Data Analytics And Artificial Intelligence For Beginners

Learn the basic concepts of data analytics, AI, business intelligence, big data, machine learning, and deep learning.

What you'll learn

A brief overview of the history of analyzing data, from medieval statistics to the sophisticated techniques developed by the likes of Google and Microsoft.

A look at data stores, which are growing exponentially, and the challenges of wrangling “big data.”

Understanding of data mining—what it entails, different approaches, and who’s leading the way.

A two-part discussion of business intelligence, including the principles of sound dashboard design and data presentation.

The key differences between the four types of analytics—diagnostic, descriptive, predictive, and prescriptive

An overview of specific analytics processes and models.

A first look at AI, its evolution, its functions, and what it can do for businesses today.

An exploration of machine learning—how systems can learn from data, identify patterns, and make decisions with little human intervention.

A survey of deep learning technologies, including a variety of neural networks.

An overview of the most important machine learning data modeling techniques

A practical and honest appraisal of the analytics and AI landscape today and moving forward, including the tremendous promise and the potential pitfalls.

Resources for continued study on these topics.

Requirements

No prior knowledge required. This course is suitable for beginners

Description

**This course includes downloadable exercise files to work with**The richest data store is only as good as your ability to search, sort, analyze, and present the data within it. This introductory-level course will give students a broad overview of the theory and practice of data analytics and the many ways in which artificial intelligence (AI) contributes to it.Your instructor will begin with a brief history of data analytics and then proceed into discussions of data warehouses, data mining, business intelligence, machine learning, and other emerging AI techniques to make sense of big data.Students will learn how data is captured, cleansed, analyzed, and presented on business intelligence dashboards that captivate and persuade an audience. “It is a capital mistake to theorize before one has data," Sherlock Holmes once said.Whether you are investigating analytics as a potential career move or wish to better understand the terminology you encounter with increasing frequency in your professional circles, this course will give you the foundation you are looking for.This program includes 3 hours of instruction and a practice-based assessment, which will help students simulate real-world data analytics scenarios that are critical for success in today's increasingly complex workplace.Students will gain:A brief overview of the history of analyzing data, from medieval statistics to the sophisticated techniques developed by the likes of Google and Microsoft.A look at data stores, which are growing exponentially, and the challenges of wrangling “big data.”Understanding of data mining—what it entails, different approaches, and who’s leading the way.A two-part discussion of business intelligence, including the principles of sound dashboard design and data presentation.The key differences between the four types of analytics—diagnostic, descriptive, predictive, and prescriptive—and how they relate to and build upon each other, and how they apply to various industries.An overview of specific analytics processes and models.A first look at AI, its evolution, its functions, and what it can do for businesses today.An exploration of machine learning—how systems can learn from data, identify patterns, and make decisions with little human intervention.A survey of deep learning technologies, including a variety of neural networks.An overview of the most important machine learning data modeling techniquesA practical and honest appraisal of the analytics and AI landscape today and moving forward, including the tremendous promise and the potential pitfalls.Resources for continued study on these topics.This course includes:3 hours of video tutorials20 individual video lecturesCourse and Exercise files to follow alongCertificate of completion

Overview

Section 1: Analytics Beginnings

Lecture 1 Introduction

Lecture 2 WATCH ME: Essential Information for a Successful Training Experience

Lecture 3 DOWNLOAD ME: Course Instructor Files

Lecture 4 DOWNLOAD ME: Course Exercise Files

Lecture 5 History of Analytics

Lecture 6 Introduction to Data and Big Data

Lecture 7 Data Mining - Part 1

Lecture 8 Data Mining - Part 2

Lecture 9 Business Intelligence - Part 1

Lecture 10 Business Intelligence - Part 2

Lecture 11 4 Types of Analytics

Lecture 12 Types of Analytics Methods

Section 2: Artificial Intelligence

Lecture 13 Artificial Intelligence - Part 1

Lecture 14 Artificial Intelligence - Part 2

Lecture 15 Machine Learning - Part 1

Lecture 16 Machine Learning - Part 2

Lecture 17 Deep Learning

Lecture 18 Other AI Analytics Techniques - Part 1

Lecture 19 Other AI Analytics Techniques - Part 2

Lecture 20 Other AI Analytics Techniques - Part 3

Lecture 21 Conclusion

Section 3: Exercise & Course Close

Lecture 22 Exercise 1

Lecture 23 Course Close

People who want to start their careers in data analytics,Those who want to learn the basic concepts of data analytics and AI,Individuals who want to kickstart their data science skills