Improving data quality in data analytics & machine learning

سرفصل های دوره

Learn why, when, and how to maximize the quality of your data to optimize data-based decisions

1 - Introduction

1 - Is this course right for you

2 - Download course materials Python code

2 - Download the code

2 - dataQC-code.zip

3 - Why data quality matters

3 - Section summary

4 - Is data or are data

5 - On the origins and quality of data

6 - GIGO garbage in garbage out

7 - Data quality influences datadriven decisions

4 - Ensuring high data quality

8 - Section summary

9 - Data management

10 - Data documentation

11 - Data audits

12 - Data cleaning phases

13 - Improve quality before getting data

14 - Improve quality during data collection

15 - Improve quality after data collection

16 - Improve quality during data analysis

17 - Risks of biased results

5 - Assessing data quality

18 - Section summary

19 - Qualitative vs quantitative quality assessments

20 - Qualitative assessments via visual inspection

21 - Code Visualizing data distributions

22 - Variance assessments

23 - Correlations and correlation matrices

24 - Data error rates

25 - Sample sizes

26 - Code Measuring data quality

6 - Data transformations

27 - Section summary

28 - Zscore scaling

29 - Minmax scaling

30 - Binning rounding

31 - Unit normalization

32 - Rank transform

33 - Nonlinear transformations

34 - Code Transforming data

7 - Outliers and missing data

35 - Section summary

36 - What are outliers

37 - The zscore method

38 - The modified zscore method

39 - Dealing with missing data

40 - Code Dealing with bad or missing data

8 - Be a highquality data scientist

41 - Section summary

42 - Keeping up with data science developments

43 - Can you know everything

44 - What data scientists want

9 - Bonus

45 - Bonus material.html

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تولید کننده: Udemy-Training