Project Details

🎯Objective: The objective of this IPL data analysis project is to extract meaningful insights and trends from the dataset to enhance our understanding of the game and provide valuable information to cricket enthusiasts, team management, and sports analysts. We aim to answer questions such as which teams have been the most successful over the years, what factors contribute to winning a match, and how player performance impacts team outcomes.

🛠️ Approach:
1. Data Collection: Gather and preprocess the IPL dataset, ensuring data quality and consistency.
2. Exploratory Data Analysis (EDA): Perform EDA to understand the dataset's structure, identify missing values, and gain initial insights.
3. Descriptive Statistics: Calculate basic statistics, such as averages, win percentages, and run rates, to provide an overview of IPL trends.
4. Team Performance Analysis: Analyze team-wise performance by assessing win-loss records, season-wise performance, and home vs. away match statistics.
5. Player Performance Analysis: Evaluate player performance by examining batting, bowling, and fielding statistics, as well as identifying top performers.
6. Match Factors: Investigate the impact of factors like toss decisions, venues, and match types (e.g., day/night) on match outcomes.
7. Visualization: Create informative visualizations, including graphs, charts, and heatmaps, to present key findings effectively.
8. Predictive Modeling (Optional): Develop predictive models to forecast match outcomes or player performance based on historical data.
9. Conclusion: Summarize the insights gained from the analysis and their implications for IPL teams and cricket fans.

Interactive Dashboard

Project Details

In this project, the goal is to create one report that could be used by stakeholders from the sales, marketing, finance, and executive teams. The focus is made on the following

1. Robust Data Modeling
2. User-empathetic Report design
3. Drillable Insights

Learnings

1. Power Query (Basic and Advanced Operations)
2. Basic and complex DAX formulas
3. Data modelling involving 10+ tables
4. Choosing the right visuals and formatting
5. Dashboard designing principles
6. Using bookmarks
7. Deploying in Power BI service
8. Stakeholder Feedback Implementation
9. Sales, Marketing, Finance & Supply Chain metric

Live Interactive Dashboard

Project Details

Objective:

The objective of the Sales Analysis Dashboard is to provide stakeholders with a real-time view of sales performance, enabling them to:
1. Monitor and track sales revenue over time.
2. Understand customer segments and their contribution to sales.
3. Identify regions and markets with high and low sales performance.
4. Analyze the impact of discounts on sales and profitability.
5. Visualize shipping costs and their effect on overall profit margins.
6. Prioritize orders based on order priority.

Approach:

The approach to creating the Sales Analysis Dashboard involves the following steps:
1. Data Collection: Gather and consolidate data from the provided dataset, ensuring data accuracy and completeness.
2. Data Processing: Clean and preprocess the data, handling missing values and outliers.
3. Metric Calculation: Calculate key performance metrics such as total sales, profit, and shipping costs.
4. Customer Segmentation: Group customers into segments (e.g., Corporate, Consumer, Home Office) to analyze their behavior.
5. Visualization: Create interactive charts and graphs to visualize sales trends, regional performance, and more.
6. Dashboard Development: Build a user-friendly dashboard using a data visualization tool (e.g., Tableau, Power BI).
7. Real-time Data Integration: Implement real-time data integration to keep the dashboard up to date.
8. User Access: Provide access to relevant stakeholders for monitoring and analysis.

Live Interactive Dashboard

Project Details

🎯Objective:

The objective is to help RSVP Movies plan their new global audience-focused movie project by analyzing the data of movies released in the past three years. This analysis aims to provide meaningful insights that can guide their decision-making process.

🛠️ Approach:

1. Import and understand the dataset:

Start by importing the dataset containing information about movies released in the past three years. Understand the structure of the data, including the tables and their relationships.

2. SQL Queries for Analysis:

Break down the analysis into four segments, each focusing on different aspects of the data. Use SQL queries to extract relevant information and derive insights based on the questions provided in each segment.

3. Data Exploration:

Perform data exploration to gain a better understanding of trends, patterns, and relationships within the dataset. Use SQL functions and aggregation to summarize data.

4. Interpretation and Recommendations:

Interpret the results of each analysis segment and provide recommendations to RSVP Movies based on the insights obtained. These recommendations should help them make informed decisions for their new global audience-oriented movie project.

📚Learnings

Through this analysis, we can learn about the past performance of movies produced by RSVP Movies and identify key factors that contributed to their success or failure. We can also understand audience preferences and market trends, which can guide the production company in making strategic decisions for their upcoming global project.

Segment 1:

Movie Success Analysis
Objective: Analyze the success of movies based on box office performance.

Segment 2:

Genre and Audience Analysis
Objective: Understand the popularity of movie genres and their appeal to different audiences.

Segment 3:

Release Timing Analysis
Objective: Determine the impact of the release timing on a movie's success.

Segment 4:

Actor and Director Analysis
Objective: Identify the influence of actors and directors on a movie's performance.

Executive summary

: Executive summary report that helped the RSVP movies team to take decision of their next movie project.

SQL QUERIES RSVP MOVIES CASE STUDY

Project Details

Objective:

The objective is to create a comprehensive dashboard for the Sales team of ABC, an ecommerce company. This dashboard will monitor the performance of various vendors and items, provide a ranking/categorization of the products, and identify key products for the team to focus on.
1. Monitor and track sales revenue over time.
2. Understand differnt sales managers and their contribution to sales.
3. Analyze the impact of on sales
4. Analyse the vendor sales and items they sold

Approach:

The approach to creating the Sales Analysis Dashboard involves the following steps:
1. Data Collection: Gather and consolidate data from the provided dataset, ensuring data accuracy and completeness.
2. Data Processing: Clean and preprocess the data, handling missing values and outliers.
3. Metric Calculation: Calculate key performance metrics such as total sales, total quantities ,
4. Visualization: Create interactive charts and graphs to visualize sales trends, manager performance, and more.
5. Dashboard Development: Build a user-friendly dashboard using a data visualization tool (Power BI).
6. Real-time Data Integration: Implement real-time data integration to keep the dashboard up to date.
7. User Access: Provide access to relevant stakeholders for monitoring and analysis.

Live Interactive Dashboard

Project Details

🎯Objective:

The main objectives of this EDA are as follows:
1. Analyze the loan dataset to identify factors influencing loan default.
2. Perform univariate and bivariate analysis to gain insights into the data.
3. Develop a clear understanding of the risk analytics domain.
4. Create visualizations to present key findings.
5. Provide actionable insights for the consumer finance company to mitigate credit losses.

🛠️ Approach:

1. Data Collection:

Obtain the loan dataset from 2007 to 2011 and the corresponding data dictionary.

2. Data Preprocessing:

Clean and preprocess the data, handle missing values, and format columns.

3. Univariate Analysis:

Explore individual variables (both consumer and loan attributes) to understand their distributions and characteristics.

4. Bivariate Analysis:

Examine relationships between variables, particularly how consumer attributes relate to loan default.

5. Visualization:

Create visualizations (e.g., histograms, bar charts, scatter plots) to illustrate key insights.

6. Interpretation:

Interpret findings in the context of risk analytics and loan approval decisions.

7. Presentation:

Summarize the analysis results in a presentation format for stakeholders.

📚Learnings

Through this analysis, learners will gain the following knowledge and skills:
1. Understanding risk analytics in the banking and financial services industry.
2. Data preprocessing techniques, including handling missing data.
3. Univariate and bivariate analysis to explore data patterns.
4. Data visualization for effective communication of insights.
5. Interpreting business implications from data analysis.

📊Insights:

1. Identification of key factors influencing loan default, such as credit score, income, and loan amount.
2. Understanding the distribution of loan types and their impact on default rates.
3. Insights into the company's risk assessment and portfolio management.
4. Recommendations for potential strategies to reduce credit losses, such as adjusting lending criteria or interest rates.
5. Improved decision-making processes in loan approval and risk assessment for the consumer finance company.

Presentation OF Exploratory Data Analysis (EDA) for Risk Analytics in Consumer Finance

Project Details

🎯Objective:

The objective of this HR analysis project in Tableau is to visually explore and analyze employee attrition data, identifying patterns, trends, and potential drivers of attrition within the organization. We aim to provide actionable insights through interactive visualizations.

🛠️ Approach:

1. Data Preparation:

Load the HR dataset into Tableau and perform data cleaning, such as handling missing values and formatting the data for analysis.

2. Interactive Dashboards:

Create interactive dashboards that allow users to explore attrition-related data visually.Use filters and parameters to enable users to customize their views and focus on specific aspects of attrition.

3. Attrition Overview Dashboard:

Develop a dashboard that provides an overview of attrition rates, including the total number of attrition cases and the attrition rate by various categories (e.g., department, job role, gender).

4. Learning and Insights:

Summarize key insights and findings from the visualizations and analyses. Highlight actionable recommendations based on the visualized data.

DASHBOARD