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.
  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)