Data cleaning and preparation are crucial steps, so the concepts should be understood comprehensively.
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
- 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:
- 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. - 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).
Causes of NaN:
Duplicates
- Detect
and delete repeated rows to avoid skewed results.
Structural Errors
- Correct
typos, inconsistent formatting (e.g., “USA” vs.
“U.S.A”), or mismatched data types (e.g., numbers stored as
text).
Irrelevant Data
- Remove
columns or rows unrelated to the analysis (e.g., internal tracking IDs).
Formats
- Ensure
consistency in units (e.g., “USD” vs. “$”), date
formats (e.g., MM/DD/YYYY), and categorical labels (e.g.,
“Male” vs. “M”).
Outliers
- Identify
and address extreme values (e.g., a 1,000 purchase in a data set of 10-$50
transactions).
New Variables
- Derive
calculated fields (e.g., conversion rate = purchases / visitors).
Validation
- Verify
that cleaned data aligns with business rules (e.g., ages cannot be
negative).
- 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 |
enables accurate analysis, such as identifying which traffic sources drive the
highest conversions or whether age correlates with purchase behavior.
is the foundation of reliable insights. Always start with these steps before
diving into analysis!