How to make confident decisions using Data Science – IQ Online Training

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How To Make Confident Decisions Using Data
Science?
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Quality of Data is always important than Quantity

• If the quality of the input is of low quality,
  even the best predictive model could show the
  worst results, which are not reliable. So first,
  we need to understand, the quality of data is
  more important than gathering more data. To
  evaluate the data whether it is accurate,
  complete and consistent, we need to know from
  where and how it came together.

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Learn Skills For Tomorrow

• Data literacy is one of those skills, which is
  necessary for almost everyone in the digital age.
  The employees of an organization should be
  prepared for tomorrow by improving their skills
  of understanding, analyzing, and most importantly
  questioning data.
• Some employees may fear for change of a cultural
  shift but they need to be educated about the
  importance of learning data literacy. The
  importance of including Artificial Intelligence
  (AI) in the workplace to support their role has
  to be made clear. Once implemented and we start
  seeing changes, organizations need to take
  feedback and be ready for the open challenges.

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Learn And Upgrade By Doing

• Although organizations train their employees to
  hone skills, they need to participate in other
  ways of learning as well such as blogs, webinars,
  video playlists, etc. These fill them with
  confidence in the new technology.
• The technology should be learned to use as a part
  of their work culture but should not feel like an
  extra burden by the employees. The aim should not
  be to turn a companys marketing team or
  accountants into Data Scientists but enhance
  their skill set with the technology in their
  particular role.

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• With the knowledge of Data Science or Artificial
  Intelligence, an employee can analyze and
  interpret enterprise data avoiding any ambiguity.
  Natural language processing helps users in
  finding answers through a search-engine like an
  experience.
• The new technology might be of use in little
  things but very useful things that reduce the
  work. It could be a small machine-generated
  alert, but once the user gets the correct
  forecast and benefits from the insights, his
  confidence in the technology grows and curiosity
  to learn increases.

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Start Small Go Big

• Small things matter a lot. Even a small feature
  in AI can give greater insight. A small and
  innovative project in AI could turn into success
  and can then scale it up across the entire
  enterprise. For this to happen, the right
  employees in the team are much necessary. A
  person with the right mindset to identify
  problems and test hypothesis are better than with
  a good understanding of the algorithm.
• A curious, self-motivated who like to try and
  fail frequently can do the work better in data
  science technology. Such employees would decipher
  the mystery of data science and help enhance the
  projects in the organization.

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Go With The Right Approach

• Data Science techniques are complex and most
  time-consuming. Therefore, buying pre-trained AI
  models that suit your needs and could automate
  complex processes could be a better idea.
  Choosing the right solution will help users about
  the data science process and the factors
  considered during the model creation. It creates
  transparency about the reliability and accuracy
  to the users and helps them in understanding the
  steps in the process that fail and how to rectify
  them. In such a process of corrections, users
  will adopt the best practices for the
  implementation.

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• Even though Data Science is based on logical
  thinking and mathematics, there is never a single
  right answer, since there are many approaches to
  a single problem or a question. The key is to
  find a solution that fits everyone by conducting
  various experiments and improving continuously.
  The entire process is to create a trust between
  machine learning models and the users by
  delivering valuable information to the business.

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