Informatics Practices: Class 12 — Chapter 7: Project Based Learning

1. Identification of a project: Recognizing real-life situations or interdisciplinary problems that can be solved computationally.
2. Defining a plan: Identifying team members, assigning a project leader, defining specific roles, and selecting appropriate development tools.
3. Fixing a time frame and processing: Establishing a structured yet flexible timeline for completing various project milestones.
4. Providing guidance and monitoring: Utilizing resources such as books, websites, and mentor feedback to resolve technical bottlenecks.
5. Outcome of a project: Generating single or multiple deliverables, which are peer-reviewed and refined based on user feedback.

A successful project team relies on several non-technical interpersonal components alongside technical proficiency:

• (A) Communicate with Others: Maintaining clear, active communication channels via emails, phone calls, or group meetings to resolve issues and align goals.
• (B) Listen to Others: Understanding and respecting team members' ideas during group discussions to reach a consensus.
• (C) Share with Others: Distributing ideas, tools, and domain expertise to ensure the project is completed within the designated timeframe.
• (D) Respect for Others: Treating every team member respectfully and valuing their contributions to maintain high morale.
• (E) Help Others: Offering assistance within the team and seeking external expert guidance when necessary.
• (F) Participate: Encouraging active involvement from all members in both technical tasks and group discussions.

Description: An online shopping platform featuring two broad categories (Men, Women) and three sub-categories (Clothing, Footwear, Accessories). It supports promotional events like Festive Sales and End of Season Sales, and tracks monthly revenue, category-wise sales, and promotional discounts.

Specifications:

• Data storage: CSV/Data file containing fields: Apparel Code , Name , Category , Size , Price , Customer Name , Payment Mode , Discount Code .
• Payment validation: If payment is by Card, collect card number, name, CVV, and validity. If Cash on Delivery (COD), bypass details.
• Discount logic: FEST (10% discount) and EOS (15% discount).
• Implementation: Model data structures using Pandas and plot sales trends for decision-making.

Description: Automating the operations of a Bookworm Club donation camp that collects, assesses, refurbishes, and resells books and notebooks.

Specifications:

• Initial collection file: CSV with columns Item category , Item ID , Item name , Item type , Condition .
• ID Prefixing: Books prefixed with 'B' (Academic/Non-Academic); Notebooks prefixed with 'N' (Single/Four/Five line).
• Condition categories: Fit , Needs Mending , or Unfit .
• Refurbished inventory file: CSV with columns Item id , Item name , Item Category , Quantity , Price .
• Order processing: Update quantities in the refurbished CSV when orders are placed. Plot sales and collection metrics.

Description: A data-driven study analyzing the impact of social media usage on the mental health and behavior of teenagers.

Specifications:

1. Design a survey questionnaire using online tools (e.g., Google Forms) and export responses to a CSV file.
2. Formulate analytical questions to extract insights from the survey data.
3. Import the CSV file into a Pandas DataFrame .
4. Perform statistical computations (e.g., mean() , median() ).
5. Visualize findings using appropriate charts (bar, pie, line).

Description: Analyzing census data from the Open Government Data (OGD) Platform India ( data.gov.in ). The dataset used is "Special Tabulation on Adolescent and youth population classified by various parameters for India, States and Union Territories, 2011".

Selected Column Descriptions:

Step 0: Import required libraries

  
    import pandas as pd
    import matplotlib.pyplot as plt
  

Step 1: Read the CSV file into a DataFrame

  
    # Load the downloaded dataset
    data = pd.read_csv("PCA_AY_2011_Revised.csv")
    df = pd.DataFrame(data)
  

Step 2: Check the shape of the DataFrame

  
    print(df.shape)
    # Output: (12168, 123)
  

Step 3: Display the columns

  
    print(df.columns.values)
    # Displays the list of 123 columns including 'Area Name', 'Total/ Rural/ Urban', etc.
  

Step 4: Filter Data

We identify the columns needed for plotting: 'Area Name' , 'Total/Rural/Urban' , 'Adolescent and youth categories' , and 'Total Population - Persons' .

Step 5: Create a new DataFrame containing the filtered data

  
    # Filter rows where Area Name is 'INDIA'
    df1 = df.loc[(df['Area Name'] == 'INDIA'), 'Area Name':'Total Population - Females']
  

Step 6: Rename columns for ease of use

  
  df1.columns = ['Area', 'Class', 'Category', 'TotalPop', 'MalePop', 'FemalePop']

Step 7: Group and clean data as per requirement

The Category column contains six distinct groups: '10-14', '15-19', '20-24', 'Adolescent (10-19)', 'All Ages', and 'Youth (15-24)'. We group by Category, sum the values, and drop the aggregate categories to isolate the 5-year age brackets.

  
    # Group by Category and sum
    d = df1.groupby('Category').sum()
    # Drop unwanted aggregate rows
    d = d.drop(['Adolescent (10-19)', 'All Ages', 'Youth (15-24)'], axis=0)
    print(d)
  

Resulting Cleaned DataFrame:

Step 8: Plot the data as a bar chart

  
    # Generate the bar chart
    d.plot(kind='bar')
    plt.title("Population in Different Categories (Ages 10-24)")
    plt.ylabel("Population (in hundreds of millions)")
    plt.show()