When people think of the tech industry, it’s often names of men that come to mind. This is a shame because there’s a large number of outstanding women in tech who should be just as well known. In fact, there are far too many innovative and influential women in tech to fit into one list — this is just a...
Pandemic lockdown revealed not only Digital Divide, but challenges with students engagement, curriculum relevance , limited alternatives for high schoolers to finish education and acquire relevant skills. The education industry will change, and COVID is one of the factors pushing it forward. It is not an evolution, but revolution due to long awaited changes needed.
When changes are long awaited,...
What is your Brain Quadrant Dominance?
Just how positive a thinker are you?
HVTechFest's 2020 special guest Julie "Brain Lady" Andersen gave a special workshop How to Use the Science Behind Psychology to Train Your Brain for Greater Success in Business and in Life.. Watch it on YouTube, subscribe HVTechFest channel
Your...
Let's face it: small businesses generate a lot of data, but cannot utilize it.
Data can inform their decision making. How to get there? Where to start?
Developing a data-driven culture in the small business is a daunting task, especially during this time of economic recovery. In this hands-on workshop, attendees are introduced to a framework for data-driven decision-making. Using a step-by-step approach, the information used in the planning process is used to transformed into an action plan for implementation.
Understanding business analytics is an essential skill for the small business owner. In this hands-on workshop, a variety of business analytics concepts and related techniques are demonstrated using a common spreadsheet application.
Small or medium business (SMB) generates data, lots of data. Are we using it?
This data is a rich resource but many SMBs are held back from benefiting from that data because of the following factors:
Excel
In this course, students will learn how to put data into a chart, graph, or other visual format that helps inform analysis and interpretation using Excel, Tableau, R Studio, and PowerBI.
At the end of the course, student will know:
In this course, students will expand on skills acquired in the prior courses by learning how to combine R and Python capability into one project using R Studio and Anaconda. Students will develop a working Notebook using R Markdown and transfer results into an interactive web-based dashboard using Shiny.
At the end of the course, student will know:
In this course, students will learn how to load a data set into R for analysis. The R language will be covered to allow the student to become familiar with data analysis techniques accomplished. Specific algorithms and related packages will be used to develop machine learning models used for prediction.
At the end of the course, student will know:
In this course, students will learn how to load a data set into Python for analysis. The Python language will be covered to allow the student to become familiar with data analysis techniques accomplished. Specific algorithms and related libraries will be used to develop machine learning models used for prediction.
At the end of the course, student will know:
In this course, students will learn about the three main rules of data analysis and the associated statistical concepts relevant to performing different analytical methods. Using insight gaining from the data preprocessing step, students will learn how to identify a simple model with the ideal number of predictor variables.
At the end of the course, student will know:
Data analytics is important because it helps businesses to make better decisions, optimize business performance, analyze customer trends and satisfaction, which can lead to new—and better—products and services.
By 2022, 85% of companies will have adopted big data and analytics technologies and 96% of companies seeking to hire new permanent staff! (World Economic Forum).
In this course, students will learn the various methods used to source and load data sets selected for analysis. The features and attributes of data will be reviewed after a data quality assessment has been conducted.
basic MS Excel / GSuite Sheets
In this course, students will learn about the three main rules of data analysis and the associated statistical concepts relevant to performing different analytical methods. Using insight gaining from the data preprocessing step, students will learn how to identify a simple model with the ideal number of predictor variables.
At the end of the course, student will know:
In this course, students will learn how to load a data set into Python for analysis. The Python language will be covered to allow the student to become familiar with data analysis techniques accomplished. Specific algorithms and related libraries will be used to develop machine learning models used for prediction.
At the end of the course, student will know:
In this course, students will learn how to load a data set into R for analysis. The R language will be covered to allow the student to become familiar with data analysis techniques accomplished. Specific algorithms and related packages will be used to develop machine learning models used for prediction.
At the end of the course, student will know:
In this course, students will expand on skills acquired in the prior courses by learning how to combine R and Python capability into one project using R Studio and Anaconda. Students will develop a working Notebook using R Markdown and transfer results into an interactive web-based dashboard using Shiny.
At the end of the course, student will know:
In this course, students will learn how to put data into a chart, graph, or other visual format that helps inform analysis and interpretation using Excel, Tableau, R Studio, and PowerBI.
At the end of the course, student will know: