Overview:
This data contains job postings for data-related roles across various locations, companies, and job requirements. Key attributes include job titles, locations, job platforms, work schedule types, work-from-home availability, required skills, and posted dates. This data is ideal for analyzing trends in job titles, skills demand, geographical distribution, and remote work patterns offered by companies.
Project Goal:
The goal of this project is to analyze job market trends in the year 2023, providing valuable insights into the evolving demand for roles, skills, and locations.
Developed a comprehensive project involving data extraction and analysis (Python-based) with interactive visualization (Tableau). The analysis rigorously quantifies leading job roles and their geographical distribution, assesses remote work patterns, identifies top-tier in-demand skills, and evaluates educational requirements over time. This work delivers data-driven recommendations crucial for enhancing recruitment strategies and improving market positioning."
This project aim is to design and execute a fantastic personalized rewards program that keeps customers returning to the TravelTide platform.
My approach to customer analysis focuses on identifying booking patterns and behaviors. Developed a rule-based segmentation strategy that categorizes customers into eight distinct groups, considering factors such as booking frequency, spending habits, and preferences. This segmentation allows us to tailor specific perks and incentives that align with each group's interests, effectively encouraging them to choose our services for future bookings.
The project focused on conducting a comprehensive funnel analysis to identify areas for improvement. It aimed to address specific business questions, providing insights for optimization and recommendations based on data-driven findings.
About Metrocar’s User Funnel
Metrocar's business model is based on a platform that connects riders with drivers through a mobile application. Metrocar acts as an intermediary between riders and drivers, providing a user-friendly platform to connect them and facilitate the ride-hailing process The customer funnel includes stages such as app download, signup, ride request, ride completed and review. Drop-offs at each stage are analysed to identify optimization opportunities.
About Metrocar’s Ride Funnel
The ride funnel starts when the ride is requested and includes stages such as ride requested, ride accepted, ride completed, payment approved and review.
The online sector has been slowly eating up market share in the past two decades. E-commerce platforms like Unicorn allow people to buy products online: from books, toys, clothes, and shoes to food, furniture, and other household items.
The following dataset includes Unicorn sales data from the years 2015-2018.
The task is to analyze the data, find interesting insights, and identify weak areas and opportunities for Unicorn to boost its business growth
The project includes 4 main parts, each with their subtasks:
● Data Exploration by SQL
● Data Cleaning using Spreadsheets
● Getting Insights using Tableau
● Executive summary and presentation
Dr. Ignaz Semmelweis, a Hungarian physician at the Vienna General Hospital makes a study about childbed fever: A deadly disease affecting women that just have given birth. In the early 1840s at the Vienna General Hospital as many as 10% of the women giving birth die from it. It's the contaminated hands of the doctors delivering the babies.
He makes a breakthrough discovery of "Handwashing" and by enforcing handwashing at his hospital he saved hundreds of lives.
In this project, we're going to reanalyze the data that made Semmelweis discover the importance of handwashing. Let's start by looking at the data that made Semmelweis realize that something was wrong with the procedures at Vienna General Hospital.
Explore Netflix movie data and perform exploratory data analysis for a production company to uncover insights about movies from a particular decade.
This project aims to do some research on movies released in the 1990's to discover if Netflix’s movies are getting shorter over time using everything from lists and loops to pandas and matplotlib. The exploratory data analysis. This will allow you to perform critical tasks such as manipulating raw data and drawing conclusions from plots you create of the data.
Concepts:
Data Manipulation
Data Visualization
Python Basics and Programming
The Nobel Prize has been among the most prestigious international awards since 1901. Each year, awards are bestowed in chemistry, literature, physics, physiology or medicine, economics, and peace. In addition to the honor, prestige, and substantial prize money, the recipient also gets a gold medal with an image of Alfred Nobel (1833 - 1896), who established the prize.
The Nobel Foundation has made a dataset available of all prize winners from the outset of the awards from 1901 to 2023. The dataset used in this project is from the Nobel Prize API and is available in the nobel.csv file in the data folder.
Project Goal
Analyze Nobel prize winner data, explore data, identify patterns and answer several questions related to this prizewinning data.
Note:
This project has been done in two programming languages, the links to respective GitHub repositories are given below.
1) Python
2) R Program