Data analysis 2 presentation at ipmc.pptx

Data analysis 2 presentation at ipmc.pptx

• 1.
  Data Cleaning &Analysis Data Cleaning & Analysis Preparing data for accurate insights
• 2.
  What is DataCleaning? Process of fixing raw data Removes errors and inconsistencies Improves data quality Essential before analysis
• 3.
  Why Data CleaningMatters Dirty data leads to wrong decisions Inaccurate reports Loss of trust in data Business risks increase
• 4.
  Common Data Problems Missingvalues Duplicate records Incorrect formats Outliers
• 5.
  Missing Data ● Somefields may be empty ● Can affect analysis results ● Must be handled properly ● Depends on context
• 6.
  Handling Missing Values Removerecords Replace with average Replace with median Mark as “Unknown”
• 7.
  Duplicate Data Same recordappears multiple times Inflates counts Distorts results Must be removed
• 8.
  Removing Duplicates Identify uniqueIDs Compare multiple columns Keep most accurate record Use tools to automate
• 9.
  Data Standardization Consistent formats Samedate structure Same currency Same naming conventions
• 10.
  Data Validation Check valueranges Ensure correct data types Logical consistency checks Improves reliability
• 11.
  Exploratory Data Analysis(EDA) First step after cleaning Understand patterns Identify trends Detect anomalies
• 12.
  Tools for DataCleaning Microsoft Excel SQL queries Python (Pandas) Power BI
• 13.
  Analysis Techniques Summary statistics Trendanalysis Comparisons Visual analysis
• 14.
  Business Impact ● Betterdecisions ● Accurate reports ● Improved forecasting ● Increased efficiency
• 15.
  Conclusion ● Cleaning isa critical step ● Analysis depends on clean data ● Good data = good insights ● Core skill for analysts