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. 2. Introduction and MindShift Refresher

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Into Video:

Hello Learner, this 30 min course is designed to give you an overview of the data analytics field to help with a career enhancement, a career change, or for you to develop an understanding data analytics. We do this by supporting you in building mental models of the field. This course builds upon the MindShift concepts in the self-directed learning course you previously took, taking those concepts and applying them to the Data Analytics domain. Don’t worry we have a quick review of what you learned in the Self-Directed course a little bit later.

Data Analytics is a set of tools, processes and techniques using data to make better decisions, automate a process, or improve performance. In the course we will:

Outline the areas of Data Analytics, AI, Data Science, Data Engineering, etc.
Understand terms like Big Data, Cloud Computing, Deep Learning
Examine the process of doing data analysis project
Help you develop a roadmap about the Data Analytics field and
in the end, you will have done exercises to give you more clarity about the field

Now a little about Team MindShift. We are an innovative training and technology solutions company, with decades of experience in technology, entrepreneurship, and education at a university and corporate level. We believe in empowering individuals with the ability to “learn to learn how to learn” fearlessly.

Now let’s have that quick MindShift review.

Remember in MindShift. We talked about the importance of developing your why and how it helps to give you purpose and focus. After this video, you will develop your why for this data analytics overview. Get more specific. This is a different exercise than any before.

Also in talking about MindShift we discussed
* emotion and self-talk can be channeled towards your goals. Use them to your advantage (not hard or easy but unknown to known)
* conceptually mapping out knowledge and connecting skills to that model allows flexibility and speed in learning. This course is designed to continue to strengthen those capabilities
* look at other areas of knowledge that you have and see how they may apply to what you are learning in this course

Have Fun and Choose Powerfully

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Video Study Notes - Hide/Show
Data Analytics is a set of tools, processes, and techniques using data to make better decisions, automate a process, or improve performance

Remember from self-directed learners guide MindShift is:

  • emotion and self-talk can be channeled towards your goals. Use them to your advantage (not hard or easy but unknown to known)
  • conceptually mapping out knowledge and connecting skills to that model allows flexibility and speed in learning. This course is designed to continue to strengthen those capabilities.
  • look at other areas of knowledge that you have and see how they may apply to what you are learning in this course

About Team MindShift – Decades of experience in technology, entrepreneurship, and education at a university and corporate level.

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MindShift Review

 

Complete the exercise that follows the video. If you need to, watch the Why video again for inspiring you to develop your why for this course.

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Developing your Why gives you purpose and direction bring about more focused results.

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Exercise

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2. 1. Pre Survey

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3. 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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4. 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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5. 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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6. 7. Rating

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7. 6. Summary

MindShift Review

Data Analytics is a set of tools, processes, and techniques using data to make better decisions, automate a process, or improve performance Remember from self-directed learners guide MindShift is:
  • emotion and self-talk can be channeled towards your goals. Use them to your advantage (not hard or easy but unknown to known)
  • conceptually mapping out knowledge and connecting skills to that model allows flexibility and speed in learning. This course is designed to continue to strengthen those capabilities.
  • look at other areas of knowledge that you have and see how they may apply to what you are learning in this course
About Team MindShift - Decades of experience in technology, entrepreneurship, and education at a university and corporate level.

Move Towards Automation

  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.

Knowledge Areas

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

Activity Areas

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)

8. 8. Post Survey

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9. 9. Net Promoter

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