SQL> SELECT * FROM (
2 SELECT sequence#, archived, applied,
3 TO_CHAR(completion_time, 'RRRR/MM/DD HH24:MI') AS completed
4 FROM sys.v$archived_log
5 ORDER BY sequence# DESC)
6 WHERE ROWNUM <= 10
7 /
SEQUENCE# ARCHIVED APPLIED COMPLETED
---------- -------- ------- ----------------
11211 YES NO 2004/09/16 09:30
11210 YES YES 2004/09/16 09:00
11209 YES YES 2004/09/16 08:30
11208 YES YES 2004/09/16 08:00
11207 YES YES 2004/09/16 07:30
11206 YES YES 2004/09/16 07:00
11205 YES YES 2004/09/16 06:30
11204 YES YES 2004/09/16 06:30
11203 YES YES 2004/09/16 06:30
11202 YES YES 2004/09/16 06:00
10 rows selected.
Aspect Data Wrangling (Data Preprocessing) Exploratory Data Analysis (EDA) Objective Prepare raw data for modeling by cleaning, transforming, and formatting it appropriately. Explore and understand the data to gain insights, identify patterns, and make decisions on data handling and modeling. Order Typically performed as a preliminary step before EDA. Usually conducted after data wrangling to further investigate data characteristics. Data Handling Focuses on data cleaning, filling missing values, encoding categorical variables, and scaling features. Involves data visualization, statistical analysis, and summary statistics to uncover patterns, relationships, and anomalies. Techniques Techniques include imputation, outlier detection, feature scaling, and one-hot encoding. Techniques include histograms, scatter plots, box plots, correlation matrices, and descriptive statistics. Data Transformation Involves structural changes to the dataset, such as feature engineering, data normaliz...
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