Data Science Methodology in Action using ikigailabs

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Neena Sathi

4:46:57

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1. Introduction

1. Why This Course.mp4

03:28

2.1 S1-L2-Course-Overview.pdf

2. Course Overview.mp4

06:23

3. Learning Objectives.html

4.1 S1-L3-Course Outline.pdf

4. Course Outlne.mp4

05:24

5.1 L1-S4-DSM.pdf

5. Data Science Methodology.mp4

11:08

6. ASUM-DM Project Management Method components.html

7.1 Instructors Bio.pdf

7. Instructors Bio.mp4

02:32

2. Set-up Sandbox

1.1 S2-L1-Setup-Sandbox.pdf

1. Sand-box set-up overview.mp4

07:35

2. What is the requirement to share an Ikigai project with others.html

3.1 S2-L2-Product-Features.pdf

3. Product Features.mp4

06:53

4. Which of the features apply to IkigaiLabs.html

5.1 S2-L3-Product-Demonstration.pdf

5. Product Demonstration.mp4

09:38

6. What are various functionalities available with Ikigai flows.html

3. Step 1 - Define Project

1.1 S3-L1-Step-1-Define-Project-Concepts.pdf

1. Define Project - Concepts.mp4

12:22

2. Learn use case selection criteria.html

3.1 S3-L2-Define-Project-Example.pdf

3. Define Project - Example.mp4

07:52

4. Learn use case components.html

4. Step 2 - Describe Data

1.1 S4-L1-Describe-Data-Concepts.pdf

1. Describe Data - Concepts.mp4

06:59

2. Learn big data characteristics.html

3.1 S4-L2-Step-2-Describe-Data-Tasks-Deliverables.pdf

3. Describe Data - Templates.mp4

07:40

4.1 Health Equity DataSet ikigailabs.zip

4.2 S4-L3-Task 1. Load Data Sources.pdf

4. Step 2 - Task 1 - Load Data Sources.mp4

06:52

5. Limit on number of files in a folder.html

6.1 S4-L4-Task 2 Classify Datasets.pdf

6. Step 2 - Task 2 -Classify Data Sets.mp4

01:41

7.1 S4-L5-Task 3 Describe Datasets.pdf

7. Step 2 - Task 3 - Describe Data Sets.mp4

02:12

8.1 S4-L5-Task 3 Describe Datasets.pdf

8. Step 2 - Task 4 - Verify Data Quality.mp4

03:25

9. Data Quality.html

5. Step 3- Prepare Data

1.1 ✅S5-L1-Prepare-Data-Reduction.pdf

1. Prepare Data - Reduction.mp4

06:27

2. Data Reduction.html

3.1 ✅S5-L2-Prepare-Data-Feature-Engineering.pdf

3. Prepare Data - Feature Engineering.mp4

07:05

4. Health Equity Feature.html

5.1 ✅S5-L3-Prepare-Data-Synthesis.pdf

5. Prepare Data - Synthesis.mp4

04:14

6. Example of Data Synthesis.html

7.1 ✅S5-L4-Ikigai Data Preparation Capabilities.pdf

7. Data Preparation toolkits from Ikigailabs.mp4

03:29

8. data preparation facets available in Ikigai.html

9.1 ✅S5-L5-Task1 Select.pdf

9. Step 3 - Task 1 - Select.mp4

08:09

10. How to utilize Select facet.html

11.1 ✅S5-L6-Task2-Reformat.pdf

11. Step 3 - Task 2 - Reformat.mp4

05:27

12. Use of Convert function.html

13.1 ✅S5-L7-Task3- Filter.pdf

13. Step 3 - Task 3 - Filter.mp4

06:18

14. Use of Filter facet.html

15.1 ✅S5-L8-Task4 Merge.pdf

15. Step 3 - Task 4 - Merge.mp4

09:54

16. Use of Inner join facet.html

17.1 ✅S5-L9-Task5-Group.pdf

17. Step 3 - Task 5 - Group.mp4

04:37

18. Use of Summary facet.html

19.1 ✅S5-L10-Task6 Feature Engineering.pdf

19. Step 3 - Task 6 - Feature Engineering.mp4

05:44

20. Use of Multi Input Formula facet in Ikigai Labs.html

21.1 ✅S5-L11- Summary of Prepare Data Section.pdf

21.2 Final ABTl.csv

21. Prepare Data - Summary.mp4

05:56

22. Number of rows in ABT.html

6. Step 4 - Develop Model

1.1 S6-L2-DA-Simple-Statistics.pdf

1. Descriptive Analytics - Simple Statstics.mp4

08:02

2.1 S6-L3-DA-Bar-Chart.pdf

2. Descriptive Analytics - Bar Chart.mp4

05:56

3. Bar Chart.html

4.1 S6-L4-DA-Pie-Chart.pdf

4. Descriptive Analytics - Pie Chart.mp4

04:13

5. Pie chart.html

6.1 S6-L5-DA-Map-Charts.pdf

6. Descriptive Analytics - Map generation.mp4

06:00

7. Map chart.html

8. Predictive Analytics - Clustering Set-up.mp4

09:27

9. Clustering facet.html

10.1 S6-L7-Clustering-Results.pdf

10. Predictive Analytics - Clustering Example.mp4

10:02

11. Visualize clustering results on a Map.html

12.1 S6-L8-Predictive-Analytics-Regression-setup.pdf

12. Predictive Analytics - Regression Set-up.mp4

09:31

13. Predict facet.html

14.1 S8-L9-Regression-Examples.pdf

14. Predictive Analytics - Regression Example.mp4

02:53

15. Model Type field in the Predict facet.html

16.1 S6-L10-Prescriptive-Set-up.pdf

16. Prescriptive Analytics - Examples.mp4

09:21

17. Confounder for scenario analysis.html

18.1 S6-L11-Prescriptive-Algorithm.pdf

18.2 S6-L11-Prescriptive-Algorithm.pdf

18. Prescriptive Analytics - Algorithm.mp4

11:58

19. Concept of causation.html

7. Step 5 - Evaluate Model

1.1 Clustering results - evaluation.csv

1.2 S7-L1-Evaluate-Model-Clustering.pdf

1. Evaluate Model-Clustering.mp4

05:22

2. Clustering evalutaion.html

3.1 Regression Results - Evaluation.csv

3.2 S7-L2-Evaluate-Model-Prediction..pdf

3. Evaluate Model - Prediction.mp4

10:36

4. Evaluating a Prediction model.html

8. Step 6 - Deploy Model

1.1 S8-L1-Deploy Model.pdf

1. Step 6- Deploy Model.mp4

13:49

2. API Key.html

9. Step 7 - Optimize Model

1.1 S9-L1-Optimize-Model.pdf

1. Step 7 - Optimize Model.mp4

05:29

2. How to optimize a prediction model.html

10. Summary

1.1 PPT-S10-L1-SummaryNS.pdf

1. Summary and Next Steps.mp4

04:54

Description

Gain hands-on experience in building a Data Driven AI engagement using ikigailabs

What You'll Learn?

Students will learn proven data science methodology to deal with big data challenges as we move from BI world to AI world.
Students will use real case study and will gain hands-on experience in Designing / prototyping a Data science engagement on the chosen case study.
We divide the data scientists into clickers and coders. Clickers Examples include SPSS Modeler, Excel, and ikigailabs. This course is primarily for clickers
This course uses ikigailabs as a tool to show all necessary steps and activities needed for data science engagement.

Who is this for?

This course is for anyone interested in becoming a data scientist such as students, business analysts, developers, testing professionals.

There are several job categories where this course can be used as introductory material, such as data scientists, AI or automation engineer, test engineers, and knowledge engineers.

What You Need to Know?

You do not need prior knowledge of ikigailabs tool. We will cover basic operations and will provide ample examples on how to use the tool for a data science engagement.

We will introduce machine learning terms but assume the student has basic knowledge of machine learning and statistics.

More details

Description
With explosive growth of data in unstructured data, we have ample opportunities to design, develop and deploy AI models. While there are many courses which teach you Data Science, you need a step-by-step guide on how to select a problem, explore data, develop & deploy models, and improve the model using user feedback and learning. This course covers many big data challenges and modifies CRISP-DM to deal with big data. This course provides you a methodology for AI model development and deployment as modified by us to deal with AI and big data. Our modifications have been tried on many real-life large-scale projects. We will select a real case study for this data science project and will provide hands-on experience in Designing / prototyping a Data science engagement on the chosen case study. You will be able to use the results in your day-to-day life.
We divide the data scientists into clickers and coders. Clickers are those data scientists who use a data science tool with a user interface to provide a high-level specification. Examples include SPSS Modeler, Excel, Alteryx and Ikigailabs. In each case you can add formula using pre-packaged libraries without writing code. The second set of data scientists are those who use a procedural language with libraries to write code for data science work. The objective of this course is to get you an introductory clicker experience in data science using Ikigailabs as a tool.
Related courses:
Â· If you are interested in a coding course, we offer a course using Python.
Â· Our data science methodology course is also designed for Business Analysts and Project Managers with limited development background.
Â· If you are interested in advanced machine learning, we have made recommendation for courses.
Â· If you are interested in model deployment, monitoring and control, we offer a course on AI governance and control.
Course starts with two critical activities
Â· Set up Environment - step by step instructions in preparing sandbox environment for your Ikigailabs exercises
Â· Data Science Methodology - to review key steps, tasks and activities associated with our data science methodology
After above section, this course introduces our 7-step data science methodology and use Python to explain each step using our real-life use case example. These 7 steps include
Â· Step 1: Describe Use Case to explain selected use case for data science work
Â· Step 2: Describe Data to describe Data Sources and explain data sets using Ikigailabs
Â· Step 3: Prepare Datasets to Prepare Data Sets using Ikigailabs
Â· Step 4: Develop Model will provide hands-on exercises in applying many AI modeling techniques on data sets such as clustering, and regression using Ikigailabs.
Â· Step 5: Evaluate Model will provide measurements to Evaluate your AI Model Results
Â· Step 6: Deploy Model will provide process for deploying your AI models.
Â· Step 7: Monitor model will provide process for continuous monitoring and evaluating your models in production
In this course, we will give you an opportunity to design a use case and then work on its implementation using Ikigailabs. You should download all data sets and sample code. Complete all assignment in each section of the course and submit your final notebook using instructions provided.
Who this course is for:
This course is for anyone interested in becoming a data scientist such as students, business analysts, developers, testing professionals.
There are several job categories where this course can be used as introductory material, such as data scientists, AI or automation engineer, test engineers, and knowledge engineers.

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Ms. Neena Sathi is a Principal at Applied AI Institute, specializing in developing hands-on interactive solution and training contents (videos and class lectures) for various AI and Analytics related topics. She is also a Lecturer at University of California Irvine, where she teaches many courses on Generative AI, Conversation AI and Business Analytics. She had worked as Director/Data Scientist at KPMG Lighthouse labs with specialization in developing / integrating AI solutions associated with enhancing customer experience, back office automation and risk and compliance. Neena is an experienced professional with 30+ years of experience in architecting, designing, and implementing AI and Analytics application for Healthcare, Telco, Media, Retail, Public Services and Accounting Services organizations. She drives AI solutions in prototype to production-level system development for internal and external use cases. She has been affiliated with many universities (Carnegie Mellon University, University Of Phoenix, MIT and University of California, Irvine) for advanced research, teaching and training/curriculam/content development related with many AI and Analytics related topicsShe is Master certified integration architect from IBM and Open Group as well as certified Project management professional (PMP) from Project management institute. She is also certified in many Cloud and Cognitive technologies. She has widely presented and published many papers in AAAI, IEEE, WCF, ECF, IBM Information on Demand, IBM Insight, World of Watson, IBM Developer Works and many other journals.

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