Basic Concepts of Data Cleaning and Organizing for Analysis: Cleaning Marketing Dataset Examples

Home » News » Basic Concepts of Data Cleaning and Organizing for Analysis: Cleaning Marketing Dataset Examples

Data cleaning and preparation are crucial steps, so the concepts should be understood comprehensively. 

data cleaning and organizing

List the key concepts: handling missing data, removing duplicates, fixing structural errors, filtering irrelevant data, standardizing formats, handling outliers, creating new variables, data validation, and documentation.

Documentation and version control are often overlooked by data analyst beginners. The documentation will help them maintain reproducibility.

For a beginner data analyst, understanding the foundational steps is critical to ensure data is accurate, consistent,
and ready for analysis.

Data Cleaning & Organizing for Analysis

Below are the basic concepts of data cleaning and organizing:
 
  1. Handling
    Missing Data

  • Identify
    gaps
    : Detect missing values (e.g., blank cells, NaN).

  • Decide
    on treatment
    : Delete rows/columns, impute (fill) missing values
    (e.g., using averages), or flag missing data.


    In data analytics, NaN stands for “Not a Number” and is a special value used to represent missing, undefined, or unrepresentable numerical data. 

    Handling NaN Values:

    1. Detection:

    • Use
      functions like isna() or isnull() (in pandas) to
      identify NaN entries.

  • Removal:

    • Drop
      rows/columns with dropna() if the missing data is negligible or
      biased.

    Causes of NaN:

    • Data
      Entry Errors: Manual input mistakes or incomplete records.

    • Undefined
      Calculations: E.g., 0/0, sqrt(-1), or log(0).

    • Data
      Merging: Missing matches during joins/merges (e.g., unmatched keys in
      SQL or pandas).


  • Removing
    Duplicates

    • Detect
      and delete repeated rows to avoid skewed results.

  • Fixing
    Structural Errors

    • Correct
      typos, inconsistent formatting (e.g., “USA” vs.
      “U.S.A”), or mismatched data types (e.g., numbers stored as
      text).

  • Filtering
    Irrelevant Data

    • Remove
      columns or rows unrelated to the analysis (e.g., internal tracking IDs).

  • Standardizing
    Formats

    • Ensure
      consistency in units (e.g., “USD” vs. “$”), date
      formats (e.g., MM/DD/YYYY), and categorical labels (e.g.,
      “Male” vs. “M”).

  • Handling
    Outliers

    • Identify
      and address extreme values (e.g., a 1,000 purchase in a data set of 10-$50
      transactions).

  • Creating
    New Variables

    • Derive
      calculated fields (e.g., conversion rate = purchases / visitors).

  • Data
    Validation

    • Verify
      that cleaned data aligns with business rules (e.g., ages cannot be
      negative).

  • Documentation

    • Track
      changes made (e.g., in a log) to ensure reproducibility.

    Example: Cleaning a Marketing Dataset

    Scenario: A marketing team provides a dataset of a
    recent campaign with 10,000 customer records containing
    errors.

    Dataset Issues:

    • Missing
      values in the Age and Purchase Amount columns.

    • Duplicate
      entries for some customers.

    • Inconsistent Traffic
      Source labels (e.g., “Google Ads,” “Google”,
      “Social Media”).

    • Outliers
      in Time Spent on Website (e.g., 10,000 seconds).

    • Irrelevant
      columns like Internal Notes.

    Step-by-Step Cleaning Process:

    Remove Irrelevant Columns

    • Action:
      Delete Internal Notes.

    • Tool:
      Excel (delete column) or Python (df.drop(columns=[‘Internal Notes'])).

    Handle Missing Data

    • Problem:
      Missing Age and Purchase Amount.

    • Action:

      • Fill
        missing Age with the median age (avoids skewing from
        outliers).

      • Flag
        missing Purchase Amount as “No Purchase” (if
        analyzing conversions).

    • Code
      (Python)
      :
      df[‘Age'].fillna(df[‘Age'].median(), inplace=True) 
      df[‘Purchase Amount'].fillna(0, inplace=True)  # If analyzing revenue 

    Remove Duplicates

    • Action:
      Drop rows where all values are identical.

    • Code
      (Python)
      : df.drop_duplicates(inplace=True) 

    Standardize Categorical Data

    • Problem:
      Inconsistent Traffic Source labels.

    • Action:
      Merge similar categories (e.g., “Google Ads” → “Paid Search”,
      “Social Media” → “Social”).

    • Code
      (Python)
      : df[‘Traffic Source'] = df[‘Traffic Source'].replace( 

        {‘Google Ads': ‘Paid Search', ‘Google':
    ‘Paid Search', ‘Social Media': ‘Social'} 

    Handle Outliers

    • Problem: Time
      Spent on Website has extreme values (e.g., 10,000 seconds).

    • Action:
      Cap outliers at a reasonable threshold (e.g., 1 hour = 3600 seconds).

    • Code
      (Python)
      : df[‘Time Spent on Website'] = df[‘Time Spent on
      Website'].clip(upper=3600) 

    Create New Variables

    • Action:
      Calculate Conversion Rate (purchases / total visitors).

    • Code
      (Python)
      :

    df[‘Conversion'] = df[‘Purchase
    Amount'].apply(lambda x: 1 if x > 0 else 0)
    conversion_rate =
    df[‘Conversion'].mean() 

    Validate Data

    • Check
      for:

      • Negative
        values in Age or Purchase Amount.

      • Valid
        dates in Campaign Start Date.

    • Code
      (Python)
      : assert df[‘Age'].min() >= 0, “Negative age
      detected!” 

    Export Cleaned Data

    • Save
      as a new file (e.g., cleaned_marketing_data.csv).

    Resulting Organized Dataset:

    Customer ID

    Age

    Traffic Source

    Time Spent (seconds)

    Purchase Amount

    Conversion

    001

    32

    Paid Search

    1200

    50.00

    1

    002

    28

    Social

    800

    0.00

    0


    Impact: Cleaned data
    enables accurate analysis, such as identifying which traffic sources drive the
    highest conversions or whether age correlates with purchase behavior.

    Key Takeaway: Clean data
    is the foundation of reliable insights. Always start with these steps before
    diving into analysis!

    New Providers
    Quotex

    1000+ Trading Instruments
    The best trading broker in the world.

    Stockity

    1000+ Trading Instruments
    The best trading broker in the world.

    New Games
    Lies of P

    $59.99 Standard Edition
    28% Save Discounts
    See Top 10 Provider Games

    COCOON

    $24.99 Standard Edition
    28% Save Discounts
    See Top 10 Provider Games

    New Offers
    Commission up to $1850 for active user of affiliate program By Exness

    Top Points © Copyright 2023 | By Topoin.com Media LLC.
    Topoin.info is a site for reviewing the best and most trusted products, bonus, offers, business service providers and companies of all time.

    Discover more from Topoin

    Subscribe now to keep reading and get access to the full archive.

    Continue reading