Data science class 0011 to 013 - Advanced Excel
Data science class 013 - Advanced Excel
A travel company specializing in Premium Adventure Travel, want to be able to look at sales for various destinations and the profitability of bookings to be able to help the decision making process. given current report for this year, and provided last years full set of data. please make intuitive dashboards,data is organized in sheets one sheet per month,
Features:
- Track various sales figures: total sales, department-specific sales, profit margins, booking sources, and individual performances of our reservations staff.
- Create a simple summary with visual aids (graphs) to represent key metrics clearly.
- Use appropriate graphical representations: flexible on whether we use bar charts, pie charts, or line graphs; just want the best visual for each data set.
Understanding Your Data Needs
Based on your problem statement, the key areas you want to focus on are:
- Destination Performance: Sales and profitability by destination.
- Sales Channels: Booking sources and their impact on sales.
- Staff Performance: Individual sales and performance metrics.
- Profitability: Overall profit margins and departmental breakdowns.
Required Data Points
To effectively analyze this data, you'll need the following information for each booking:
Booking Details:
- Booking ID
- Booking Date
- Check-in Date
- Check-out Date
- Number of Travelers
- Total Booking Value
Customer Information:
- Customer ID
- Customer Name
- Contact Information
Destination Information:
- Destination ID
- Destination Name
- Country
Product/Service Information:
- Product/Service ID
- Product/Service Name
- Price
- Cost
- Margin
Sales Channel Information:
- Sales Channel ID
- Sales Channel Name
Staff Information:
- Staff ID
- Staff Name
- Department
Financial Information:
- Total Revenue
- Total Cost
- Profit Margin
Sample Data Structure
A sample data structure might look like this:
| Booking ID | Booking Date | Check-in Date | Check-out Date | Number of Travelers | Total Booking Value | Customer ID | Destination ID | Product/Service ID | Sales Channel ID | Staff ID |
|---|---|---|---|---|---|---|---|---|---|---|
| B12345 | 2023-11-15 | 2024-02-10 | 2024-02-20 | 2 | 5000 | C12345 | D001 | P001 | SC001 | S001 |
Data Analysis and Visualization
Once you have this data, you can perform various analyses:
- Destination Performance: Calculate total sales, average booking value, and profit margin for each destination. Use bar charts to compare sales and profit margins across destinations.
- Sales Channel Analysis: Determine the contribution of each sales channel to overall revenue. Use a pie chart to visualize the sales channel distribution.
- Staff Performance: Track individual sales, booking numbers, and customer satisfaction ratings. Use bar charts to compare sales performance among staff.
- Profitability Analysis: Calculate overall profit margin and break it down by department or product category. Use a line graph to visualize profit trends over time.
Class 011 : Descriptive Statistics
here are some descriptive statistics you can do on your data:
Total Bookings: This is the total number of bookings made in a particular period. In the sample data, there were 31 bookings in July 2024.
Bookings by Destination: This shows the number of bookings made for each destination. You can see that Rome had the most bookings (16), followed by Paris (8) and London (7).
Bookings by Product/Service: This shows the number of bookings made for each product or service offered. In this case, Packages were the most popular (12), followed by Flights (10) and Hotels (9).
Bookings by Sales Channel: This shows the number of bookings made through each sales channel. The data shows that Online bookings were the most frequent (17), followed by In-Store (7) and Phone (7).
Summary Statistics: This provides an overview of the revenue generated from bookings. You can calculate the total revenue and average revenue per booking. In the sample data, the total revenue was $14650.05 and the average revenue per booking was $472.58.
Class 012: Predictive Statistics for Your Travel Company
Predictive statistics can help you forecast future trends and make informed decisions.
Demand Forecasting
- Predicting booking volume: Use historical data to forecast the number of bookings for specific destinations, time periods, or product types.
- Identifying peak seasons: Analyze past booking patterns to predict when demand will be highest.
- Forecasting revenue: Estimate future revenue based on booking projections and pricing strategies.
Customer Behavior Analysis
- Customer churn prediction: Identify customers at risk of canceling future bookings.
- Customer lifetime value prediction: Estimate the potential revenue a customer will generate over their lifetime.
- Upselling and cross-selling opportunities: Identify customers likely to purchase additional products or services.
Pricing Optimization
- Price elasticity analysis: Determine how changes in price affect demand for different products or destinations.
- Dynamic pricing: Implement algorithms to adjust prices based on real-time demand and competition.
Inventory Management
- Optimal inventory levels: Predict the quantity of inventory needed for specific products or destinations.
- Overbooking and underbooking prevention: Optimize inventory levels to balance supply and demand.
Marketing and Sales
- Customer segmentation: Identify customer groups with similar characteristics for targeted marketing campaigns.
- Campaign effectiveness: Measure the impact of marketing campaigns on booking conversions.
- Sales forecasting: Predict sales performance for different sales channels and regions.
Statistical Techniques
To implement these predictive models, you can use various statistical techniques, including:
- Time series analysis: Analyze historical data to identify trends, seasonality, and cyclical patterns.
- Regression analysis: Model the relationship between variables to make predictions.
- Machine learning: Utilize algorithms to learn from data and make predictions.
- Clustering: Group similar customers or products based on their characteristics.
Remember: The effectiveness of predictive models depends on the quality and quantity of your data. It's essential to have a clean and comprehensive dataset to achieve accurate predictions.
here are all the descriptive statistics you can do on the above data with formulas:
Central tendency:
- Mean: This is the average of the values in a dataset. It is calculated by adding all the values and dividing by the number of values. You can use the following formula to calculate the mean:
mean = sum(x) / n- Median: This is the middle value in a dataset when the values are arranged in order from least to greatest. You can calculate the median using the following steps:
- Order the data from smallest to largest.
- If the number of data points (n) is odd, the median is the value in the middle position (n / 2 + 1).
- If n is even, the median is the average of the two middle values (n / 2 and n / 2 + 1).
Dispersion:
- Range: This is the difference between the largest and smallest values in a dataset. It is calculated by subtracting the minimum value from the maximum value.
range = max(x) - min(x)- Variance: This is a measure of how spread out the data is from the mean. It is calculated by finding the squared deviations from the mean for each data point, and then averaging those squared deviations. You can use the following formula to calculate the variance:
variance = sum((x - mean) ** 2) / n- Standard deviation: This is the square root of the variance. It is measured in the same units as the original data.
standard_deviation = sqrt(variance)Frequency:
- Frequency: This is the number of times a particular value appears in a dataset.
- Relative frequency: This is the frequency of a value divided by the total number of values in the dataset. It is often expressed as a percentage.
relative_frequency = frequency / n- Cumulative frequency: This is the total number of values that are less than or equal to a particular value in a dataset.
Percentiles:
- Percentiles: These are values that divide a sorted dataset into 100 equal parts. The nth percentile is the value such that n% of the data points are less than or equal to it. You can use the following function to calculate percentiles in Python:
percentile(x, p)where x is the data and p is the percentile (e.g., 25 for the 25th percentile).
These are just a few of the descriptive statistics that you can calculate on your data. By understanding these statistics, you can gain valuable insights into the characteristics of your data.
here are all the descriptive statistics you can do on the above data with formulas:
Central tendency:
- Mean: This is the average of the values in a dataset. It is calculated by adding all the values and dividing by the number of values. You can use the following formula to calculate the mean:
mean = sum(x) / n- Median: This is the middle value in a dataset when the values are arranged in order from least to greatest. You can calculate the median using the following steps:
- Order the data from smallest to largest.
- If the number of data points (n) is odd, the median is the value in the middle position (n / 2 + 1).
- If n is even, the median is the average of the two middle values (n / 2 and n / 2 + 1).
Dispersion:
- Range: This is the difference between the largest and smallest values in a dataset. It is calculated by subtracting the minimum value from the maximum value.
range = max(x) - min(x)- Variance: This is a measure of how spread out the data is from the mean. It is calculated by finding the squared deviations from the mean for each data point, and then averaging those squared deviations. You can use the following formula to calculate the variance:
variance = sum((x - mean) ** 2) / n- Standard deviation: This is the square root of the variance. It is measured in the same units as the original data.
standard_deviation = sqrt(variance)Frequency:
- Frequency: This is the number of times a particular value appears in a dataset.
- Relative frequency: This is the frequency of a value divided by the total number of values in the dataset. It is often expressed as a percentage.
relative_frequency = frequency / n- Cumulative frequency: This is the total number of values that are less than or equal to a particular value in a dataset.
Percentiles:
- Percentiles: These are values that divide a sorted dataset into 100 equal parts. The nth percentile is the value such that n% of the data points are less than or equal to it. You can use the following function to calculate percentiles in Python:
percentile(x, p)where x is the data and p is the percentile (e.g., 25 for the 25th percentile).
These are just a few of the descriptive statistics that you can calculate on your data. By understanding these statistics, you can gain valuable insights into the characteristics of your data.
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