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.
Enroll now: Data science, machine learning, and analytics without coding
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 (don't 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: Bob's 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!
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