This repository houses a collection of SQL code utilized in diverse projects dedicated to practicing my SQL, Python and Tableau skills.
Knowledge gained through YouTube tutorials, Coursera, DataCamp, Stratascratch platform
Certifications: Google Data Analytics, Datacamp Data Analyst Associate Certificate, IBM Data Analyst Professional
Visualizations: 1.Financial Report, StaffPerformance,| 2. Worldwide Homicide, Interactive dashboard | 3. Corruption and economic crime, Interactive Dashboard | 4. Cocaine Prevalence in population
Powerpoint presentation covering popular programming languages, database languages, current trends, demographics, preferred platforms, and future skills.
Presentation link: here,
Chart links: Dashboard, Dashboard2
In this project I have created 4 tables in SQL, performed exploratory data analysis in Python, and created dashboards in Tableau. All the data is fictional and a part of it was randomly generated using Faker library in Python.
Useful links: SQLcode, PythonCode, Dashboard-FinancialReport, Dashboard-StaffPerformance
In this Jupyter Notebook project, I conducted an analysis to explore worldwide meat production spanning over the past six decades.The primary objective of this analysis was to uncover patterns and distributions of production across different regions and over time. Throughout the project, I identified both the most and least produced types of meat, examining variations among countries. I have renamed columns, created data frames in order to aggregate columns, and plotted visualizations.
In this Jupyter Notebook project, I conducted data analysis to recognize sales trends and distribution patterns. I precisely identified states and cities generating the highest and lowest revenue, as well as specified the most frequently sold products. These insights were effectively represented through visualizations to enhance understanding and interpretation.
In this Jupyter Notebook project, I conducted a thorough data analysis to pinpoint food items with the highest protein and sugar content, examine the distribution of kcal, and explore relationships between values. To enhance clarity, I renamed columns and addressed null values by replacing them with the mean. The analysis provides valuable insights into nutritional aspects.
This IBM Data Analyst exam project involved data manipulation, including value replacement and column dropping, along with creating plots to visualize variable relationships and identifying outliers and correlations. I implemented linear regression, ridge regression, and polynomial regression, as well as pipeline objects, and calculated R^2.
In this project, I collected data on human evolution fossils from Wikipedia using web scraping techniques. I have cleaned it Python, removed duplicates, renamed and deleted columns, performed string cleaning, and manually modified strings.
In this project, my focus centers on thorough data exploration and analysis with the primary objective of identifying the most profitable and top-selling items. Additionally, I aim to unveil revenue trends over distinct time intervals such as quarters, months, days, and hours, providing a broad understanding of sales distribution patterns. To gain deeper insights, I explore order frequency, aiming to pinpoint the peak operational periods within the shop. This analysis serves as a valuable resource for stakeholders, offering actionable insights to enhance overall shop efficiency through optimal stock and staff management strategies. Tools used include SQL and Tableau. I have performed table alteration queries, aggregation, data type converting, date formatting, and used CTE's. To present the findings in a user-friendly manner, I have designed interactive dashboards using Tableau.
Revenue Dashboard, Revenue Distribution
In this project, I performed data analysis to determine the most and least profitable items within a pizza restaurant, pinpoint peak operational times, and reveal customer preferences, specifically focusing on preferred pizza sizes and toppings. The insights gained from this analysis serve as a valuable resource for restaurant management to enhance the menu and improve overall staffing efficiency. Tools used include SQL and Tableau. I have performed aggregations, joins and used CTE tables. To present the findings in a user-friendly manner, I have designed interactive dashboards using Tableau.
Revenue Distribution, Most-least profitable items, Order Distribution, Other Visualizations