CRISP-ML(Q)-Data Pre-processing Using Python(2023)

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360DigiTMG Elearning

19:47:49

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1. Introduction

1. Introduction to Project Management Methodology CRISP ML(Q).mp4

00:57

2. Agenda & Stages of Analytics.mp4

03:18

3. What is Diagnostic Analytics .mp4

01:15

4. What is Predictive Analytics .mp4

01:52

5. What is Prescriptive Analytics .mp4

11:35

6. What is CRISP ML (Q) .mp4

03:02

2. Business Understanding Phase

1. Business Understanding - Define the Scope of Application.mp4

18:39

2. Business Understanding - Define Success Criteria.mp4

08:06

3. Business Understanding - Use Cases.mp4

09:53

3. Data Understanding Phase Data Types

1. Agenda Data Understanding.mp4

00:43

2. Introduction to Data Understanding.mp4

06:12

3. Data types-continuous data (vs) Discrete data.mp4

11:11

4. Categorical Data Vs Count Data.mp4

06:39

5. Practical Data Understanding Using Realtime Example.mp4

11:09

6. Scale of Measurement.mp4

03:29

7. Quantitative (vs) Qualitative.mp4

04:57

8. Structured Vs Unstructured Data.mp4

12:58

9. Bigdata vs Not Big Data.mp4

09:39

10. Cross Sectional vs Time Series vs PanelLongitudinal Data.mp4

06:53

11. Balanced vs Imbalanced (or) Rare Events.mp4

15:29

12. Batch data(offline) vs Live streming data(Online).mp4

17:17

4. Data Understanding Phase Data Collection

1. What is Data collection .mp4

04:11

2. Understanding Secondary Datasources.mp4

13:26

3. Understanding Primary Datasources.mp4

22:17

4. Understanding Data collection using survey.mp4

06:43

5. Understanding Data collection using DoE.mp4

07:09

6. Understanding Possible Errors in Data Collection Stage.mp4

16:15

7. Understanding Bias & Fairness.mp4

05:11

5. Understanding Basic Statistics

1. Introduction to CRISP ML(Q) Data Preparation & Agenda.mp4

02:01

2. What is Probability .mp4

05:28

3. What is Random Variables .mp4

11:56

4. Understanding Probability and its Application, Probability Distribution.mp4

13:18

5. What is Inferencial Statistics .mp4

10:35

6. Data Preparation Phase Exploratory Data Analysis (EDA)

1. Recap of Priliminaries Concepts.mp4

02:09

2. Understanding Normal Distribution.mp4

15:36

3. Understanding Standard Normal Distribution & Whats is Z Scores.mp4

28:09

4. Understanding Measures of central tendency ( First moment business decession ).mp4

26:36

5. Understanding Measures of Dispersion ( Second moment business decision).mp4

10:46

6. Understanding Box Plot(Diff B-w Percentile and Quantile and Quartile).mp4

06:09

7. Understanding Graphical Techniques-Q-Q-Plot.mp4

08:34

8. Understanding about Bivariate Scatter Plot.mp4

35:31

7. Python Installation & Set-up

1. Python Installation.mp4

06:07

2. Anakonda Installation.mp4

06:55

3. Understand about Anakonda Navigator & Spyder & Python Libraries.mp4

24:23

4. Understand about Jupyter & Google Colab.mp4

08:35

8. Data Preparation Phase EDA Using Python

1. Recap of Concepts until Phase-2.mp4

16:05

2. Understanding 1st & 2nd Moment Business Decision Using Python.mp4

24:28

3. Understanding 3rd Moment Business Decision Using Python.mp4

20:58

4. Understanding 4th Moment Business Decision Using Python.mp4

20:34

5. Understanding Unvariate (Bar Plot & Histogram) Using Python.mp4

14:14

6. Understanding Unvariate Plots Using Python.mp4

34:30

7. Understanding Unvariate Box Plot Using Python.mp4

12:13

8. Understanding Unvariate Q-Q-Plot Using Python .mp4

08:00

9. Understanding Bivariate Scatter Plot Using Python.mp4

32:12

9. Data Preparation Phase Data Cleansing- Type Casting

1. Recap of Concepts.mp4

04:02

2. Understanding Data Cleansing Typecasting.mp4

10:26

3. Understanding Data Cleansing Typecasting Using Python.mp4

15:34

10. Data Preparation Phase Data Cleansing- Handling Duplicates

1. Recap of Concepts.mp4

05:36

2. Understanding Handling Duplicates.mp4

10:48

3. Understanding Handling Duplicates Using Python.mp4

25:26

11. Data Preparation Phase Data Cleansing-Outlier Analysis Treatment

1. Understanding Outlier Analysis Treatment.mp4

18:06

2. Understanding Outlier Analysis Treatment Using Python.mp4

27:31

3. Understanding Winsorization Using Python.mp4

29:36

12. Data Preparation Phase Data Cleansing-Zero & Variance Features

1. Understanding Zero & Variance Features using Python.mp4

20:47

13. Data Preparation Phase Data Cleansing-Discretization Techniques

1. Understanding Discretization Techniques - Binarization & Rounding & Binning.mp4

12:05

14. Data Preparation Phase Data Cleansing-Dummy Variable Creation

1. Understanding Encoding Technique - Binary Encoding.mp4

12:56

2. Understanding Encoding Technique - Ordinal Encoding & Attribute Construction.mp4

24:05

3. Understanding Binarization & Discretization Using Python.mp4

25:16

4. Understanding Dummpy Variables Using Python.mp4

23:10

5. Understanding One Hot & Label Encoding Using Python.mp4

19:24

6. Understanding about Attribute Construction.mp4

29:35

15. Data Preparation Phase Data Cleansing-Missing Values

1. Understanding Missing Values Variants - MCAR, MAR, MNAR.mp4

14:14

2. Understanding Missing Values Imputation Technique - Deletion & Single Imputat.mp4

22:10

3. Understanding Missing Values Imputation Types Using Python.mp4

12:52

16. Data Preparation Phase Data Cleansing-Transformation

1. Understanding Log & Exponential Transformation, Normal Q-Q Plot Using Python.mp4

11:38

2. Understanding Power, Sqrt, Reciprocal Transformations.mp4

07:16

3. Understanding Box-Cox Transformations Using Python.mp4

18:54

4. Understanding Yeo -Johnson Transformations Using Python.mp4

08:28

17. Data Preparation Phase Data Cleansing-Standarzation

1. Understanding Data Preprocessing - Data Scaling Method.mp4

12:01

2. Understanding Normalization & Standardization & Q-Q Plot & Robust Scaling.mp4

23:48

3. Normalization & Standardization & Q-Q Plot & Robust Scaling Using Python.mp4

30:02

4. Understanding Feature Extraction & Feature Selection.mp4

09:29

5. What is AutoEDA and Understanding Sweetviz Using Python.mp4

12:05

6. Understanding Auto Clean Library Using Python.mp4

14:34

7. Understanding Autoviz, D-Tale, Pandas Profilling & Data Prep Using Python.mp4

29:28

Description

Data Science - Data Pre-processing Using Python

What You'll Learn?

Understand Project Management Methodology to Handle Data Related Projects in Structured Manner.
Understand Business Problem Definition, Setting Objectives & Constraints.
Understand Data Types as well as Data Collection Mechanisms.
Understand Exploratory Data Analytics (EDA) / Descriptive Statistics as well as Graphical Representation
Understand the various Data Cleansing /Pre-Processing Tasks using Python.

Who is this for?

Beginners, Intermediate as well as Advanced learners

Freshers who are new of data science and want to embark into the field of data science

Working professionals who are working in different industries

Lecturers & Professors & Teachers whose primary role is to teach students on data related concepts

More details

Description
This program will help aspirants getting into the field of data science understand the concepts of project management methodology. This will be a structured approach in handling data science projects. Importance of understanding business problem alongside understanding the objectives, constraints and defining success criteria will be learnt. Success criteria will include Business, ML as well as Economic aspects. Learn about the first document which gets created on any project which is Project Charter. The various data types and the four measures of data will be explained alongside data collection mechanisms so that appropriate data is obtained for further analysis. Primary data collection techniques including surveys as well as experiments will be explained in detail. Exploratory Data Analysis or Descriptive Analytics will be explained with focus on all the â€˜4â€™ moments of business moments as well as graphical representations, which also includes univariate, bivariate and multivariate plots. Box plots, Histograms, Scatter plots and Q-Q plots will be explained. Prime focus will be in understanding the data preprocessing techniques using Python. This will ensure that appropriate data is given as input for model building. Data preprocessing techniques including outlier analysis, imputation techniques, scaling techniques, etc., will be discussed using practical oriented datasets.
Who this course is for:
Beginners, Intermediate as well as Advanced learners
Freshers who are new of data science and want to embark into the field of data science
Working professionals who are working in different industries
Lecturers & Professors & Teachers whose primary role is to teach students on data related concepts

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Established in 2013, 360DigiTMG is the training arm of Innodatatics Inc., USA, an IT services company that builds innovative solutions for core business problems. 360DigiTMG is a leading training institute that has been rated among the “20 Most Promising Data Analytics Solution Providers - 2018” by CIO Review. The institute is an accredited centre for Skim Bantuan Latihan (SBL) schemes by the Human Resources Development Fund (HRDF) under the Ministry of Human Resources, Malaysia.360DigiTMG offers a range of courses in big data analytics, machine learning, data science, artificial intelligence, deep learning, internet of things, robotic process automation, Amazon Web Services (cloud computing), data visualisation (business intelligence), Tableau, digital marketing, risk management, quality management, agile methodology, project management and many more.With headquarters in the United States and presence in India, Malaysia, East Asia, Australia, Middle East, the United Kingdom, and the Netherlands, 360DigiTMG adds a holistic, global market perspective to its curriculum

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