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What are the Core Skills needed in Data Science?


Data are becoming the new raw material of business.”-Craig Mundie, Senior Advisor to the CEO at Microsoft

In a fast-paced technology-driven world, Data becomes the ‘the new oil’, which flows like bloodline to every business decisions and strategies such as launching a new product, expanding a new line of assembly, improving the marketing campaigns, challenging the credibility of accounting records and building models for fraud detection. Data Science is reinventing the approach by how companies are solving their complex problems. The goal of every data scientist functions much like an oil refinery, converting data into insights that can both save money and generate capital.

“Without a systematic way to start and keep data clean, bad data will happen.” –  Donato Diorio

Hence, Data Science is about extracting, analyzing, visualizing, managing and storing data to create insights.

It is an interdisciplinary field which integrates skills from different areas in Mathematics, Programming and Business Management. Since it is essential to harness the power of massive data and processing power using technologies to provide smarter decisions, then it will be helpful to break down the Core Data Science Skills needed to thrive in the broad field of Data Science.


Building the muscle memory of solving problems and critical thinking are vital to become a successful Data Scientist. Business problems will require multiple approaches to derive creative solutions in line with the business requirements.

Linear Algebra and Calculus are branches of mathematics which theoretical concepts will be used widely to formulate functions / conditions to train algorithms. These algorithms are vital in solving problems in Machine Learning which are computer implementable instructions to perform calculations.

Regression Analysis, Forecasting and Probability and Statistics are essential tools to analyze the data and used as a modeling technique to investigate the significant relationships of variables and therefore build predictive models out of it.


The skill of programming will become the backbone for analyzing big data, developing models and conducting algorithms for solutions.


Python is a high-level programming language that is widely used in Data Science for data analysis, data manipulation and data visualization. This is also used for Web Development, Machine Learning, Scripting, Administering servers and Creating games. It also supports a wide variety of libraries.


This program is essentially useful for statistically oriented tasks and ad-hoc. R is used by Data scientists to perform efficient statistical and numerical analysis of large data sets for years. This language has all the benefits of analytics, optimization, statistical processing and machine learning.


Standard Query Language is a database language for interacting with relational databases. SQL is essential for data retrieving, extracting, manipulating and querying databases to extract tables from large data sets. Since many industries have geared their product management to CRM and Business Intelligence Tools, creating test environments to experiment data is a must to prepare the data for the next process of analyzing


SAS is one of the oldest languages for statisticians. This is a secure and stable platform for creating analytical requirements. This programming language has a lot in common with R both focusing on statistical analysis, advanced analytics and predictive modeling. While R is an open-source programming language, SAS is a closed source software.

Machine Learning

A baby learns to crawl, walk and then run. We are in the crawling stage when it comes to applying machine learning.”  – Dave Waters

These are autonomous models produced by a group of techniques of algorithms to derive solutions, predictions and investigations from the raw data. Machine algorithm types are Supervised, Unsupervised and Reinforcement Learning. These types are used depending on the approach to be used to solve the business requirements.

Supervised learning explicitly identifies the target or the required data sets. For example, a person has collected pictures of books, then data, scientists can train a model which can be recognized if the picture presented on the system is a book photo. On the other hand, Unsupervised learning is clustering the data and the model learns through observation and finding structures in the data. Imagine that a person has pictures of a cat without information about the requirement of identifying the breed. Therefore, the data scientist can train a model to cluster the data into a dataset which will produce repetitive pictures of similar looking cats. The goal of reinforcement learning is for a model to learn in an interactive environment by trial and error until it produces the highest frequency of accuracy.

Business Management

Effective Communication — Data Visualization is an integral part of Data Science. It will become the bridge between the technical team and the management. Valuable insights gained from framing the questions appropriately in line with the business requirements can make or break the strategy to drive proper solutions.

Business Sense — Data Scientists will not just work with numbers and codes, understanding the business itself will have a huge impact on focusing on the right solutions and workarounds. They will immensely provide support on future success and growth of the industry through data.

Domain Knowledge — This means the knowledge of the environment in which the target operates. This is particularly useful in optimizing machine algorithms. The advantage of knowing the domain can metaphorically be described as for navigating into an unknown island equipped with maps and compass.

The job of Data Scientists start with asking the right questions

In conclusion…

Data Science is a broad field encompassing data wrangling, data representation and transformation, data visualization, predictive analytics and machine learning. 

In order to start to a vast discipline, one must need to understand the theoretical concepts of integrated fields and develop the capability to perform the necessary tasks. The process will take time, personal growth and experiences. Remember that the learning curve can become steep as Data Science is still new to almost every industries and exposure to the right projects will be essential. Finally, deciding to become a data scientist can be rewarding as the intention of doing something worthwhile values the impact of good credible data coming into life.