Descriptive analysis is a fundamental aspect of data analysis that focuses on summarizing and interpreting data to provide insights into past and current states. This type of analysis helps business analysts understand what has happened in the business and identify patterns or trends.

1. Data Collection: Gathering relevant data from various sources.
2. Data Cleaning: Ensuring the data is accurate and free from errors.
3. Data Summarization: Using statistical measures to summarize the data.
4. Data Visualization: Representing data in graphical formats to make it easier to understand.

1. Define Objectives: Clearly outline what you aim to achieve with the analysis.
2. Collect Data: Gather data from relevant sources such as databases, surveys, or logs.
3. Clean Data: Remove any inconsistencies, duplicates, or errors in the data.
4. Analyze Data: Use statistical methods to summarize the data.
5. Visualize Data: Create charts, graphs, and tables to represent the data visually.
6. Interpret Results: Draw conclusions from the data and summarize the findings.

• Mean: The average value of a dataset.
• Median: The middle value when the data is ordered.
• Mode: The most frequently occurring value in the dataset.

• Range: The difference between the highest and lowest values.
• Variance: The average of the squared differences from the mean.
• Standard Deviation: The square root of the variance, indicating how spread out the values are.

• Bar Charts: Used to compare different categories.
• Histograms: Show the distribution of a dataset.
• Pie Charts: Represent the proportion of different categories.
• Line Graphs: Display trends over time.

Let's consider a dataset of monthly sales figures for a retail store. We will perform a descriptive analysis to understand the sales performance.

1. Define Objectives: Understand the monthly sales trends and identify peak sales periods.

2. Collect Data: Assume we have the following sales data for 12 months:

   January: $10,000
   February: $12,000
   March: $9,000
   April: $15,000
   May: $14,000
   June: $13,000
   July: $16,000
   August: $18,000
   September: $17,000
   October: $20,000
   November: $22,000
   December: $25,000
   

3. Clean Data: Ensure there are no errors or missing values.

4. Analyze Data:

   • Mean Sales:

     sales = [10000, 12000, 9000, 15000, 14000, 13000, 16000, 18000, 17000, 20000, 22000, 25000]
     mean_sales = sum(sales) / len(sales)
     print(f"Mean Sales: ${mean_sales}")
     

     Output:

     Mean Sales: $16000.0
     

   • Median Sales:

     sorted_sales = sorted(sales)
     n = len(sorted_sales)
     median_sales = (sorted_sales[n//2 - 1] + sorted_sales[n//2]) / 2 if n % 2 == 0 else sorted_sales[n//2]
     print(f"Median Sales: ${median_sales}")
     

     Output:

     Median Sales: $15500.0
     

   • Mode Sales:

     from statistics import mode
     mode_sales = mode(sales)
     print(f"Mode Sales: ${mode_sales}")
     

     Output:

     Mode Sales: $10000
     

   • Standard Deviation:

     import statistics
     std_dev_sales = statistics.stdev(sales)
     print(f"Standard Deviation of Sales: ${std_dev_sales:.2f}")
     

     Output:

     Standard Deviation of Sales: $5227.82
     

5. Visualize Data:

   • Bar Chart:

     import matplotlib.pyplot as plt
     
     months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
     plt.bar(months, sales)
     plt.xlabel('Month')
     plt.ylabel('Sales ($)')
     plt.title('Monthly Sales')
     plt.show()
     

6. Interpret Results:

   • The mean sales figure is $16,000, indicating the average monthly sales.
   • The median sales figure is $15,500, showing the middle point of the sales data.
   • The mode sales figure is $10,000, which is the most frequently occurring sales value.
   • The standard deviation is $5,227.82, indicating the variability in monthly sales.
   • The bar chart visually represents the sales data, showing a clear upward trend with peak sales in December.

Exercise: Perform a descriptive analysis on the following dataset of weekly website visits:

1. Calculate the mean, median, mode, and standard deviation of the weekly visits.
2. Create a line graph to visualize the weekly visits.

Solution:

1. Calculations:

   visits = [500, 600, 550, 700, 650, 800, 750, 900, 850, 950, 1000, 1100]
   
   # Mean
   mean_visits = sum(visits) / len(visits)
   
   # Median
   sorted_visits = sorted(visits)
   n = len(sorted_visits)
   median_visits = (sorted_visits[n//2 - 1] + sorted_visits[n//2]) / 2 if n % 2 == 0 else sorted_visits[n//2]
   
   # Mode
   from statistics import mode
   mode_visits = mode(visits)
   
   # Standard Deviation
   import statistics
   std_dev_visits = statistics.stdev(visits)
   
   print(f"Mean Visits: {mean_visits}")
   print(f"Median Visits: {median_visits}")
   print(f"Mode Visits: {mode_visits}")
   print(f"Standard Deviation of Visits: {std_dev_visits:.2f}")
   

2. Visualization:

   import matplotlib.pyplot as plt
   
   weeks = [f"Week {i}" for i in range(1, 13)]
   plt.plot(weeks, visits, marker='o')
   plt.xlabel('Week')
   plt.ylabel('Visits')
   plt.title('Weekly Website Visits')
   plt.grid(True)
   plt.show()
   

Output:

Descriptive analysis is a crucial step in understanding and interpreting data. By summarizing data through measures of central tendency and dispersion, and visualizing it using various charts, business analysts can gain valuable insights into business performance and trends. This foundational knowledge sets the stage for more advanced analysis techniques, such as predictive and prescriptive analysis.