Blog posts from the Jovian data science community


Deep Learning algorithms have become extremely powerful tools over the last couple of years because they can perform much better than humans in certain tasks. However, they are not very good at solving tasks that they have never seen before, even the most simple ones. Below one can see three examples of tasks that a human would be able to solve within a few seconds, whereas a DL algorithm wouldn’t - unless trained specifically for that particular kind of task.

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Analysing the top Youtube channel ‘T-Series’ using Python

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Image source: PHD Media

Have you ever wondered how YouTube Analytics works? How any Youtube channel is growing? How to check your channel’s progress? How YouTube shows trending videos? How to check the statistics (i.e subscribers, likes, dislikes, views, comments, etc.) of any channel? How websites like Tubics, Social Blade, etc. analyse any YouTube channel?

No? Don’t worry. ✌️ I have made a small project based on it. Click here YouTube Channel Analysis for complete source code and to check how it works. You’ll get an idea about what’s happening behind.
Note: Understanding of Python programming language is required.

It’s obvious that we need data to analyse something. So, we download the data from T-Series : the top YouTube channel, using YouTube Data API and Python. Then we prepare our dataset in CSV format and remove the unwanted data. Once we’ve prepared and cleaned our dataset, we can use it for analysis and visualisation of our YouTube channel. …

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The objective of this project is to deliver insights to understand customer demands better and thus help developers to popularize the product. The dataset is chosen from Kaggle. It is of 10k Play Store apps for analyzing the Android market. This dataset contains details of different applications and reviews from different users.

Discussion of Google play store dataset will involve various steps such as:

  • loading the data into data frame
  • cleaning the data
  • extracting statistics from the dataset
  • exploratory analysis and visualizations
  • questions that can be asked from the dataset
  • conclusion

We can move to first step of data analysis by cleaning the data that will make the results more accurate. …

Performed Exploratory Data Analysis through Python and visualization concepts to give valuable insights of data.

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Have ever thought what is Data Analysis and how these giant tech companies make use of it? Here we have taken a psuedo facebook data to perform data analysis and help facebook to take intelligent decision to identify its useful users and provide correct recommendations to them.

Let’s first know What is Data Analysis?

Data Analysis isn’t one finite set of arrangement of functions with a start and an end. It’s our perspective towards the world around. It is collection of data which involves the interpretation of data gathered through the use of analytical and logical reasoning to determine patterns, relationships or trends. …

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Photo by freestocks on Unsplash

The Growth of an Empire

Netflix has been the leader of original content around the world for years. The streaming service launched in 1997 in the United States. Since then, it has grown tremendously through continuous efforts of expansion into new markets. One key factor that has definitely played a part in Netflix’s success is its content distribution. Let’s take a look at how the service decided to release movies and TV shows to its platform.

Above, its clear that the service began adding more content to its platform after 2014, gradually adding more movies and TV shows after each year. With competition on the rise, it only makes sense to stay on top by adding more original content over the years. …

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Data Analytics is the main driving force of change for HR Professionals across industries. Right from hiring the right talent to increasing the employee retention rate, HR analytics can change it all.

“Today HR has a seat at the table, and in order to maintain that business partnership, you need to have an analytics framework” — Andy Kaslow

For exploring the HR analytics domain, I have downloaded the IBM HR Analytics dataset from Kaggle. …

“Understand more on ourselves through analysis”

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After 6 week course with on Data Analysis with Python: Zero to Pandas. I am now finally able to do my Final Course Project that is related to WhatsApp chat analysis.

The reason why I choose WhatsApp as my analysis is because I always wanted to know more about my chat room behaviour. And after reading some of the notebooks from Prajwal Prashanth at It really inspired me to start this analysis as my Final Course project.

In this project, I will attempt to find out what normally I will do in a group chat with my friends such as the active hours we usually talk and the number of emoji we use in the chat. …

“ Don’t lose hope. When the sun goes down, the starts come out.”

A Depressed Boy
A Depressed Boy
Depression Is The Biggest Reason To Commit Suicide.

This is My Course i.e; Data Analysis with Python: Zero to Pandas, Final Course Project on Exploratory Data Analysis.

In this project, I will attempt to provide an explanation for what is probably the specific motives because of which human beings commits suicide in India (the usage of the dataset “Suicides in India”).


For this analysis, we are selecting the below dataset, which has well-categorized data of various reasons for suicides committed by people in India, along with the state, profession, and the year, when it was committed. …

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Como empezamos en el post anterior ( link ), seguimos analizando información de la LIGA PRO de #Ecuador 202.

(Cont…) Segundo Paso: Visualizaciones y Exploración de Datos

Vamos a analizar el peso de los jugadores

Este gráfico denominado BOXPLOT nos da un vistazo rápido de los equipos, nos dice que Liga de Quito tiene los jugadores con mayor peso, pero el Nacional tiene el promedio más alto. Más información de como leer un Boxplot aquí ->
Aquí podemos ver como se ve el peso de los jugadores por posición, los jugadores más pesados son los delanteros, luego los defensas y la final los mediocampistas. Los puntos que vemos sobre le bigote inferior o superior de denominan outliers o valores atípicos.

Vamos a analizar la altura de los jugadores

En el boxplot podemos observar rápidamente que existen más valores atípicos que en el peso, el equipo de Emelec es el que en promedio tiene los jugadores más altos, las primeras veces es difícil leer un boxplot, luego con la práctica puedes observar mucha información interesante.
En este análisis de la altura podemos observar que los defensores son los más altos en cantidad y en promedio

3.- Haciendo y contestando preguntas

¿ Cuál es el promedio de la altura y peso en cada posición ?

Nos damos cuenta que la estatura y peso de los delanteros y defensas son similares, los volantes tienen a ser más bajos y con menos peso

¿ Cuál es el promedio de la altura y peso por equipo ?

¿ Cuál es el equipo que tiene los jugadores más altos ?

¿ Cuál es el promedio de la altura y peso por equipo ?

¿ Cuál es el equipo que tiene los jugadores más altos ?

¿ Cuál es el equipo que tiene los jugadores más pesados ?

Asi llegamos a final de esta serie de 2 post donde pudimos hacer un breve análisis de los equipos ecuatorianos.

Link primer post ->

Contactos: —

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Indian Premier League

As finally, this year IPL Season 13 has started on Sept. 19, 2020 , the cricket mood is on. While watching the first match itself, the idea of analyzing IPL dataset struck my mind and luckily I found one dataset on Kaggle which contains the data of matches held between 2008–2019. So, I shall be analyzing that dataset only. Although it can be improved to a great extent, hope you like my work.

Data Preparation and Cleaning

We have selected the below datasets for the analysis which contains data related to IPL matches for the last 12 seasons :

Also for better insights into the analysis, do refer to my Jovian Notebook here. …

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