other Programming Languages
Cloud Driven Computing
Linear Algebra, Matrix Algebra and Mathematical Calculus
Artificial Intelligence (AI)
Predictive Analytics and Modeling
Data Visualization
Analytical Tools (Apart from R and Python)

R – Programming Language

R is a popular programming language that is an indispensable ingredient in the field of data science. R is considered critical when it comes to getting important statistics and graphics, from bulky datasets. It is leveraged across multiple industry sectors like eCommerce, healthcare, finance, and more. R has great community support through different forums that are prompt in answering any sort of queries that developers have.

Being free, open-source software, R has been considered ideal by data scientists for extracting, shaping, and analyzing information from datasets. It is used for machine learning visualization and statistical data analysis. The statistical functions in R make it simple to manage and analyze the data. It can be equipped with any IDE for making working with software packages, an easy job. RStudio is one such IDE that enhances the availability of graphics and has a syntax highlighted editor.

Machine Learning & Deep Learning

Data scientists have been extracting the best of potential from modern-day technologies like machine learning and deep learning. While data science focuses on studying data and extracting meaning out of it, machine learning looks at the different tools, techniques, and algorithms that can help create models that can self-learn. Deep learning is an extended arm of machine learning that offers neural network techniques for computers to perform just as humans do. Insightful information is obtained from heaps of data through these technologies.

It is important for data scientists to learn machine learning and deep learning since many organizations hire resources to implement machine learning apps for analyzing patterns, trends, and errors. Hence, having training in different types of machine learning methods is essential for data science experts. Data scientists must have detailed knowledge about decision trees, k-nearest neighbor, logistic regression, linear regression, random forests, k-means clustering, and more.

Python and Other Programming Languages

As such, it is best if data scientists are well versed with programming techniques and languages. If not all, Python is one well-known language in the data sector arena that is easy to learn and important to master. The data structures in Python are best for data science-related applications. Data scientists use Python for many tasks such as creating simulations, online scraping, etc. Since Python is open source in nature, it is considered vital for data science algorithms and applications.

As for other programming languages like SQL, C, C++, Java, etc., it is vital for data science experts to have know-how about how to program and move ahead, if not in detail. Basic knowledge about these technologies apart from R and Python is a must for enhanced statistical programming, web and application development, dynamic typing, and binding along with other important activities.

Cloud Driven Computing

Moving to the cloud is inevitable now for organizations big and small. Most IT infrastructure is now looking at the cloud, to maintain their infrastructure, without any hassles. Learning cloud computing and associated skills is a must for implementing data science applications. Since cloud-based computing helps companies to scale their IT framework based on requirements, cloud computing with data science competencies would be the right approach.

As the world moves towards a ‘work from anywhere’ model, the need for adapting to the cloud-driven infrastructure is a must. Data scientists must be well versed with cloud-based technologies like DevOps, Azure, AWS, Java, Docker, etc. With the cloud, centralized servers can be created for better management of data. That makes it easy for scientists to access and avail data, at any point of time. Organizations can leverage the cloud for data hosting and there is no need to bother about managing servers also. The cloud has brought in data independence and easy computing of data, making the world of data science much easier and more accessible.

Linear Algebra, Matrix Algebra, and Mathematical Calculus

Linear algebra is an important arm of mathematics that is widely used in the fields of data science and machine learning. There are many machine learning algorithms and models that are shown in matrix form. Even datasets can be shown as matrices. Linear algebra is leveraged for data transformation, pre-processing, and evaluating models. For data scientists, it is important that they can use the mathematical fundamentals in the data science techniques and models, for better accuracy and security.

Data science needs multivariable calculus formulas and other algebra skills for simply complex analytical issues with high-dimensional information. Training of artificial neural networks is also important in data science for huge volumes of data, through different mathematical calculations and formulas. Matrix algebra is used for optimization and cost functions, dimension reduction algorithms, support vector machines, and time-series analytics.


Statistics lies at the fundamental core of machine learning algorithms and data science applications. It is apt for extracting and analyzing data patterns for getting insightful information. Data scientists utilize it for gathering, reviewing, and getting results from data, by applying different mathematical models to it. For any data scientist to excel in his/her work, it is imperative to possess effective statistical capabilities. For that, they must get sufficient training on statistical techniques and methods that are useful in data science and ML.

Possessing good analytical and statistical experience offers better management of data through different algorithms. Statistical tests are performed on a variety of datasets to check for trends, patterns, and further analysis. Data scientists also perform the role of a programmer, business analyst, researcher, etc. for which they need the fundamental knowledge of statistics and related techniques.

Artificial Intelligence (AI)

As a booming technology, Artificial Intelligence is the competence of a computer or robot that is managed through a computer, to perform activities that are generally done by humans. It needs human intelligence. Data science leverages AI to extract predictions for data transformations and further analysis. AI helps in automating data analysis functions and betters the prediction outputs in terms of accuracy.

AI has been integrated into the data science forum in terms of getting real-time and insightful information from the huge bulks of datasets. Data scientists look at learning AI techniques as an added advantage to manage processes like cognitive science, statistics, computing techniques, information science, etc. from the bulk of a variety of data – structured and unstructured data.

Predictive Analytics and Modeling

As an important process in the world of data science, predictive analytics looks at the creation of predictive models that reproduce the performance of the systems or models. Predictive modeling is more like a statistical method that uses data mining and machine learning for better prediction and forecasting of the possible future outcomes, based on historical information. It analyses the current data and depending on the model, generates futuristic statistics.

Data scientists need to master the art of predictive analytics and modeling quite effectively. They need to study the patterns in datasets for existing data/new information and based on that, derive the insightful information that can be used for different use cases. It can be leveraged for different use case scenarios like equipment maintenance schedules, clinical diagnostics, client analytics, preferences, etc. Predictive analytics is an important skill for data scientists to master.

Data Visualization

Visualizing data while working with huge sets of information is crucial in data science. Data scientists must have the competence to highlight the desired information in the recommended way and hence data visualization is highly important for them. Various techniques and methods must be learned by them through different tools so that there is effective collaboration between stakeholders while exchanging information. Data scientists must master the technical skill of different data visualization tools s that they can get the best out of them.

These two terminologies are bound together – data visualization is a subset of data science and hence must be taken up as an essential skill with easily understandable data that can be extracted. Data scientists and engineers must learn to develop a variety of data visualization outputs like histograms, bar charts, bubble charts, pie charts, heat maps, scatter plots, etc. This leads to better clarity of understanding and pattern identification with the proper transformation of analysis into attractive dashboards.

Analytical Tools (Apart from R and Python)

There are many analytical tools that are considered apt for data science. The prime most important ones are R and Python that are described above, but over and about that, it is recommended that data scientists have good knowledge about other analytical tools like Tableau, SQL, Hadoop, Spark, etc. As an in-demand data visualization and analytics tool, Tableau has been a preferred choice by many, in the data science arena, since it offers a wide variety of dashboards in real-time.

Other analytics tools can assist in getting the best of insightful data from the data sources and help in big data processing. Even big data processing tools are a need for data scientists to master since they help in managing the volume, variety, and veracity of information. A data scientist must have the proficiencies of a data analyst first before he/she moves ahead with other responsibilities.

Let Data Science Rule the World of Data

Data science is a powerful technology that is ruling across the globe amidst an array of industry segments. Having a strong data scientist as a resource is wanted by many and what better than the above-listed skills, to prove their worth! These skills are almost a mandatory asset to all aspiring data scientists or experienced ones.

Organizations are trying hard to recruit data scientists who have all the above-mentioned skills, but that is a tough task. At the same time, developers and enthusiasts are attempting their best to achieve the best of skills for becoming trained data scientist. Overall, the world of data science is shining bright!

Ankit Kumar
A Tech enthusiast, Business Development Manager for Software Development firm. A good listener and reader. Global Enterprise Web and Mobility, CRM and Sales Force Automation Consultant. Interested in New Web, Mobile App, Big DATA, BI technologies, Engaging with new people around the globe.


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