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Pushpa Sree Potluri
Pushpa Sree Potluri

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Introduction to Data Science

Most of the people are under the misconception that data science is all about machine learning algorithms. That is not true. Data Science is a combination of mathematics, computer science and, machine learning.

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Data Science is a study of data, where you maintain datsets and derive insights from the dataset. Data Science uses different parts mentioned in the pattern below to solve the problems.

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Perception - try to identify patterns with the help of the data
Planning - involves two steps:

  1. Finding all possible solutions
  2. Finding the best possible solution among all solutions

What do you need to know to be a successful data scientist?

  1. Programming Knowledge
  2. Data modelling and evaluation
  3. Data Visualization and reporting
  4. Probability and Statistics
  5. Machine Learning techniques
  6. Relational Database knowledge

Let's get started with some basic terminology used in data science:

  1. Observations - data points in your dataset (rows)
  2. Features - variables in your dataset (columns)
  3. Target Variable - which you are trying to predict
  4. Train data - data from which your algorithm learns
  5. Test data - data to evaluate your model performance
  6. Model - set of patterns learned from the data
  7. Algorithm - specific machine learning process used to train your model

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