Seasoning Data Science Skills

Posted By: administrator
Posted On: 17 Feb, 2021

In this article, we will visit the unsung data science skills that can empower you if you are working – on data science already, looking to make a career in data science or switch in data science or you want to enhance your skillset from analytics to data science.

Let’s look at the basics first

What is Data Science?

To me, it is the application of science, mathematics, statistics to real-life problems, working on finding meaningful solutions by using some simple as well as some very complicated methods. The input to this process is simply the data that business SMEs have collected, domain experts, which can be utilized to extract insights, plan for future, forecast trends and make informed critical business decisions to enhance business. This process is supported by cutting-edge technologies such as Big Data, Data Mining, Machine Learning Algorithms and AI.

What do data scientists do?

A data scientist works day in and out with the data. The expectation from a data scientist is he delivers the value of the data. Value of the data is extracted by building and executing various machine learning-algorithms, ML tools and statistical analysis, related standard processes within the organization, such as optimization, recommendation engines or automated ranking systems. A data scientist takes such data from various sources; data goes through its cycle to reach a structure which can be accepted as an input to a data science model built. The model’s output is the value of the most used data to various business process experts.

Also Read 5 Important Qualities for a Career in Data Engineering

The skillset of a Data Scientist:

A data scientist wears hats of a data analyst who is an expert in statistics, fluent in programming languages such as Python, R etc., can quickly build ML models using standard ML tools and algorithms. He should understand the process of data pipeline ingestion and data wrangling processes and methods. Basic understanding of business domain knowledge would really add a great value. Most data scientists are also experts in data visualizations tools and various graphs, plots etc.

Tools mostly used: Python, R, SQL, Jupyter notebooks, AWS, Tableau, C/C++, TensorFlow, No SQL etc.

Data Science Process

Key-Value Adds:

  1. Help set short term, mid-term as well as long term goals.
  2. Identify the actual user data from the huge volume of data being collected on a daily basis.
  3. Deliver value of data for critical business decision making.
  4. Predict future outcomes, opportunities and leads.
  5. In some cases, the simulation models can even help predict critical metrics and their interdependent relationships and impact of crisis situations.

Roadmap to sharpen Data Science skills

  1. Joining, participating, sharing and continuous learning through the various DS communities available at a considerable scale.
  2. Participating and reviewing solutions in Kaggle competitions.
  3. Reading, browsing and contributing to the latest content on DS communities such as data community from IBM, Reddit etc.
  4. There are many researchers and senior data scientists who post and share a lot of important information on various platforms such as LinkedIn, DS blogs etc. Following them helps learn and build a network.
  5. Attending Webinars, Workshops and Events to learn, meet peers.
  6. A few online courses also add a great deal to your skillset.

Now let’s look at the communities that help take your DS skills to a higher level.

The most popular one is Kaggle. This is a great platform to shine your skills, share and learn with other peers across the globe. Kaggle is known to have more than 3 million users across the world. Kaggle allows users to share their own work, their data sets, their scripts, algorithms and the approach taken to solve a particular problem. This is a great platform to learn.

Communities that are actively providing the more relevant and recent content to master DS skills are many.

List of a few popular ones below:

  • Quora Data Science
  • Open Data Science
  • Data Community DC
  • IBM data community
  • Reddit
  • Data Science Association
  • Data Science Central
  • Dataquest – Slack channel
  • Deep Learning on Udacity – slack channel

In addition to the content on above, wide-scale communities a few DS influencers and experts publish many relevant blogs. Following such content keeps you updated with

Data science, ML and AI related Blogs.

A few blogs are listed below:

  • Statistical Modeling, Causal Inference, and Social Science
  • FastML
  • Data Mining Research

Events to look forward to:

  • Data Science in the Post-COVID World
  • Open Data Science Conference
  • The AI Summit New York
  • Data 2020-2021
  • Big Data 2020-2021
  • Data Integrity: The Next Business Imperative

Online Courses to Enhance you Data Science Skills:

Many online platforms offer a hands-on learning support and exposure.

List a few below:

  • The Open Source Data Science Masters
  • Data Camp
  • Data Science Academy
  • School of Data

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