An official is appointed who investigates complaints, made by citizens, independently, impartially and confidentially, learn everything about an ombudsman and its 7 types

Image for post
Image for post

The word “Ombudsman” evolved from the Swedish ombudsman, signifying “the legal representative”. Initially, the Swedish legislature made the designation of the ombudsperson in early 1800, with the translation of ombudsperson as “the official investigator of citizen complaints”. This person was acknowledged as “the person of legal capabilities and superior virtue.”

Understanding Ombudsman

The Ombudsman is independent, unbiased and gives cost-free services. He/she investigates complaints when problems have been commanded inappropriately or unfairly, or making citizens undergo unfair consequences, some…

Edsger Dijkstra published Dijkstra’s algorithm in 1959, implemented over a weighted graph, to find the shortest path, but how it works, read in the blog

Image for post
Image for post

A Dutch computer scientist, Edsger Dijkstra, in 1959, proposed an algorithm that can be applied to a weighted graph. The graph can either be directed or undirected with the condition that the graph needs to embrace a non-negative value on its every edge. He named this algorithm “Dijkstra’s Algorithm” at his name.

What is Dijkstra’s Algorithm?

What if you are provided with a graph of nodes where every node is linked to several other nodes with varying distance. …

A brief overview of the Confusion matrix in machine learning is explained in the blog for the classification-based problems in ML.

Image for post
Image for post

The confusion matrix is the most persuasive tool for predictive analysis in machine learning. In order to check the performance of a classification-based ML model, the confusion matrix is hugely deployed.

It provides information about how a machine classifier has performed, matching suitably classified examples corresponding to misclassified examples.

Let’s discuss the concept of confusion matrix in detail.

Introduction to Confusion Matrix

A confusion matrix is a summarized table of the number of correct and incorrect predictions (or actual and predicted values) yielded…

An introductory guide is provided explaining the notion of vital statistics, its importance, types and uses along with the note on vital statistics systems.

Image for post
Image for post

“If your experiment needs a statistician, you need a better experiment.”― Ernest Rutherford

As a scientific discipline, Vital Statistics is a subfield of demography and the study & research of characteristics of the civilized population. The term “vital statistics’’ is deployed to the individual determination of some vital events.

For example, the birth rate is an example of vital statistics and an investigation of trends in birth rates is an example of an application in the…

Let us learn the theory of conditional probability in context with definition, examples and properties through this blog.

Image for post
Image for post

Being a classical concept in probability theory, the conditional probability is one of the prominent approaches of measuring the probability of occurrence of an event, provided that another event has occurred.

Learning Objectives


*What is the conditional probability?

  • Conditional probability of independent events
  • Conditional probability of mutually exclusive events
  • Chain rule or multiplication rule
  • The law of total probability

*Properties of conditional probability

*Examples of conditional probability, and

*Ending notes


First, let’s catch the quick introduction to the concept of probability.

  • Can we…

Data Lakes vs. Data Warehouses, when and how to use, catch the difference between the two most famous options for storing big data.

Image for post
Image for post

Talking about buzzwords today regarding data management, and listing here is Data Lakes, and Data Warehouse, what are they, why and where to deploy them. So, in this blog, we will unpack their definition, key differences, and what we see in the near future.

“The world is now awash in data and we can see consumers in a lot clearer ways.” — — Max Levchin, PayPal co-founder.

There are several modes to stockpile big data, but the…

Image for post
Image for post

Natural language processing is the most promising field in data science and artificial intelligence that concerns in teaching computers how to elicit meaningful information from text.

“Artificial intelligence will reach human levels by around 2029. Follow that out further to, say, 2045, we will have multiplied the intelligence, the human biological machine intelligence of our civilization a billion-fold.” — Ray Kurzweil, American inventor and futurist

Through this blog discussion, you will learn the basics of Natural Language Processing(NLP), its usage and applications. After the brief introduction of NLP, you will see the next section emphasizing top 10 natural language processing…

An introductory tour about statistical modelling, top 5 statistical data analysis techniques and a note on statistical modelling vs machine learning is provided in this blog.

Image for post
Image for post

As Josh Wills put it, “A data scientist is a person who is better at statistics than any programmer and better at programming than any statistician.”

From the previous blog, you must have acquired a brief note about Statistical Data Analysis. In order to understand statistics properly, it demands one of the most important aspects as understanding statistical modelling. …

This blog lays out on the deeply discussed concept of personal finance that includes why it’s important, types, aspects, strategies and planning process

Image for post
Image for post

John C. Maxwell explains “A budget is telling your money where to go instead of wondering where it went.”

Let’s begin with defining Finance, “It is a comprehensive phrase that fully specifies explicit activities linked with banking, leverage or debt, credit, capital markets and investments, basically, it reflects the entire money management and the procedure of obtaining money according to requirement. …

Discrete mathematics and its major topics (branches) included combinatorics, binary trees, Boolean algebra, number, graph, probability, and set theory

Image for post
Image for post

Mathematics makes a nice distinction between the usually synonymous terms “elementary” and “simple”, with “elementary” taken to mean that not very much mathematical knowledge is needed to read the work and “simple” to mean that not very much mathematical ability is needed to understand it. — Julian Havel

In this blog, a portrayal on the perception of mathematics is bestowed that further explains a deep note on discrete mathematics and the topics come under it such as Combinatorics, Number Theory, Probability…

Neelam Tyagi

The Single-minded determination to win is crucial- Dr. Daisaku Ikeda | LinkedIn:

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store