Data Science Methodology in Action using Dataiku

Data Science Methodology in Action using Dataiku, available at $64.99, has an average rating of 3.95, with 38 lectures, 14 quizzes, based on 80 reviews, and has 369 subscribers.

You will learn about 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 Dataiku. This course is primarily for clickers. This course uses Dataiku to show all necessary steps and activities needed for data science engagement. This course is ideal for individuals who are This course is for anyone interested in becoming a data scientist such as students, business analysts, developers, testing professionals. or 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. It is particularly useful for This course is for anyone interested in becoming a data scientist such as students, business analysts, developers, testing professionals. or 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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Summary

Title: Data Science Methodology in Action using Dataiku

Price: $64.99

Average Rating: 3.95

Number of Lectures: 38

Number of Quizzes: 14

Number of Published Lectures: 38

Number of Published Quizzes: 14

Number of Curriculum Items: 52

Number of Published Curriculum Objects: 52

Number of Practice Tests: 1

Number of Published Practice Tests: 1

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will 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 Dataiku. This course is primarily for clickers.
This course uses Dataiku to show all necessary steps and activities needed for data science engagement.

Who Should Attend

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.

Target Audiences

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.

Embark on a journey into the world of Data Science with our “Data Science in Action using Dataiku” course, designed to harness the power of unstructured data and AI modeling. This course is perfect for those who want a practical, hands-on experience in the field, following a modified CRISP-DM methodology with Dataiku as the primary tool.

Course Overview:

Categorization of Data Scientists: Learn the distinction between ‘clickers’ and ‘coders’ in data science, focusing on the ‘clicker’ approach using tools like Dataiku.
Capstone Project: Apply your learning in a comprehensive capstone project, offering a real-world experience in designing and prototyping a Data Science engagement.
Comprehensive Methodology: The course begins with setting up your Dataiku environment and reviewing our unique data science methodology.
Seven-Step Data Science Methodology: Dive deep into each step of the process, from describing your use case to continuous model monitoring and evaluation, all within Dataiku. These steps include:
- Use Case Description: Understand and articulate your selected data science use case.
- Data Description: Explore data sources and datasets using Dataiku.
- Dataset Preparation: Get hands-on experience in preparing datasets within Dataiku.
- Model Development: Apply AI modeling techniques like clustering and regression in Dataiku.
- Model Evaluation: Learn how to measure and evaluate your AI model results.
- Model Deployment: Understand the process of deploying your AI models.
- Model Monitoring: Master continuous monitoring and evaluation of your models in production.

This course is tailored for those seeking an introductory ‘clicker’ experience in data science. Whether you’re a business analyst, project manager, or someone interested in coding or advanced machine learning, this course offers a foundational understanding of data science methodologies and practical applications using Dataiku. Download datasets, follow step-by-step instructions, complete assignments, and submit your final notebook to fully engage in this immersive learning experience. Join us to transform your data science skills and apply them in everyday scenarios.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Why-This-Course

Lecture 2: Course Introduction

Lecture 3: Class Project

Lecture 4: Course Outline

Lecture 5: Instructors – Neena Sathi

Lecture 6: Instructors – Arvind Sathi

Chapter 2: Dataiku Sandbox

Lecture 1: Dataiku Sandbox

Chapter 3: Data Science Methodology

Lecture 1: Methodology Evolution

Lecture 2: Methodology Overview

Chapter 4: Step 1 – Define Project

Lecture 1: Define Project Steps

Lecture 2: Define Project Example

Chapter 5: Step 2 – Describe Data

Lecture 1: Describe Data – Concepts

Lecture 2: Describe Data – Steps

Lecture 3: Describe Data – Example – Load Data Sources

Lecture 4: Describe-Data – Example – Classify Datasets

Lecture 5: Describe-Data-Example-Describe-Datasets

Lecture 6: Describe-Data-Example-Verify-Data-Quality

Chapter 6: Step 3: Prepare Data

Lecture 1: Prepare Data – Reduction

Lecture 2: Prepare Data – Feature Engineering

Lecture 3: Prepare Data – Synthesis

Lecture 4: Task 1 – Select

Lecture 5: Task 2 – Filter

Lecture 6: Task 3 – Transform

Lecture 7: Task 4 – Group

Lecture 8: Task 5 – Feature Engineering

Lecture 9: Task 6 – Merge

Chapter 7: Step 4 – Develop Model

Lecture 1: Modeling Overview

Lecture 2: Task 1 – Classification Setup

Lecture 3: Task 2 – Clustering

Lecture 4: Task 3 – Prediction Set-up

Lecture 5: Task 4 – Prediction

Chapter 8: Step 5 – Evaluate Model

Lecture 1: Step 5 – Prediction Evaluation Overview

Lecture 2: Step 5 – Task 1 – Prediction

Chapter 9: Step 6 – Deploy Model

Lecture 1: Step 6 – Deploy Model Overview

Lecture 2: Step 6 – Deploy Model – Task 1 Scoring Engine

Chapter 10: Step 7 – Optimize Model

Lecture 1: Step 7- Optimize Model Overview

Chapter 11: Summary

Lecture 1: Key Takeaways

Lecture 2: Bonus Lecture

Chapter 12: FInal Exam

Instructors

Neena Sathi
Principal, Applied AI Institute

Rating Distribution

1 stars: 2 votes
2 stars: 3 votes
3 stars: 9 votes
4 stars: 20 votes
5 stars: 46 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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