Best Data Analytics Course in Pune with placement
- 2. INTRODUCTION DEFINITION: EDA IS THE PROCESS OF ANALYZING DATA SETS TO SUMMARIZE THEIR MAIN CHARACTERISTICS USING VISUAL METHODS. PURPOSE: TO UNDERSTAND THE DATA, DETECT ANOMALIES, TEST ASSUMPTIONS, AND IDENTIFY PATTERNS. Objectives of EDA Discovering Patterns: Identifying trends, clusters, and relationships. Spotting Anomalies: Detecting outliers and unusual data points. Testing Hypotheses: Validating assumptions and ideas. Checking Data Quality: Ensuring accuracy, completeness, and consistency
- 3. KEY TECHNIQUES IN EDA DESCRIPTIVE STATISTICS MEAN, MEDIAN, MODE STANDARD DEVIATION, VARIANCE SKEWNESS, KURTOSIS DATA VISUALIZATION HISTOGRAMS BOX PLOTS SCATTER PLOTS BAR CHARTS DATA CLEANING HANDLING MISSING VALUES REMOVING DUPLICATES CORRECTING ERRORS
- 4. DESCRIPTIVE STATISTICS PURPOSE: SUMMARIZES THE CENTRAL TENDENCY, DISPERSION, AND SHAPE OF THE DATA’S DISTRIBUTION. EXAMPLE METRICS: MEAN: AVERAGE VALUE MEDIAN: MIDDLE VALUE MODE: MOST FREQUENT VALUE STANDARD DEVIATION: MEASURE OF SPREAD SLIDE 6: DATA VISUALIZATION TECHNIQUES HISTOGRAMS: SHOW THE DISTRIBUTION OF A SINGLE VARIABLE. BOX PLOTS: DISPLAY THE SUMMARY OF A SET OF DATA VALUES. SCATTER PLOTS: EXAMINE THE RELATIONSHIP BETWEEN TWO CONTINUOUS VARIABLES. BAR CHARTS: COMPARE DIFFERENT CATEGORIES.
- 5. CONCLUSION EDA IMPORTANCE: THIS IS A CRUCIAL FIRST STEP IN THE DATA ANALYSIS PROCESS TO GAIN INSIGHTS AND PREPARE FOR MORE SOPHISTICATED ANALYSIS. FUTURE OF EDA: INTEGRATION WITH MACHINE LEARNING FOR AUTOMATED INSIGHTS AND MORE INTERACTIVE VISUALIZATION TOOLS.