1. Introduction of Data Science

12 Top Data Science Skills

A domain of Important Skills for Data Scientists

We can divide the required set of Data Science skills into 3 domains

  • Analytics
  • Programming
  • Domain Knowledge

Domain of Important Skills for Data Scientists

This is on a very abstract level in the taxonomy. Below, we are discussing some Data Science Skills in demand

  1. Statistics
  2. Programming skills
  3. Critical thinking
  4. Knowledge of AI, ML, and Deep Learning
  5. Comfort with math
  6. Good Knowledge of Python, R, SAS, and Scala
  7. Communication
  8. Data Wrangling
  9. Data Visualization
  10. Ability to understand analytical functions
  11. Experience with SQL
  12. Ability to work with unstructured data

a. Statistics

As a data scientist, you should be capable of working with tools like statistical tests, distributions, and maximum likelihood estimators.

A good data scientist will realize what technique is a valid approach to her/his problem. With statistics, you can help stakeholders take decisions and design and evaluate experiments.

b. Programming Skills

Good skills in tools like Python or R and a database querying language like SQL will be expected of you as a data scientist. You should be comfortable carrying out different tasks of programming activities. You will be expected to deal with both computational and statistical aspects of it.

c. Critical Thinking

Can you apply an objective analysis of facts to a problem or do you render opinions without it? A data scientist should be able to abstract the paydirt of the problem and ignore irrelevant details.

d. Knowledge of Machine Learning, Deep Learning, and AI

Machine Learning is a subset of Artificial Intelligence that uses statistical methods to make computers capable of learning with data. For this, they shouldn’t need to be explicitly programmed.

With Machine Learning, things like self-driving cars, practical speech recognition, effective web search, and understanding of the human genome are made possible.

Deep Learning is a part of a family of machine learning methods. It is based on learning data representations; learning can be unsupervised, semi-supervised, or supervised.

e. Comfort With Math

A data scientist should be able to develop complex financial or operational models that are statistically relevant and can help shape key business strategies.

f. Good knowledge of Python, R, SAS, and Scala

Working as a data scientist, a good knowledge of the languages Python, SAS, R, and Scala will help you a long way.

g. Communication

Skilful communication- both verbal and written, is key. As a data scientist, you should be able to use data to communicate effectively with stakeholders. A data scientist stands at the intersection of business, technology, and data.

Qualities like eloquence and storytelling abilities help the scientist dilute complex technical information into something simple and accurate to the audience. Another task with data science is to communicate to business leaders how an algorithm arrives at a prediction.

h. Data Wrangling

We have seen this with Python Data Wrangling. A lot of data you will be working on will be messy. Values could be missing, there could be inconsistent formatting with dates and strings. You will need to clean and wrangle your data.

i. Data Visualization

This is an essential part of data science, of course, as it lets the scientist describe and communicate their findings to technical and non-technical audiences. Tools like Matplotlib, ggplot, or d3.js let us do just that. Another good tool for this is Tableau.

j. Ability to Understand Analytical Functions

Such functions are locally represented by a convergent power series. An analytic function has its Taylor series about x0 for every x0 in its domain converge to the function in a neighbourhood.

These are of types real and complex- both infinitely differentiable. A good understanding of these helps with data science.

k. Experience with SQL

SQL is a fourth-generation language; a domain-specific language designed to manage data stored in an RDMS (Relational Database Management System) and for steam processing in an RDSMS (Relational Data Stream Management System).

We can use it to handle structured data in situations where variables of data relate to each other.

l. Ability To Work With Unstructured Data

If you are comfortable with unstructured data from sources like video and social media and can wrangle it, it is a plus for your journey with data science.

So, this was all in General and Demanding Data Science Skills. Hope you like our explanation.

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