Unlocking Business Value with AI Driven Data Analytics

Name: Unlocking Business Value with AI Driven Data Analytics
Rating: 4.0 (8 reviews)

Data Analytics Lifecycle Stages and Machine Learning basics

Created bySimpy sinha

Last updated 4/2025

English

What you'll learn

Concepts of Data Analytics Lifecycle Stages
Data Discovery
Data Preparation
Model Planning
Model Building
Communicating Results
Operationalize the Model

Course content

1 section • 5 lectures • 34m total length

Introduction to Unlocking Business Value with AI driven Data Analytics4:19
What You Will Learn:
1. Introduction to Data Analytics and Its Importance
2. Stages of the Data Analytics Lifecycle
Data Discovery and Data Preparation14:56
1. Data Collection, Cleaning, and Preparation Techniques - Basics and Tools
2. Real-world examples and scenarios
3. Best Practices and Industry Trends
Model Planning8:38

1. Exploratory Data Analysis and Visualization Basic Techniques and Tools
2. Model Selection Techniques
3. Real-world examples and scenarios
4. Best Practices and Industry Trends
Model Building3:22
1. Model Building, Evaluation, and Deployment
2. Real-world examples and scenarios
3. Best Practices and Industry Trends
Communicating the results and operationalizing3:00
1. Communicating the results - action points
2. Operationalizing the Model - Action Points
3. This course will prepare you for taking up data science projects and for advanced analytics

Requirements

No programming experience required
Basic idea of Data Analytics and Data Science is required, This course talks about core concepts for all 6 lifecycle stages including building a machine learning model and deployment.

Description

This beginner-friendly course is designed to introduce aspiring data scientists to the fundamentals of data analytics and machine learning. This small crash course will set the background if you want to learn the data science and machine learning concepts in detail. This course provides a comprehensive introduction to data analytics and machine learning, covering fundamental concepts, techniques, and real-world scenarios. Participants will learn how to collect, clean, analyze, and visualize data, as well as build and evaluate basic machine learning models.

By the end of the course, students will be able to:
1. Understand the fundamentals of all 6 stages in Data Analytics Lifecycle and the steps or action points in each stage.
2. Understand Data Discovery and action items in this phase.

3.Understand Data preparation steps and key aspects.

4. Understand Model Planning and Model building key aspects and steps to follow
5. Categorize between business problem types and the suitable model for the problem.

6. Churn Prediction Model Selection For different Industries
7. Discover real-world scenarios and Model selection based on problem type
8. Tools and Techniques used in each phase of data analytics

Who Should Attend?

1. Beginners with no prior experience in data analytics or machine learning.
2. Students and professionals looking to upskill in data-driven decision-making.
3. Business analysts, IT professionals, and enthusiasts interested in AI and ML applications.

Prerequisites:

1. Basic understanding of computers and mathematics (No programming background required).

Who this course is for:

Beginners - Data Analytics and Machine Learning