Preparing for a Data Scientist Role

In my preparation for this exciting opportunity as a Data Scientist , I am focusing on mastering the following skills. This article documents my approach to each skill, detailing technical implementations and practical applications.

1. Pandas for Data Processing

Key Tasks:

• Handling Missing Data:
  • Replace missing values with the mean/median:

    df['column_name'].fillna(df['column_name'].mean(), inplace=True)
    

  • Drop rows with missing data:

    df.dropna(subset=['column_name'], inplace=True)
    

  • Forward-fill missing data:

    df.fillna(method='ffill', inplace=True)
    

• Data Transformation:
  • Normalize or scale columns:

    from sklearn.preprocessing import MinMaxScaler
    scaler = MinMaxScaler()
    df['scaled_column'] = scaler.fit_transform(df[['column_name']])
    

• Removing Duplicates:

    df.drop_duplicates(inplace=True)
  

• Group and Aggregate:

    grouped = df.groupby('category_column')['numeric_column'].sum()
  

2. Manage Data Transfer Between Different Platforms Using Data Transfer Packages (e.g., FTP)

Key Tools:

• Using Python’s ftplib for FTP Transfers:
  • Upload a File:

    from ftplib import FTP
        
    ftp = FTP('ftp.server.com')
    ftp.login(user='username', passwd='password')
    with open('file_to_upload.csv', 'rb') as f:
        ftp.storbinary('STOR file_to_upload.csv', f)
    ftp.quit()
    

  • Download a File:

    ftp = FTP('ftp.server.com')
    ftp.login(user='username', passwd='password')
    with open('downloaded_file.csv', 'wb') as f:
        ftp.retrbinary('RETR remote_file.csv', f.write)
    ftp.quit()
    

• Data Transfer with SFTP (Using pysftp):

    import pysftp
      
    with pysftp.Connection('hostname', username='user', password='pass') as sftp:
        sftp.put('local_file.csv', 'remote_file.csv')
        sftp.get('remote_file.csv', 'local_file.csv')
  

3. Data Integrity

Ensuring Data Integrity:

• Verify Data Completeness:

    assert df.isnull().sum().sum() == 0, "Data contains missing values!"
  

• Check Duplicates:

    assert df.duplicated().sum() == 0, "Data contains duplicates!"
  

• Validate Data Types:

    assert df['date_column'].dtype == 'datetime64[ns]', "Date column is not in the correct format!"
  

• Use Hashing for Validation:

    import hashlib
      
    def hash_file(file_path):
        with open(file_path, 'rb') as f:
            return hashlib.sha256(f.read()).hexdigest()
      
    assert hash_file('file.csv') == 'expected_hash_value', "File integrity compromised!"
  

4. Relational Data Warehouses

Working with SQL:

• Creating a Task:

    CREATE TASK daily_update
    SCHEDULE 'USING CRON 0 0 * * * UTC'
    AS
    SELECT * FROM source_table INTO target_table;
  

• Creating Views:

    CREATE VIEW sales_summary AS
    SELECT category, SUM(revenue) AS total_revenue
    FROM sales_data
    GROUP BY category;
  

• Writing Complex Queries:

    SELECT customer_id, AVG(purchase_amount) AS avg_purchase
    FROM purchases
    WHERE purchase_date >= '2023-01-01'
    GROUP BY customer_id
    HAVING avg_purchase > 100;
  

5. Machine Learning (Regression, Classification, Clustering)

Regression (Linear Regression):

• Implementation:

    from sklearn.linear_model import LinearRegression
    model = LinearRegression()
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)
  

• Metrics:

    from sklearn.metrics import mean_squared_error
    print(mean_squared_error(y_test, predictions))
  

Classification (Logistic Regression):

• Implementation:

    from sklearn.linear_model import LogisticRegression
    clf = LogisticRegression()
    clf.fit(X_train, y_train)
    predictions = clf.predict(X_test)
  

• Metrics:

    from sklearn.metrics import classification_report
    print(classification_report(y_test, predictions))
  

Clustering (K-Means):

• Implementation:

    from sklearn.cluster import KMeans
    kmeans = KMeans(n_clusters=3)
    kmeans.fit(data)
    print(kmeans.labels_)
  

• Visualization:

    import matplotlib.pyplot as plt
    plt.scatter(data[:, 0], data[:, 1], c=kmeans.labels_)
    plt.show()
  

Conclusion

By deeply understanding and practicing these topics, I am equipping myself with the technical knowledge and practical skills required for this role. This preparation ensures I can confidently handle the challenges and responsibilities outlined in the job description.