10 Invisible Secrets of Data Scientists

Every day, humans & machines are successfully investing in contributing 2.5 quintillion bytes of data, it is so much that 90% of the data accretion on the internet has been built since 2016.

In 2010, it was 200qn bytes which grew up to 410000qn in Feb 2020. (1qn byte is 10^18 bytes on a short scale and one million million million in long-form). This comes from everywhere across the globe. It could be a Facebook message from a friend or the discovery of a meteor in another galaxy. All these pieces of information consist of disorganized or sloppy data which offers a great challenge in terms of analysis either by humans or any automated machines. A good data expert can dig gold mines, if he can make sense of this data which leads to the evolution of Data Science and usage of Big Data, Analytics, Machine Learning, AI and other fields.

As per Gartner’s ‘Magic Quadrant report’, Data Science and Machine Learning are the two most emerging technologies which can re-shape the future. Data Science, as we know is a blend of various tools, algorithms, and machine learning principles to discover hidden patterns and meaningful insights from raw and unstructured data. Experienced Data scientists have some well-kept secrets which make them expert in this field.

Statistics: Processing complex problems in the real world is very challenging with some messy data structure. The user’s primary focus would be to separate random noise from the data source and make the thread workable. Statistics helps in acquiring meaningful insights from data by performing mathematical computations on it.

Visualization: No humans speak in 1’s and 0’s. To get more transparency for any business solution, data must be transferred from binaries to a visually accepted form. Developers need to have a better view of the problems before proposing their solutions. Here, Visualization indicates converting data to a simple form.

Domain Knowledge: Even if it is health care or rocket science, technology is the only area where data scientists differ from each other. The main advantage of Data Scientists is that they can adapt to any language or technology within a few weeks of practice. Once Data scientists acquire this knowledge, they will be capable of defining precise solutions for any problem based on the environment and experience in that domain.

Data Mining: This is all about how the information is getting extracted from a distributed dataset. This process involves interrogation of the data, looking for trance and finding crucial information from the existing data set. This requires intensive computational and creativity skills. The process is used for transformation, cleaning, data integration, and pattern analysis.

Database and Data Processing: This includes cleaning, storing and manipulation of the data to dig actionable insights out of it. The information which is collected from multiple sources needs to be transformed and loaded to the systems the way user needs.

Communication: For a data scientist, knowing the answers for all what and why questions are the initial steps for the process. Communicating your findings and feeding solutions to the audience is the last step. For this process, 60% of the data will come from records. For the balance of 40%, you need to dig holes, communicate with several departments or sit with Directors & Janitors. A successful Data Scientist is also a very good listener.

Presentation – Exhibiting the data in an attractive and useful manner to the viewers. As a data scientist, you are considered as the gatekeeper between insights and people. Even though you can write millions of code to create a solution, your eye-catching presentation makes the work complete. If you are incapable of presenting the output to the key decision-makers, the whole damn work is trash.

Real-life Practice: As the old saying, practice makes you perfect. The best and easy way to achieve real-life experience is to manage or build small projects. You can get sample data from the internet or can collect it from any known stores or retailers. Identify some Open source projects and contribute to its work, Clean some funky DBs and make insights from it by exploring or making predictions.

Programming – The better you talk to machines, it gives you the desired outcome. Learn how machines behave with your technical skills. Data scientists usually prefer to code with some commonly used programming languages like Python, R, Java, Julia, Scala, SQL. Python seems to be preferred by most data scientists as it ends up faster compared to others.

Creativity – Enable your curiosity to generate fresh ideas and guide your brain to find those mind-blowing insights. If you want to grow as a great Data scientist, you must discover innovative solutions in every step. Listen to the saying, It’s not Newton who discovered Gravity, it’s the curiosity inside him.

Explore and understand these basic secrets and be crazy to solve hard problems.

By: Joys Joy


Trends That Will Disrupt Your Data & Analytics Strategy in 2020–2021

Continuous Intelligence, DataOps, Data Democratisation, and Data Meshes are the four major trends in data & analytics I’ve been


Maturing Data Governance and Data Literacy to Serve Organizations Better

Maturing Data Governance means growing an organization’s conception and management of its data assets from a narrow IT project concepti


To Be a Data-Driven Enterprise, Become Data Literate First

(EN) In many organizations today, we hear CEOs, CDOs, and other executives crying out for their enterprises to be more data driven. Certa

developed by Avilasoto