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Data Analysis Portfolio
Data Analytics - Trainity
Visvesvaraya Technological University
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Data
Analysis
Portfolio
PREPARED BY – SHRISTI KUMARI
Professional Background My name is Shristi Kumari. I have recently completed my graduation in Geology from St. Xavier's College, Ranchi, where I obtained a percentage of 71%. I am now starting my career as a fresher data analyst. I am passionate about using data to solve real-world problems and improve business outcomes. During my studies, I developed strong analytical skills through various geology fieldwork, laboratory research, and data analysis projects. I also gained experience in using software such as Microsoft Excel, SQL, Tableau, Power BI for data analysis and visualization. In addition, I had the opportunity to work as a data analyst trainee at Trainty, where I gained hands-on experience in data analysis, database management, and creating data visualizations. This experience has helped me to develop a better understanding of the data analysis process and its applications in the industry and also made business strategy decisions. As I am a fresher it would be great to experience the real challenges of the corporate world and understand how things work. Being a fresher, I think I am very flexible and adaptive to learn new things. I have theoretical knowledge. But I am waiting to use my theoretical knowledge in a practical way. I am excited to continue developing my skills and knowledge as a data analyst or business analyst and look forward to contributing to meaningful projects in this field.
Project 1 : Description Vrinda Store Sales Analysis (With Excel ) The project aims to create an annual sales report for the year 2022 for the Vrinda Store. The report aims to provide insights into customer behavior and preferences to help the store make informed decisions and increase sales in 2023 dataset includes the following variables: ❑Order ID: Unique identification number for each order ❑Customer ID: Unique identification number for each customer ❑Gender: Gender of the customer (Male/Female/Other) ❑Age: Age of the customer ❑Status: Status of the customer (New/Returning) ❑Channel: Sales channel through which the order was placed (Online/Offline) ❑Category: Category of the product ordered (Apparel/Footwear/Accessories) ❑Size: Size of the product ordered ❑Quantity: Number of units of the product ordered ❑Amount: Total amount of the order ❑City: City where the order was placed ❑State: State where the order was placed The report will involve exploratory data analysis to identify patterns and trends in customer behavior, including demographic information, purchase patterns, and channel preferences. This analysis will help the store identify its most valuable customers and understand their preferences and purchasing habits. The report will also include insights into the most popular products and categories, as well as sales by channel and location. Finally, the report will provide recommendations for the store to improve sales in 2023, based on the insights gleaned from the data analysis.
Objective And Dataset Vrinda store wants to create an annual sales report of 2022 , So that Vrinda Store can understand customers and grow more sales in 2023. Questions to be answered:
- Compare the sales and orders using the single chart.
- Which month got highest sales and orders.
- Who purchased more men or women in 2022.
- What are different order status in 2022.
- List top 10 states contributing to the sales.
- Relation between age and gender based on number of orders.
- Which channel contributing to maximum sales.
- Highest selling category. Dataset given: 1drv/x/s!AoZAzyBqaQ8TghVMJ7t95MDbf6FQ?e=vSKBnK My Excel Workbook: 1drv/x/s!AoZAzyBqaQ8Tgh1MFEug73Bq2q_8?e=YNvliY I also prepared the dashboard with the help of the findings and observations for easily understanding the insights from the data.
Findings and their Insights Steps for the analysis : a) Selected all the data and created pivot table with the help of the columns. On the basis of the tables obtained created some charts for analysing the data. b) Created a combo chart of column and line chart for analysing the order and sales. c) Created a pie chart for analysing the sum of amount spent by men and women. d) Again created a pie chart for analysing count of status of product whether they are delivered, cancelled, refunded or returned. e) Then, created a bar chart for analysing the top 10 selling states. f) Then, created a column chart of age category and count of the order on the basis of gender in percentage. g) After that, created a pie chart for analysing the total amount of sale of products with different channels h) Further analysed the data and found important insights.
- Compare the sales and orders using the single chart.
- Which month got highest sales and orders.
- Jan Months Sum of Amount Count of Order ID
- Feb
- Mar
- Apr
- May
- Jun
- Jul
- Aug
- Sep
- Oct
- Nov
- Dec
Who purchased more men or women in 2022. Gender Sum of Amount Men 7613604 Women 13562773 ➢ Women spent more: The data clearly shows that women spent more than men. Women spent almost twice as much as men, with a total sales amount of 13,562,773 compared to men's total sales amount of 7,613,604. ➢ Gender-based marketing: Understanding the difference in spending habits between men and women can help the Vrinda Store tailor its marketing and promotional activities to each gender. For example, the store could create targeted marketing campaigns for men and women, highlighting products that are more popular among each gender. ➢ Inventory planning: The data can also be used to plan the store's inventory. Knowing which gender is spending more can help the store plan its inventory to meet the demand of its customers. ➢ Customer preferences: The data can provide insights into the preferences of the store's customers. The store can use this information to stock products that are popular among its customers, and to identify opportunities to expand its product range to meet the needs of its customers. Insights: The table shows the total sales amount for each gender, with men spending 7,613,604 and women spending 13,562,773. Here are some insights that can be derived from the data:
What are different order status in 2022. Status Count of Order ID Cancelled 844 Delivered 28641 Refunded 517 Returned 1045 Insights: The table shows the count of order IDs for different order statuses in 2022. Here are some insights that can be derived from the data: ➢ Delivered orders: Most of the orders (28,641) had a status of "delivered", which means that the products were successfully shipped to the customers. ➢ Cancelled orders: There were 844 cancelled orders, which means that the customers cancelled their orders before the products were shipped. ➢ Refunded orders: There were 517 refunded orders, which means that the customers received a refund for their orders due to some issue with the product or delivery. ➢ Returned orders: There were 1,045 returned orders, which means that the customers returned the products for some reason, such as being damaged or not meeting their expectations. ➢ Order processing: The table does not include any information about orders that are currently being processed or in transit. Understanding the status of these orders is important for the Vrinda Store to manage its inventory and ensure timely delivery.
Relation between age and gender based on number of orders. Age Category Men Women Adult 15% 34% Senior 5% 13% Teenager 9% 21% Insights: The table shows the percentage of orders placed by men and women in different age categories. Here are some insights that can be derived from the data: ➢ Gender differences: The data shows that women placed more orders than men across all age categories. Women placed 34% of the orders in the adult category, 21% of the orders in the teenager category, and 13% of the orders in the senior category, compared to 15%, 9%, and 5% of the orders placed by men in those respective categories. ➢ Age group preferences: The data also shows that women placed a higher percentage of orders in each age category compared to men. This suggests that women may be more likely to purchase products from the Vrinda Store across all age groups and may have different product preferences compared to men. ➢ Targeted marketing: Understanding the preferences and ordering habits of different age groups and genders can help the Vrinda Store create targeted marketing campaigns to attract new customers and retain existing ones.
Which channel contributing to maximum sales. Channels Count of Order ID Ajio 6% Amazon 35% Flipkart 21% Meesho 4% Myntra 23% Nalli 4% Others 4% Insights: The table shows the percentage of orders placed through different channels and their contribution to the total sales. Here are some insights that can be derived from the data: ➢ Maximum sales channel: The data shows that Amazon is the channel contributing the most to sales, with 35% of the orders placed through it. This suggests that Amazon is a popular platform for customers to purchase products from the Vrinda Store. ➢ Other significant channels: The second and third most significant channels are Myntra and Flipkart, with 23% and 21% of orders placed through them respectively. These channels are also important for the Vrinda Store and cannot be overlooked in marketing and sales strategies. ➢ Minor channels: The remaining channels (Ajio, Meesho, Nalli, and Others) make up a smaller percentage of sales individually. However, together they contribute to around 15% of the orders and cannot be ignored in the store's sales and marketing strategies up a smaller percentage of sales individually. However, together they contribute to around 15% of the orders and cannot be ignored in the store's sales and marketing strategies.
Project 2 : Description Employee Analytics Dashboard (With Power BI) The dashboard aims to create an employee report for the firm. The report aims to provide insights into employee attrition and their educational qualifications with respect to their gender and various factors. The dataset includes the following variables: The attrition column indicates whether an employee has left the organization or is still employed. The age band column categorizes employees into different age groups. The department column specifies the department in which the employee works. The education field column indicates the field in which the employee has received their education. The gender column specifies the gender of the employee. The job role column describes the employee's position within the organization. The age column specifies the age of the employee in years. Other information that may be included in the employee table but is not useful for preparing a dashboard in Power BI could include personal details such as address, phone number, or emergency contact information.
Objective And Dataset An organization wants to create an annual employee report to find out their employee statuses. Questions to be answered : 1 is the total number of employees in the organization? 2 is the attrition rate in the organization? 3 many employees are currently active in the organization? 4 is the average age of employees in the organization? 5 does the attrition rate differ across departments in the organization? 6 many employees are in each age group in the organization? 7 is the job satisfaction rating of employees in the organization? 8 does the attrition rate differ across education fields in the organization? 9 is the attrition rate for male and female employees in different age groups? 10 does the attrition rate vary across different job roles in the organization? Dataset given: 1drv/x/s!AoZAzyBqaQ8Tgh6vP7vbelAfg1Y9?e= hCFJzy
These are the charts used in the dashboard :
- The Pie chart represents the Department wise attrition. There are total three departments R&D, Sales and HR having attrition 133, 92, and 12, respectively. We can see that R&D department have highest attrition rate with 56% followed by sales department having 38% and HR department having 5%.
- The stacked column chart represents the number of employee by age-group with respect to gender. The highest number of employees are of age group 25- in which there are 337 males and 217 females.
- The matrix chart represents the Job satisfaction rating in different job roles. It can be significantly seen with the tree map included in it. The highest rating is from sales executive department have the total rating of 326 in which 112 employees rated the highest (4).
These are the charts used in the dashboard :
- Bar chart represents the Education wise attrition of
the employees
We observe that the employees having the education of life
sciences, mostly left their jobs wit the attrition number of
- Followed by medical and marketing having 63 and 35 attrition rate, respectively. The HRs are less likely to leave their job, they have the lowest attrition number of 7.
- The small donut charts represents the Attrition rate by gender for different age-group. And the KPIs in the middle of the donut chart represents the total number of employees with respect to the age group. It also represents count and percentage of the male and female for clarification.
- Bar chart represents the Education wise attrition of
the employees
We observe that the employees having the education of life
sciences, mostly left their jobs wit the attrition number of
Data Analysis Portfolio
Course: Data Analytics - Trainity
University: Visvesvaraya Technological University
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