Data Science different explanation -- An introduction

Data Science different explanation – An introduction

There are a lot of engineers who have never been involved in the field of statistics or Data Science.
But in order to build data pipelines or rewrite produced code by Data Scientists to an adequate, easily maintained code many nuances and misunderstandings arise on the engineering side. For those Data/ML engineers and novice Data Scientists, I’ve made this series of posts.

I will try to explain some basic approaches in plain english and, based on it, explain some of the basic concepts in Data Science.

The whole series topics:

  1. Probability
  2. Bayes theorem
  3. Probability distributions
  4. Measures
  5. Correlation
  6. The Central Limit Theorem and Sampling
  7. Demystifying hypothesis testing
  8. Data types in Data Science
  9. Descriptive and Inferential Statistics
  10. Exploratory Data Analysis

Follow to continue conversation series.

Data Science different explanation – An introduction missed content.

“True logic of this world lies in the calculus of probabilities — James Clerk Maxwell”

Data Science often uses statistical inferences to predict or analyze insights from data, while statistical inferences use probability and its characteristics. So knowing probability and its applications are important to effectively handle Data Science Problems.

Below is an explanation of the probability as a Frequentists probability. As it is easier to understand from the beginner point of view.

Make sure to give a like for support and motivation.

Please follow the entire series for the fresh content for the above mentioned topics’s explanation.