Improving data quality in data analytics & machine learning

Focused View

Mike X Cohen

5:22:02

158 View

1 - Introduction

1 - Is this course right for you.mp4

06:44

2 - Download course materials Python code

2 - Download the code.mp4

02:57

2 - dataQC-code.zip

3 - Why data quality matters

3 - Section summary.mp4

01:09

4 - Is data or are data.mp4

02:33

5 - On the origins and quality of data.mp4

06:32

6 - GIGO garbage in garbage out.mp4

03:29

7 - Data quality influences datadriven decisions.mp4

03:55

4 - Ensuring high data quality

8 - Section summary.mp4

01:55

9 - Data management.mp4

06:22

10 - Data documentation.mp4

05:09

11 - Data audits.mp4

07:56

12 - Data cleaning phases.mp4

02:59

13 - Improve quality before getting data.mp4

08:56

14 - Improve quality during data collection.mp4

05:10

15 - Improve quality after data collection.mp4

05:20

16 - Improve quality during data analysis.mp4

03:14

17 - Risks of biased results.mp4

07:32

5 - Assessing data quality

18 - Section summary.mp4

00:33

19 - Qualitative vs quantitative quality assessments.mp4

10:15

20 - Qualitative assessments via visual inspection.mp4

13:08

21 - Code Visualizing data distributions.mp4

16:38

22 - Variance assessments.mp4

06:42

23 - Correlations and correlation matrices.mp4

16:27

24 - Data error rates.mp4

04:42

25 - Sample sizes.mp4

08:58

26 - Code Measuring data quality.mp4

12:20

6 - Data transformations

27 - Section summary.mp4

10:49

28 - Zscore scaling.mp4

09:08

29 - Minmax scaling.mp4

05:02

30 - Binning rounding.mp4

12:21

31 - Unit normalization.mp4

10:32

32 - Rank transform.mp4

06:29

33 - Nonlinear transformations.mp4

10:39

34 - Code Transforming data.mp4

21:44

7 - Outliers and missing data

35 - Section summary.mp4

01:18

36 - What are outliers.mp4

13:51

37 - The zscore method.mp4

09:22

38 - The modified zscore method.mp4

03:40

39 - Dealing with missing data.mp4

06:26

40 - Code Dealing with bad or missing data.mp4

13:59

8 - Be a highquality data scientist

41 - Section summary.mp4

01:09

42 - Keeping up with data science developments.mp4

07:51

43 - Can you know everything.mp4

04:23

44 - What data scientists want.mp4

01:44

9 - Bonus

45 - Bonus material.html

Description

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

What You'll Learn?

Strategies for increasing data quality
Ways to assess data quality
Interpreting data visualizations
How to spot problems in data

Who is this for?

Data science practitioners

Data scientist students

Managers or colleagues who work with data practitioners

What You Need to Know?

Interest in working with data

Interest in knowing more about data quality

Some Python skills are useful for the optional coding videos

More details

Description
All of our decisions are based on data. Our sense organs gather data, our memories are data, and our gut-instincts are data. If you want to make good decisions, you need to have high-quality data.

This course is about data quality: What it means, why it's important, and how you can increase the quality of your data.

In this course, you will learn:
High-level strategies for ensuring high data quality, including terminology, data documentation and management, and the different research phases in which you can check and increase data quality.
Qualitative and quantitative methods for evaluating data quality, including visual inspection, error rates, and outliers. Python code is provided to see how to implement these visualizations and scoring methods using pandas, numpy, seaborn, and matplotlib.
Specific data methods and algorithms for cleaning data and rejecting bad or unusual data. As above, Python code is provided to see how to implement these procedures using pandas, numpy, seaborn, and matplotlib.

This course is for
Data practitioners who want to understand both the high-level strategies and the low-level procedures for evaluating and improving data quality.
Managers, clients, and collaborators who want to understand the importance of data quality, even if they are not working directly with data.
Who this course is for:
Data science practitioners
Data scientist students
Managers or colleagues who work with data practitioners

User Reviews

Rating

average 0

Total votes0

Focused display

I am a neuroscientist (brain scientist) and associate professor at the Radboud University in the Netherlands. I have an active research lab that has been funded by the US, German, and Dutch governments, European Union, hospitals, and private organizations.But you're here because of my teaching, so let me tell you about that: I have 20 years of experience teaching programming, data analysis, signal processing, statistics, linear algebra, and experiment design. I've taught undergraduate students, PhD candidates, postdoctoral researchers, and full professors. I teach in "traditional" university courses, special week-long intensive courses, and Nobel prize-winning research labs. I have >80 hours of online lectures on neuroscience data analysis that you can find on my website and youtube channel. And I've written several technical books about these topics with a few more on the way.I'm not trying to show off -- I'm trying to convince you that you've come to the right place to maximize your learning from an instructor who has spent two decades refining and perfecting his teaching style.Over 120,000 students have watched over 7,500,000 minutes of my courses. Come find out why!I have several free courses that you can enroll in. Try them out! You got nothing to lose ;) -------------------------By popular request, here are suggested course progressions for various educational goals:MATLAB programming: MATLAB onramp; Master MATLAB; Image ProcessingPython programming: Master Python programming by solving scientific projects; Master Math by Coding in PythonApplied linear algebra: Complete Linear Algebra; Dimension ReductionSignal processing: Understand the Fourier Transform; Generate and visualize data; Signal Processing; Neural signal processing

Udemy

View courses Udemy

Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.