Data Analyst

Nanasaheb Shinde  • Peerlist

Data Analytics professional at Schenider Electric having 2+ years of experience with Excel,SQL, Python,Tableau

Problem and Background:Parental leave policies play a crucial role in supporting the well-being of employees and promoting work-life balance. However, there are disparities in the distribution of leaves between genders and industries. The analysis of parental leave data reveals that there is a significant difference in the allocation of leaves between paid and unpaid, as well as between maternity and paternity leave.According to the data, 58% of companies provide paid maternity leave, while 33% offer unpaid maternity leave. In contrast, only 7% of companies provide paid paternity leave, and a mere 2% offer unpaid paternity leave. This discrepancy suggests an imbalance in the support provided to new parents, with a heavier focus on mothers rather than fathers.Furthermore, the data indicates that maternity leaves tend to be longer than paternity leaves. Approximately 94% of companies provide longer maternity leaves, while only 6% offer comparable durations for paternity leaves. This discrepancy raises concerns about gender equality and the equal distribution of caregiving responsibilities.Additionally, the analysis reveals that the technology industry stands out by providing the highest number of parental leaves compared to other industries.Solution:To address the existing disparities and promote a more equitable approach to parental leave, it is necessary to implement comprehensive policies that support both mothers and fathers. By offering paid parental leave for both genders and ensuring comparable durations, companies can contribute to a more balanced and inclusive work environment.Methodology and Project Scope:The analysis of parental leave policies involved gathering data from various companies across industries. The distribution of leaves was determined based on whether they were paid or unpaid, and whether they were classified as maternity or paternity leave. The top 15 companies offering parental leaves were identified based on the number of weeks or duration provided.The project scope includes examining the distribution of leaves, identifying discrepancies, and analyzing industry-specific trends. It focuses on gathering data from a diverse range of companies to ensure a comprehensive understanding of parental leave policies.Goals and KPI:The primary goal is to promote gender equality and work-life balance through more inclusive parental leave policies. Key Performance Indicators (KPIs) for this project may include:1. Increase in the percentage of companies offering paid paternity leave.2. Reduction in the disparity between maternity and paternity leave durations.3. Increase in the overall percentage of companies offering parental leave.4. Reduction in the percentage of companies providing only unpaid leave.Concepts Used:1. Gender Equality: The concept of treating men and women equally in terms of parental leave policies, ensuring that both genders have access to paid leave and comparable durations.2. Work-Life Balance: The idea of creating a harmonious integration between work responsibilities and personal life, achieved through supportive parental leave policies.3. Inclusion: The practice of embracing diversity and creating policies that accommodate the needs of all employees, regardless of gender or caregiving responsibilities.Conclusion:The analysis of parental leave policies reveals significant disparities in the distribution of leaves between genders and industries. Maternity leave is more prevalent and longer than paternity leave, and the technology industry emerges as a leader in providing parental leave benefits.To address these issues, it is crucial to implement comprehensive policies that promote gender equality and work-life balance. Companies should strive to offer paid parental leave to both mothers and fathers, ensuring comparable durations. By adopting more inclusive approaches, organizations can contribute to creating a supportive and equitable work environment for all employees.

Parental Leave Policies Analysis

<h3 class="rich-heading">Problem &amp; background</h3>Analysing daily weather conditions in Madrid from 1997-2015.The dataset includes information such as max/min/mean temperature, dew point, humidity, visibility, and wind speed, along with precipitation events, cloud cover, and wind direction. The goal of the analysis is to answer several questions related to the weather conditions in Madrid during this time period. These questions include determining the percentage of days with precipitation, identifying the warmest month for planning a vacation, finding the date with the fastest wind gust, and comparing average visibility for clear and foggy days.Recommended Analysis:During the time period in this sample, what % of days had some sort of precipitation event? What % were clear?Suppose you're planning a vacation to Madrid and hoping for the warmest temperature. Which month might you plan to travel?On which date in the sample did Madrid see the fastest wind gust? What was the weather like on that day?How does the average visibility (Km) compare for clear days vs. foggy days?<h3 class="rich-heading">Methodology &amp; Project scope</h3>Methodology:The methodology for this project involved using Excel to analyse the daily weather conditions in Madrid from 1997-2015. The raw data was collected and compiled into a single spreadsheet, which was then cleaned and processed to prepare it for analysis. In order to answer the research questions, two additional columns were added to the spreadsheet: visibility and precipitation. The visibility column was used to categorise days as either clear or foggy based on the visibility range, and the precipitation column was used to categorise days as either having precipitation or being clear based on the precipitation amount.Project Scope:The scope of this project was limited to analysing the daily weather conditions in Madrid from 1997-2015. The focus was on answering specific research questions related to precipitation, temperature, wind, and visibility. The project involved cleaning and processing the raw data using Excel and adding two columns to the spreadsheet for analysis. The project did not include any statistical modelling or predictive analysis beyond simple calculations and comparisons. The goal was to provide insights and answers to the research questions using the available data.<h3 class="rich-heading">Goals &amp; KPIs</h3>To measure the success of the project, the following goals and KPIs have been established:Goal 1: To determine the percentage of days with precipitation and clear weather during the time period of the sample.KPI: Calculate the percentage of days with precipitation and clear weather using the additional analysis columns created in Excel.Goal 2: To identify the month with the warmest temperature in Madrid from 1997-2015.KPI: Analyse the temperature data and determine the month with the highest maximum temperature.Goal 3: To compare the average visibility (Km) on clear days versus foggy days.KPI: Analyse the visibility data and calculate the average visibility on clear days and foggy days separately. Compare the two averages to determine if there is a significant difference.<h3 class="rich-heading">Concepts Used</h3>Weather data analysis: This involves analysing and interpreting weather data to gain insights and make decisions.Data visualisation: This involves using charts, graphs, and other visual aids to represent data in a meaningful and understandable way.Excel: This is a software program commonly used for data analysis, which includes tools for organising, manipulating, and analysing large sets of data.Visibility: This refers to the distance at which objects can be clearly seen, and is often affected by factors such as weather conditions, air pollution, and geographic features.Temperature: This refers to the degree of hotness or coldness of the atmosphere, and is typically measured using a thermometer.

Madrid Daily Weather

The project can be useful for businesses, investors, and entrepreneurs who are looking to identify potential market opportunities and growth areas. By analyzing the data on various industries, states, and companies, the project can provide valuable insights into the business environment and market trends. The project can help businesses to make informed decisions about expanding their operations, investing in high-growth industries, and hiring new employees. Similarly, the project can help investors to identify promising investment opportunities, and entrepreneurs can take inspiration from the success of newly founded companies.

Inc.5000 Companies

This report analyses a dataset containing ratings and reviews for 15,000+ movies reviewed by Rotten Tomatoes. The dataset includes film details, featured reviews, "Tomatometer" status, and audience ratings. The analysis reveals insights about the distribution of ratings, genres, studios, and release dates, and discrepancies between Tomatometer ratings and audience ratings. The report concludes with recommendations for studios, filmmakers, and critics.

Rotten Tomatometer Movie Rating

In this report, we analyzed the factors that significantly impact web purchases, the most successful marketing campaign, the average customer profile, the best performing product, and the underperforming channel. Based on our analysis, we recommend certain actions to improve the performance of web purchases and increase revenue.

Marketing Campaign Results

This report analyses the telecom number churn rate for the last quarter of 2022. The report provides an overview of the data, identifies trends and causes, and provides recommendations to reduce churn rates in the future.

Telecom Customer Churn

The data on monthly pizza sales shows fluctuations in sales throughout the year, with some months having lower sales than others. The project may explore potential reasons for these fluctuations, such as seasonal factors, promotional activity, or changes in customer behavior.The data on time of day sales shows the breakdown of pizza sales during the morning, afternoon, and evening periods. The project may explore how this breakdown varies by category and whether there are opportunities to increase sales during certain time periods.Finally, the data on pizza sales by category provides insights into which types of pizzas are most popular with customers. The project may explore trends in pizza preferences, customer demographics, and potential opportunities for new pizza offerings.Overall, the Pizza Sales Project appears to be focused on using data analysis to better understand pizza sales trends and identify opportunities to improve sales. The project may include recommendations for marketing and promotional strategies, changes to menu offerings, or other tactics aimed at increasing sales and driving business growth.