Jovian Blog

The supply chain can operate more efficiently and provide better services by leveraging machine learning and visualization tools to make data-driven and analytical decisions. Here are five innovative SCM project ideas for all data scientists!

There are currently 194,000+ Data Science jobs in the United States which clearly indicates the massive demand for data science professionals. Here are the top five entry-level roles you can pursue in the data science industry.

Business intelligence (BI) tools are now a fundamental part of every organization. The two most well-known BI platforms on the market are Tableau and Power BI. Data from Gartner indicates that Power BI and Tableau are the top two business intelligence tools. Let's find out what makes these tools different from one another.

Have you wondered how Data Science can be helpful in the telecommunication sector? Unlike common beliefs, it actually involves a lot of data-managed operations and extraction. Here are some ideas for data science projects in this industry.

Your career simply won't take off without an outstanding resume, regardless of how many technical skills you possess in data science. Here are five valuable tips for building a job-winning resume for data scientists!

Although distinguishing between "data scientist" and "data analyst" can be challenging, the two are sometimes used interchangeably. This blog presents a detailed comparison between data analyst and data scientist to help you decide which role is better for you.

In today's data-driven world, data is often used to make informed decisions. When it comes to making business decisions, hypotheses are a crucial aspect of that process. In this blog post, we will walk through the various types of hypothesis testing that help data scientists come up with the right judgments and make better decisions.

Different probability and statistical distributions serve as the fundamental building blocks for numerous applications, including weather forecasting, market analysis, etc. This blog will walk you through some important types of statistical distributions that every data scientist must know about.

Regression algorithms identify correlations between dependent and independent variables. These algorithms help forecast continuous variables like housing values, market movements, etc. This blog explores the top five regression algorithms that are highly popular among data scientists.

Machine learning relies heavily on classification since it teaches computer systems how to classify data according to specific criteria, such as predefined attributes. Let us look at the top five classification algorithms that are highly popular among data scientists.

From robotics and self-driving cars to chatbots and virtual assistants, deep learning is everywhere. But are you aware of what techniques go behind the working of these self-driven cars or virtual assistants? Well, here come some deep-learning algorithms you should know!

Beginners often struggle with finding the right project ideas to help enhance their skills and knowledge in data science. This blog will help you find the most unique and exciting data science project ideas for beginners.

A Pattern code or Pyramid Pattern is made using the combination of stars or numbers and represents a specific pattern. In this article we are going learn to print a Left Half Pyramid also called an Increasing Star Pattern. The left half pyramid is one of the two basal pattern codes to be able to create complex pattern codes.

Folium is a powerful Python library that allows us to manipulate geospatial data and visualize it. In this blog post, you will have a good look at what is Folium and how to use it to plot geographic coordinates.

By analyzing user data and offering relevant insights, machine learning helps businesses provide their customers with a more customized experience. This blog explores five exciting projects around the application of ML in retail.

The demand for more qualified data scientists has been fueled by the growing popularity of big data and its potential impact on the healthcare sector. This blog is here to help you explore the five topmost machine learning projects in healthcare.

The finance sector, including banking institutions, trading companies, and fintech companies, is progressively implementing machine algorithms to automate tedious, time-consuming tasks. In this blog post, you'll be looking at 5 ML projects in Finance, along with their source code.

Pandas offers a variety of tools & methods to optimize the data loading, pre-processing, and analyzing process. Datasets with millions of rows can be processed using Pandas smoothly. map, apply, & applymap are such methods that allow element-wise modification of a Dataframe or Series.

Analyzing the model metric is one of the most crucial tasks while training any Machine Learning or Deep Learning model. It allows us to diagnose the model statistics if the model prediction is not up to the mark. In this article, I will discuss how we can log the model metric from model architecture, hardware metric and epoch data in Wandb.ai.

Natural Language Processing is a field of study focusing on computer understanding of human or “natural” language. In this article, we will explore the most basic NLP technique - The bag of Words model

*args and **kwargs are special syntax's used while defining a function to pass a variable number of arguments. We will look at how to use them in the code examples below. It is not complusory to use args and kwargs as parameter name, you can simply add the asterisk's but it is common practise to them so we will be sticking with that today.

In this notebook, we will learn how to download a Kaggle dataset using the open datasets library with an API token.

Kaggle is an online community platform for data scientists and machine learning enthusiasts. It allows users to find and publish data sets, explore and build models in a web-based data-science environment.

Python provides four basic inbuilt data structures that are: List, Tuple, Dictionary, and Set. To iterate through them, we use skills called Slicing and Indexing. Before getting into these, we need to understand the concept of an Index.

AVG() as an aggregate function is used to return a single value as the average of a stated numeric column. When used as a window function, it will still return the average of a specific column but instead of returning a single value, it will return the running average at each row/window.

Window Functions perform computation over a set of rows called windows and return an aggregated value for each row. In this notebook, we will look at how a window function is executed within a SQL query.

Window Functions perform computation over a set of rows called windows and return an aggregated value for each row. Aggregate Functions are used to return a summarised value of multiple rows that make some mathematical sense. You can use aggregate functions as Window Functions with the help of the OVER() clause.

The shell is the outermost layer around an operating system that uses utilities called commands to interact and access your computer's OS services. It allows users to enter a command instead of clicking buttons and returns a result within the terminal itself. There are several commands that you can use on the terminal. Below we will be looking at some basic and most frequent commands that are used to navigate through the filesystem.

An http status code represents whether a request from a client to the server was successful or not. These are attached to the web pages sent from the server to the user.

Walmart is an American Retail, Wholesale and E-commerce business. Sam Walton founded Walmart in 1962 in Rogers, Arkansas. His goal was to help people "Save Money and Live Better" which continues to be Walmart's guiding mission with "Every Day Low Prices(EDLC)" and great service.

Python is a powerful programming language yet extremely simple to learn. It is built around the philosophy of minimal code to get the same work done. This makes it a forerunner among other programming languages for extensive usage in the domains of data science and machine learning.

The aviation industry is a complex collaboration of multiple fields, from manufacturers to commercial airlines. Data collection and analysis is a key factor in the safety management systems. In this notebook, we will analyze aviation occurrence data provided by Transport Canada.

In this notebook, we will analyze and visualize dataset containing information on clickstream from the online store sales of pregnant woman clothing to deduce some intriguing insights.

Exploratory analysis and predictive analysis techniques help businesses to structure and recalibrate their pricing, marketing, inventory strategies and more in real-time. B2C e-commerce businesses generate voluminous amounts of data. In this notebook we will perform the exploratory data analysis for a multi-category store using Python, Pandas, Matplotlib and Seaborn.

EDA can help us deliver great business results, by improving our existing knowledge and can also help in giving out new insights that we might not be aware of. In this project, we are trying to analyse Global Cargo Data.

One of the most common situations while writing code is getting stuck on an unidentifiable error. In such a case, python's ecosystem helps us to trace back our function calls with the help of traceback reports, to find out the exception raised. Exceptions raised simply define the type of error our program has run into.

Random is a module in the Numpy library for providing random numerical data in any required data structure. It contains simple functions/methods to generate random numbers, permutations and probability distributions. In this tutorial, we will understand how to use these functions and create random data as per our needs.

Web scraping is the process of extracting and parsing data from websites in an automated fashion using a computer program. It's a useful technique for creating datasets for research and learning.