1) Data Analytics/Data Science/AI (Career or Interest) Prep Certification [+]
2) Data Analytics Overview [+]
3) The Self-Directed Learner’s Guide (to Success) [+]
4) The Self-Directed Learner’s Guide to Success [+]
5) Introduction to Machine Learning [+]
6) Data Empowerment [+]

1. Pre Survey Analytics (3 min)

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2. Your Why (6 min)

To continue your journey please watch the 4-minute video below.

After the “Know Your Why” video, watch this video for the next step.

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Defining Your Why

  1. Defining your why gives you more direction.
  2. Once you have your first why go a level deeper and ask yourself, why is that your why and come up with a second why.
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Exercise

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3. Welcome (7 min)

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Welcome to the Course

  • Nervous about learning new things and technology moving fast?
  • Want to learn faster, applying knowledge across multiple subjects?
  • Approach courses with a life-changing perspective
  • People reported being more comfortable learning and being in new environments with increased speed

Why This Course

  • The completion rate of Nanodegrees is at best 34% ([1][2]) with private undergrad colleges at 64% [3]
  • Traditional education is based on an industrial-age model. (See Sir Ken Robinson’s TED Talk [4])
  • In the Information Age, we need a learning paradigm shift.
  • Traditional Learning Structure:
    • Structured learning times (classes)
    • Imposed deadlines (quarter, semester)
    • Guidance on what to learn (teachers)
  • Self-Directed Learning Structure”
    • Loose learning times (online classes a 24/7 access)
    • Loose deadlines (complete this by next week or at your own paces)
    • Guidance is shared between you and the structure of the online class
  • Time for a change
  • Understanding Data Analytics helps to have
    • Positive emotion and self-talk guide
    • Techniques for developing mental models of a domain
    • Overview of Data Analytics domain

Robert Joseph, Ph.D.

  • Advanced Degrees From MIT and CMU
  • Data Scientist and Former Director at Stanley Black & Decker
  • University Professor for over 15 Years, 10 of them fulltime teaching adult learners
  • State of the art development for over 20 years
  • Patent for Internet DJ
  • Father of 2 Awesome Teenage Boys

Class Philosophy

  • You are ultimately responsible for your learning.
  • No course will give you everything you need because everyone needs something different.
  • Use supplemental sources; some listed on the Resources page. 
  • Map out your own learning path based on your and as you learn more continue to update your needs based on new information.
  • Use this and all courses as a guide and the entire knowledge source.
[1] https://urltms.com/udacity-completion – Completion rate for Udacity: 34%
[2] https://urltms.com/mooc-completion – Completion in MOOCs: 5-15%
[3] https://urltms.com/college-completion – “Federal Government Publishes More Complete Graduation Rate Data”
[4] https://urltms.com/sir-ken – Sir Ken Robinson TED Talk, “Changing education paradigms”

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4. Summary (3 min)

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Thank you for your time and for allowing me to do what I love.

HAVE FUN AND CHOOSE POWERFULLY!

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Finish up the final steps and you will receive your certificate.

The Plan: [ Click to Print ]

Data Analytics Learning Plan:

This plan was produced in the Data Analytics class based on the goals and process that wanted to execute. This plan can be changed at any time and is an aid in thinking about the process of being a self-directed learner, in essence, becoming both the student and the teacher.
  • Develop Your Specific Learning Goals
  • Develop the Why for Your Goals
  • Outline Your Plan
  • List Your Internal Needs and Plan How to Handle Them
  • List Support Systems and What You Need From Them
  • Start Building Mental Models For Your Learning Goals
  • Start Doing Course(s) or Program(s)
    • When Times Get Hard, Reread Your Why
    • Take Care of Yourself - You Can Do It All
    • Use Your Support System
  • Have Fun and Choose Powerfully!
1. Goal (edit)
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5. Learn to Learn How to Learn (L3) - Outline (edit)
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7. Example of Mental Model - What Does That Mean game (edit)
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8. Job Research, Support, Skills Liked and Skills to Upgrade (edit)
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5. Post Survey (2 min)

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6. Course Rating (1 min)

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7. Face Tracking (2 min)

MediaPipe Facemesh is a web-based facial tracking library. Below is a demo from https://github.com/tensorflow/tfjs-models/tree/master/facemesh. The images are just being shown in the browser. They are not being transmitted to any location. The video is totally private.



    The demo may take a seconds to be visible …    
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8. Smart Systems (2 min)

Driverless Cars

Click the video to see a 20 sec clip of a car driving all by it self. AI makes this possible. [Click on this link to see the entire video]

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See how the car performs without any human input.

  • Stops at lights
  • Stops at cars
  • Follows road
  • Changes Lanes
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Self Learning Mario Brothers Game Play

Click the video below to see a 15 sec clip of a Mario Brother game played completely by a computer. The computer learned how to play the game on its own without any rules of Mario play put into the system. [Click on this link to see the entire video]

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The computer learning how to play this game without any game input. It just had how to move the character and a function hat said how good or bad the systems behavior was. The system then played many games to become a Mario Brothers game playing expert.
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9. Machine Learning Example (4 min)

Machine Learning Clustering

Using the data provided below with petal dimensions this data, through machine learning we are able to determine that there is a high likelihood that there are 3 different types of species in this flower set. This is impressive because it is just based on data attributes that to the human eye don’t look that different.

Data

Graph After Machine Learning Algorithm

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10. Dashboard (4 min)

This is an example of a dashboard using data from Nike. Feel free to explore the data. You can click on the different factories and filter the data by selecting items from the pulldowns.

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11. Reflect on Analytics Overview

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12. Data Analytics Examples (2 min)

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Examples:

  1. Dashboard – explore Nike’s factory data.
  2. Machine Learning example – With petal data cluster the iris types
  3. Machine Vision Facial Tracking – technique can be used from helping disable to game control and more
  4. Deep Learning – sophisticated problems being solving by computers
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13. Developing a Roadmap (5 min)

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Use your:

  • Understanding of Data Analytics
  • Mental Models
  • Jobs and Skills Interesting to you

To build an initial roadmap of your learning journey. Change anytime and as much or as little as you want. It is your journey so have at it.

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Exercise

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14. Reflect on Examples

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15. Movement Towards Automation (15 min)

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Data Models

Data Analytics is using Data to make better decisions, automate a process, or improve performance. As you think about Data Analytics we will present several models of the area but you should build your models as you listen and think about there Data Analytics domain. The models we present give a starting point for you and will allow you insight into the domain as you begin or continue your understanding of Data Analytics. The models are:

  1. Data Usage Complexity: How data analytics moves from just presenting data to a user which can be a very powerful concept to moving towards total automation. This model can give you an idea of how different techniques can be used
  2. Data Analytics Skills:
  3. Data Analytics Project Process:

Data Usage Complexity

Data can be used on many levels which progresses in the power of the data solutions along with the sophistication of the usage. Below is a chart that captures the different uses of data and how you can go from getting access (excel spreadsheet) to data to actually having data drive decisions automatically done (self-driving cars). Companies, individual departments, and even individual people can use data in a spectrum of ways depending on the project, need, and capabilities as defined below.

Data Access

  • Spreadsheets help process data, but they are limited in that it is still up to the user to interpret the data.
  • Spreadsheets are a good tool when looking for answers to already formulated questions

Data Insights

  • By progressing into dashboards, data became easier to manipulate. Dashboards provide visual representations of data, which helps the user more easily interpret data.
  • Dashboards allow for a more interactive way to analyze data.
  • Using spreadsheets and dashboards as a means to analyze data is user-driven.

Predictive

  • Machine Learning allows a system to identify trends and insights from processed data without significant influence from the user.
  • With a goal in mind, the user can receive unsolicited insights from the system.
  • Contrasts with spreadsheets and dashboards which require the user to interpret the data and develop insights without help from the system.

Prescriptive

  • Deep learning allows the user to provide high-level goals. In addition to helping the user develop insights on the data, the system is now able to make decisions based on the data input in order to achieve the high-level goal.
  • For example, a good application of deep learning would be a self-driving car. The car is given a destination (the high-level goal) and is able to follow the path from start to finish based on the data it receives from its sensors.
  • Deep learning develops insights and provides unexpected solutions to high-level goals.

Automation

  • Finally, the technology and data are so rich that the system can make decisions better and quicker than humans, therefore we automate the entire process.
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  1. Data Access – allow users to see data that has been hidden from view or unavailable
  2. Data Visualization – allowing users to explore data and develop conclusions
  3. Predictive – with a degree of certainty forecasting future events based on historical events
  4. Prescriptive – with predicting the future suggesting solutions to achieve the future desired outcome
  5. Automation – to automate a process for monitoring, predicting the future, defining what can be done to achieve the desired future outcome and then doing the action without human intervention.

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Different level of automation and dependencies on complexity and system usage.

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Graphical of the different levels of automation
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Exercise

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16. Knowledge Areas (10 min)

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This section will give you a better framework for the inner workings of Data Science. It will also illustrate the skills needed for an individual to excel in this technology arena. The different resources that are needed for using Data Science in analyzing work projects, and some of the steps that go into creating these solutions. Let’s now examine the range of techniques that are needed in this field.

  1. Data Visualization: Allowing end-users to view data in a way that supports understanding and making good business decisions
  2. Artificial Intelligence: Making computer systems emulate the way that humans solve problems
    1. Machine Learning: uses historical data to predict the future (supervised and unsupervised)
    2. Neural Network: more advance data usage to detect patterns
    3. Other Techniques
  3. Soft Skills
  4. Programming: using commands given to computers to create user interfaces, analytical algorithms and process to solve, automate and support data-driven business decisions
  5. Data Sources: where data is being stored
    1. Types: Types of databases and data location for getting data for decision making (ex. SQL and NoSQL Databases, Streaming Data from Internet Of Things (IOT) Devices)
    2. Structure
  6. Math
  7. Concepts
    1. Big Data
    2. Cloud
    3. Edge Computing

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Graphical of the skills needed in Data Analytics
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Exercise

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17. Reflecting on Introduction (2 min)

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  • Ratings help us to make the course better
  • Ratings help you to reflect on what you learned
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18. Activity Areas (7 min)

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Once you have selected a problem to solve, you’re ready to execute a data analytics project. It has the following parts:

First, collect the raw data -> refine the data -> understand the data -> make sense of the data -> create the appropriate system for doing the analysis -> get the system in the end-user’s hand.

  1. Multiple Data Sources – Data can exist in many forms — from Excel spreadsheets being maintained on someone’s desktop to ERP systems used company-wide for the running of the organization. All of the pertinent data needs to be placed in a storage location that data scientists can retrieve.
  2. Data Storage – This is the location where data are stored. The raw data are stored so that you can always go back and get information if you need it in the future.
  3. Data Preparation – Data are very rarely ready for analysis so there is a data preparation process that consists of data cleaning (filling in missing values or correcting incorrect values) and data manipulation (this may consist of using business rules to augment the data).
  4. Data Learning – this step consists of seeking to gain insights and action items from the data. This can be in the form of a machine learning model, visualization dashboard, or deep learning system to integrate with the final workflow of the company.
  5. Learning Deployment – if the work is not being used then it is not making a positive impact on the company. The learning deployment allows end-users to take advantage of all the work that was done.
  6. Foundation – this is the infrastructure that is needed to make all of the above possible.

Team

  1. Data Engineers – responsible for getting the data from the data source and delivering it to a central location for usage. They also may be responsible for cleaning the data and formatting the data in a usable form.
  2. Data Scientists – responsible for performing analysis of the data. This analysis is based on delivering value to the business.
  3. Data Analyst – responsible for Data Access and Data Visualization, allowing end-users to get access to the data in a way that allows those end-users to explore the data and draw their own conclusions. through reports and dashboards.
  4. Data Architects along with Data Engineering and IT are responsible for the underlying structure that the system is built on. Many companies place this structure in the cloud (e.g. AWS, Azure, or some other cloud platform)

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Below are the activity areas of doing a data analytics project in a diagram form (see video notes for more descriptions).


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Graphical of the different job types
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Exercise

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19. MindShift Invitation (1 Min)

Please watch the next video for an invitation to shift your perspective:

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EL³G™ can help you achieve:

  1. Faster understanding
  2. Agility in thinking
  3. Knowledge without fear

EL³G™ stands for:

  1. Emotion and Self-Talk – can enhance or hinder your learning.
  2. Learning to Learn How to Learn – broadens your learning and understanding ability
  3. Generalization – demonstrates how to use these techniques to apply to everything.
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20. Emotion (7 min)

Please watch the short video below:

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Fear is not real. The only place that fear can exist is in our thoughts of the future. It is a product of our imagination, causing us to fear things that do not at present and may not ever exist. That is near insanity. Do not misunderstand me, danger is very real but fear is a choice. We are all telling ourselves a story.
– Will Smith, Movie: After Earth
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Watch the next video on emotion’s role in education.

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Emotions

  1. Emotions are the biggest barrier to learning and are all about the choices that we make.
  2. Fear, Frustration, and Self-doubt are all choices
  3. Developing that ability to choose is a like muscle, you can strengthen with practice.
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Exercise

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21. Self-Talk (8 min)

See the last 1 min of Dr. Crum TEDx Talk, “Change Your Mindset, Change the Game.”

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Dr. Crum TEDx Talk – Change Your Mindset, Change the Game

  • Our Mindsets Matter
  • Dr. Crum is a professor, psychologist, and researcher investigating how mindsets affect health and behavior.

Click on this link to see the entire video

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Please watch the video below then do the exercise.

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Self-Talk

“Whether you think you can, or you think you can’t – You’re Right.”
– by Henry Ford, American Industrialist

  • Changing what you tell yourself changes not only how you view things but also the actions that you take.
  • Stories can be very powerful so create stories that will help you reach your goals.
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Exercise

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22. Learning to Learn How to Learn (9 min)

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Learning to Learn How to Learn is a perspective that helps you

  1. Direct your learning
  2. Pull knowledge in from different subject area
  3. Provide a repeatable method for problem-solving

Give someone a fish and
They eat for a day

Teach someone to fish and
They eat for a lifetime

Teach someone to Learn “how to learn to fish” and
Everything is possible!
— modified by Robert


  1. Figure out what you are trying to learn and why
  2. As you are learning, filter the information into its simplest context.
  3. Act quickly in doing something fun and relevant to the information you’ve learned.
  4. Figure out a process on how to get yourself out of learning gaps by the following:
    1. Identify what you know
    2. Identify what you don’t know
      1. Break operations down into smaller steps and test assumptions.
      2. As you verify your assumptions, refine your thinking.
    3. Develop a plan on how to learn what you don’t know.
  5. This may require that you repeat the above steps several times as you continue to learn and grow. You don’t always solve the problem in one step but sometimes small steps towards the goal continue to get you there until you have that eureka moment and it all falls into place.
  6. Become a continuous universal learner
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Exercise

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23. Generalization (9 min)

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What is Transfer Learning and How Does it Help

Process for transferring knowledge from one thing to another. There are two types:

  1. Near – apply skills in one area to a similar area
  2. Far – apply what you learn to a different context

Strategies

  1. explicit teaching
  2. group learning
  3. reflection
  4. analogies and metaphors
  5. try generalizing
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Learning

  1. Thought about and used my skills in one area to apply them to a new area
  2. I worked to gain an understanding of the important components of comedy and how they fit together.
  3. Organizing principles in a way that made sense to me.
  4. I used additional sources to build on my knowledge
  5. I did not let negative talk creep into my learning
  6. I found an ally with a common goal
  7. All this allowed me to co-create something that I had never done before.
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Exercise

Base the following exercise on the Transfer Learning video.

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24. Reflect On MindShift (2 min)

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25. Building Mental Models (6 min)

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Mental Model – a tool used by a person to understand real-world objects, concepts, systems, and other things. Concepts about mental models are:

  1. Everyone creates mental models to represent the world around them.
  2. Mental models can be extended in many ways and here are two that I want to talk about:
    1. Decomposing concepts — this is used to identify building blocks
    2. Abstracting concepts — moving from specific to general

<!–

  • Mental models are representations based on a perspective and usage that highlight aspects of real-world concepts.
  • Mental models often have multiple layers to them.
  • Mental models allow you to reason and draw conclusions in one domain and make similar solutions in totally different domains.

–>

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Exercise

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26. Reflecting on Mental Models (2 min)

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27. Setting Yourself Up to Win (7 min)

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Some points of note:

  1. Set a goal and make a plan. You can always change it but setting a goal and making a plan brings it to life. It gets you underway.
  2. Goals and plans can be changed as you get more information.
  3. Learning is a continuous process that begins when you first conceive of an idea.
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Exercise

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28. Providing Internal Support (5 min)

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Things to consider as you start your journey:

  1. Take care of yourself
  2. Think about your internal needs before you start and come up with a plan on how to have them met.
  3. It is OK to change your plans and your thoughts but starting is key
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Exercise

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29. Getting External Support (5 min)

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Thinking about what you may need helps prepare you for your journey.

  1. Define what you think your needs are.
  2. Ask others for help.
  3. It is OK not to have everything all worked out. Starting is the first step.
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Exercise

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30. Reflect on Set You To Win (2 min)

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