Ribash Karki

Data Analyst | Data Specialist | Data Visualization

Excel | SQL | Tableau | Python

Projects

Python

Finance Data Analysis

Analysis of US big bank sector stock before, during, and after the Great Recession.

Python

9-11 Call Analysis

Exploratory data analysis of 9-11 calls using seaborn, pandas,numpy and matplotlib.

Tableau

Customer Analysis Dashboard

Dynamic analysis of customer by region, state, monthly revenue, gender wise.

Tableau

HR Analysis Dashboard

Analysis of employees attrition based on age, gender.

SQL

HR Report Analysis/ Testing

Testing reports and KPI
of tableau using SQL queries.

SQL

Music Database Analysis

Queries to answer data-driven decision-making in the dynamic music industry.

Excel

Road Acident Dashboard

Interactice road accident dashboard for year 2021 and 2022.

Excel

Sales Analysis Dashboard

Dynamic analysis of sales by region, state, product, custoter and category.

Python

Salary Analysis

In-depth analysis of salary data using Python pandas library.

Python

**E-Commerce Data Analysis **

Analysis of E-commerce data using python pandas library,

CERTIFICATIONS

Degree

SQL Essential Training

Excel for Business Analysts

Customer Service: Problem Solving and Troubleshooting

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A Tableau customer analysis project is a data-driven initiative that uses the Tableau platform to analyze and visualize customer data. This project can provide insights into customer behavior, preferences, and patterns, which can help businesses improve their marketing and customer service strategies.The project typically involves several stages, including data collection, data cleaning, data analysis, and visualization. Here is a brief description of each stage:1. Data collection: This stage involves gathering data from various sources such as customer databases, sales records, and social media platforms. The data collected may include demographic information, purchase history, customer feedback, and other relevant data.2. Data cleaning: This stage involves removing any duplicate or irrelevant data and ensuring that the data is accurate and consistent. This may involve using tools such as Excel or Python to clean and prepare the data for analysis.3. Data analysis: This stage involves using Tableau to analyze the data and identify patterns and trends. This may include creating calculations, building models, and performing statistical analysis.4. Visualization: This stage involves creating interactive and visually appealing dashboards and reports using Tableau. The visualizations can help businesses understand customer behavior and preferences and identify opportunities for growth.

Some of the key metrics and analyses that are included in this Tableau customer analysis project are:
- Revenue Per State
- Monthly Revenue
- Age Wise Analysis
- Region Wise Sale
- Gender Wise Sale

Overall, a Tableau customer analysis project can provide valuable insights into customer behavior and preferences, helping businesses improve their marketing and customer service strategies and drive growth.

The dashboard is completely dynamic and adjusts depending on the filter that you choose.

Here is a short demo of the project.

You can click the link below to view the project.

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The key features of this Excel sales analysis dashboard include:- Sales Overview: This section provides a snapshot of the overall sales performance, including revenue, sales growth, and other key metrics.- Sales by Product/Service: This section provides an analysis of sales by product or service, highlighting top-selling items and identifying areas of opportunity.- Sales by Region and State: This section provides an analysis of sales by geographic region, helping businesses identify areas with the highest sales potential.- Sales by Customer: This section provides an analysis of sales by customer, allowing businesses to identify their most valuable customers and focus their sales efforts accordingly.- Top Sales by Customer Representative: This section provides an analysis of sales by Sales Representative helping businesses to identify their top sales person and rewarding their hardworks.

I utilized the following skills to create this dashboard:
- Slicer
- Pivot Charts
- Pivot tables
- Sorting
- Filtering

The dashboard is completely dynamic and adjusts depending on the multiple filter.

Here is a short demo of the project.

You can click on the picture above or click the link below to view the project.

Project Link

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HR analysis project

An HR attrition dashboard in Tableau is a visual representation of key metrics related to employee turnover and attrition within an organization. It provides HR professionals and stakeholders with insights into the reasons behind employee departures, identifies patterns, and helps in developing strategies to mitigate attrition. The dashboard includes visualizations, filters, and interactive elements to facilitate data exploration and analysis.

Here are some key insights :
- The age range of 25-35 exhibit higher attrition rates compared to other age groups.
- Male employees have almost twice attrition rate compared to female employees.
- The R&D department have the highest attrition rate almost 1/3 of people are leaving in that department compared to other.

I utilized the following skills to create this dashboard:
- Parameters
- Calculated fields
- KPI
- Custom Charts

The dashboard is completely dynamic and adjusts depending on the filter that you choose.

Here is a short demo of the project.

You can click the link below to view the project in Tableau Public.

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Testing reports and KPI of tableau using SQL queries.

In this project I tested the accuracy of HR analysis. I tested most of the KPI using the SQL queries and tableau side by side.

The SQL skills demonstrated in the projects are:
1. Create
2. Order by
3. Group by
4. Sum
5. Count
6. Round
7. Average
8. Cast

Creating the table

Employee Count

Tableau Report

SQL Query

Employee Count by 'High School' education

Tableau Report

SQL Query

Attrition count by 'Doctoral Degree' education

Tableau Report

SQL Query

Attrition rate in percentage

Tableau Report

SQL Query

Active Employee Count

Tableau Report

SQL Query

Average attrition age

Tableau Report

SQL Query

Attrition by Gender

Tableau Report

SQL Query

Attrition by Department with filter 'High School' education

Tableau Report

SQL Query

Attrition Department Percentage with filter 'Doctoral Degreel' education

Tableau Report

SQL Query

Education field wise attrition

Tableau Report

SQL Query

You can click the link below to view the project.

Project Link

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This is a Road Accident Excel Dashboard Project for year 2021 and 2022. Clients want this dashboard so they can have insights on below requirements:- Primary KPI: Total Casualties taken place in the accident.
- Primary KPI: Casualties and total percent of casulties based on the accident severity.
- Secondary KPI: Total Casulties respect to vehichle type.
- Monthly trend based on current year and previous year.
- Maximum Casulties based on road type.
- Maximum casulties respect to road surface.
- Casulties based on area/location.
- Casulties based on daytime/nighttime.

Stakeholders:
- Public
- Police Department
- Ministry of Transport
- Road transport Department
- Road Safety Department
- Emergency Service Department
- Traffic Management
- Transport Operators
- Media

The dashboard is completely dynamic and adjusts depending on the years, area selected via the slicer.

Here is a short demo of the project.

The process and skills involved to create this dashboard are.
- Data Cleaning
- Data Processing
- Data Analysing
- Data Visualization
- Dashboard Building

You can click on the picture above or click the link below to view the project in your device.

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SQL QUERIES TO ANSWER MUSIC RELATED DATABASE

The project's insights help music industry professionals make smart decisions based on data. By analyzing information about user preferences, trends, and patterns, they can choose which artists to collaborate with, create personalized recommendations, target specific audiences, and curate track and playlist.

The SQL skills demonstrated in the projects are:
1. Common Table Expression (CTE)
2. Join multiple tables
3. Sub Queries
4. Order by
5. Group by
6.Limit

Scheme:

SQL QUESTIONS AND THEIR QUERIES

Q1. WHO IS THE SENIOR EMPLOYEE BASED ON JOB TITLE/LEVEL

Q2. Which countries have the most Invoices?

Q3. WHAT ARE TOP 3 VALUES OF TOTAL INVOICE

Q4. Write a query that returns one city that has the highest sum of invoice totals. Return both the city name & sum of all invoice totals.

Q5. Who is the best customer? The customer who has spent the most money will be declared the best customer. Write a query that returns the person who has spent the most money.

Q6. Write query to return the email, first name, last name, & Genre of all Rock Music listeners. Return your list ordered alphabetically by email starting with A.

Q7. Let's invite the artists who have written the most rock music in our dataset.Write a query that returns the Artist name and total track count of the top 10 rock bands.

Q8. Return all the track names that have a song length longer than the average song length. Return the Name and Milliseconds for each track. Order by the song length with the longest songs listed first.

Q9. Find how much amount spent by each customer on artists? Write a query to return customer name,artist name and total spent.

You can click the link below to view the project.

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In this project, I utilized Python's powerful data manipulation and analysis library, Pandas, to perform an in-depth analysis of salary data. The goal was to extract meaningful insights and trends from the dataset, enabling informed decision-making and providing valuable information for stakeholders.

Key steps and highlights of the project included :1. Data Acquisition: Obtained the salary dataset from reliable sources or internal databases, ensuring data integrity and quality.

2. Data Cleaning and Preprocessing: Employed Pandas functionalities to clean and preprocess the data. This involved handling missing values, removing duplicates, standardizing formats, and resolving inconsistencies in the dataset.

3. Exploratory Data Analysis (EDA): Conducted thorough exploratory analysis using Pandas to gain a comprehensive understanding of the dataset.

4.Grouping and Aggregation: Utilized Pandas' grouping and aggregation functionalities to perform analyses at different levels, such as analyzing salaries by department, position, or experience level. Aggregated metrics such as average salary, maximum salary, and minimum salary were computed to identify patterns and disparities.

By leveraging the power of Python's Pandas library, this project successfully enabled the analysis and interpretation of salary data. The outcomes provided valuable insights that supported decision-making processes and facilitated a deeper understanding of salary structures within the organization or industry.

You can click the link below to view the project.

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By leveraging Pandas for e-commerce purchase analysis, this project provided valuable insights into customer buying behavior, product preferences, and overall purchase patterns. The derived insights empowered businesses to optimize marketing strategies, enhance customer experiences, and drive revenue growth in the e-commerce domain.

Key steps and highlights of the project included:1.Data Acquisition
2.Data Cleaning and Preprocessing
3.Exploratory Data Analysis (EDA)
4.Product Analysis
5. Reporting Findings

You can click the link below to view the project.

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This project analysis how did the finance sector stocks performed before, during and after 'The Great Recession' from 2006 to 2016.In this project I analyzed 6 big banks Stock in the US before, during and after 'The Great Recession'. The banks are as below:
- JP morgan Chase (JPM)
- Bank Of America (BAC)
- Citi Group (C)
- Morgan Stanley (MS)
- Goldman Sachs (GS)
- Wells Fargo (WFC)

Steps Involved:1.Getting the Data from the web
2.Cleaning the Data
3.Using Pandas to manipulate data
4.Exploratory data analysis
5.Using matplotlib and Seaborn to visualize the data
6.Using Plotly and Cufflinks to create interactive and visually appealing plots

CODE AND PLOTS:

Max close price for each bank's stock

Each bank stock best and worst single day returns DATE

Distplot using seaborn of the 2015 returns for Morgan Stanley

Lineplot showing close price for each bank for the entire index of timeline.

30 day moving average

Heatmap of the correlation between the stocks Close Price

Candle plot of Bank of America's stock from Jan 1st 2015 to Jan 1st 2016

Simple Moving Averages plot of Morgan Stanley for the year 2015

You can click the link below to view the project.

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In this project I used a dataset from Kaggle.com to analyze the 911 calls. The steps involved in this projects are:
1. Getting the data
2. Loading the CSV file
3. Cleaning data
4. Manipulating data
5. Creating new columns
6. Analysis of data
7. Plotting different charts and lines

CODE AND PLOTS:

Top 5 zipcode and township for calls