Data science, machine learning, and analytics without coding

Data science, machine learning, and analytics without coding, available at $54.99, has an average rating of 4.15, with 45 lectures, based on 49 reviews, and has 343 subscribers.

You will learn about The fundamentals of data science problem solving Machine learning algorithms such as Random Forest, K-Means, and OLS Regression How to use the KNIME platform to import, process, explore, and clean data This course is ideal for individuals who are Beginners in data science who do not know how to code or People who want to learn data science problem solving but do not think they will be able to learn code or Business people who want to solve problems that are too large or difficult for Excel It is particularly useful for Beginners in data science who do not know how to code or People who want to learn data science problem solving but do not think they will be able to learn code or Business people who want to solve problems that are too large or difficult for Excel.

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Summary

Title: Data science, machine learning, and analytics without coding

Price: $54.99

Average Rating: 4.15

Number of Lectures: 45

Number of Published Lectures: 44

Number of Curriculum Items: 45

Number of Published Curriculum Objects: 44

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

The fundamentals of data science problem solving

Machine learning algorithms such as Random Forest, K-Means, and OLS Regression

How to use the KNIME platform to import, process, explore, and clean data

Who Should Attend

Beginners in data science who do not know how to code

People who want to learn data science problem solving but do not think they will be able to learn code

Business people who want to solve problems that are too large or difficult for Excel

Target Audiences

Beginners in data science who do not know how to code

People who want to learn data science problem solving but do not think they will be able to learn code

Business people who want to solve problems that are too large or difficult for Excel

Do you want to super charge your career by learning the most in demand skills? Are you interested in data science but intimidated from learning by the need to learn a programming language?

I can teach you how to solve real data science business problems that clients have paid hundreds of thousands of dollars to solve. I’m not going to turn you into a data scientist; no 2 hour, or even 40 hour online course is able to do that. But this course can teach you skills that you can use to add value and solve business problems from day 1.

This course is different than most for several reasons:

1. We start with problem solving instead of coding.I feel like starting to code before solving problems is misguided; many students are turned off by hours of work to try to write a couple of meaningless lines rather than solving real problems. The key value add data scientists make is solving problems, not writing something in a language a computer understands.

2. The examples are based on real client work. This is not like other classes that use Kaggle data sets for who survived the Titanic, or guessing what type of flower it is based on petal measurements. Those are interesting, but not useful for people wanting to sell more products, or optimize the performance of their teams. These examples are based on real client problems that companies spent big money to hire consultants (me) to solve.

3. Visual workflows. KNIME uses a visual workflow similar to what you’ll see in Alteryx or Azure Machine Learning Studio and I genuinely think it is the future of data science. It is a better way of visualizing the problem as your are exploring data, cleaning data, and ultimately modeling. It is also something that makes your process far easier to explain to non-data scientists making it easier to work with other parts of your business.

Summary:This course covers the full gamut of the machine learning workflow, from data and business understanding, through exploration, cleaning, modeling, and ultimately evaluation of the model. We then discuss the practical aspects of what you can change, and how you can change it, to drive impact in the business.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Intro: why this course is the best option for those wanting to make an impact

Lecture 2: About your instructor

Lecture 3: What is KNIME, the platform we will use in this course?

Lecture 4: How to get KNIME (dont worry, it is free!) – do this before we start

Chapter 2: Data Exploration

Lecture 1: Intro to data exploration

Lecture 2: KNIME tour and importing / accessing data

Lecture 3: Data types in KNIME and data analysis everywhere

Lecture 4: Group By: The most powerful node in KNIME

Lecture 5: Pivoting: Using pivots and the math formula for more value out of Group By

Lecture 6: Statistics: Using summary statistics from the nodes

Lecture 7: Visualization: Graphing & plotting in KNIME

Chapter 3: Data Cleaning

Lecture 1: Intro to data cleaning

Lecture 2: String to Date: Changing strings to dates

Lecture 3: String Manipulation: Fixing our strings so we can use them

Lecture 4: Metanodes: Combine nodes to clean up your workflow

Lecture 5: String Manipulation (part deux): So important we do it twice

Lecture 6: Rule engine: How to use if-else statements in KNIME

Lecture 7: Row Filter: Removing rows we do not want to analyze

Lecture 8: Missing Data: How KNIME can help us deal

Chapter 4: Modeling Overview

Lecture 1: Modeling introduction

Lecture 2: CRISP-DM data problem solving methodology

Lecture 3: What is machine learning?

Chapter 5: Client model #1: Random Forest for predicting sales outcomes

Lecture 1: Jacksonville Sales & Marketing business situation

Lecture 2: The Random Forest model: how it works and set up in KNIME

Lecture 3: Concatenate / Union to make a complete data set

Lecture 4: Joiner – another super powerful node; create data set with outcomes for modeling

Lecture 5: Filtering and binning

Lecture 6: Implementing the model

Lecture 7: Model scoring / business usefulness

Chapter 6: Client model #2: Linear regression for call center performance improvement

Lecture 1: Call Center Collections (CCC) business and data situation

Lecture 2: Linear regression and how to perform it correctly

Lecture 3: Implementing linear regression part 1: Building

Lecture 4: Implementing linear regression part 2: Refining

Lecture 5: Implementing linear regression part 3: Checking assumptions

Lecture 6: Evaluating linear regression

Lecture 7: What can we do with this?

Chapter 7: Client model #3 – K-Means and clustering to find attractive segments

Lecture 1: Bobs Best Boats – business and data understanding

Lecture 2: K-Means clustering – how it works

Lecture 3: Performing our first K-Means cluster

Lecture 4: Correcting our first K-Means cluster

Lecture 5: Evaluating the clusters – are they any good?

Lecture 6: Creating better segments for the client

Chapter 8: If you are interested in learning more

Lecture 1: My thoughts on where to go from here

Lecture 2: Additional resources that I think are solid

Instructors

Eric Hulbert
Data scientist | Top tier consultant

Rating Distribution

1 stars: 0 votes

2 stars: 1 votes

3 stars: 9 votes

4 stars: 13 votes

5 stars: 26 votes

Frequently Asked Questions

How long do I have access to the course materials?

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

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!