FasteR Workflow: Time - Saving Tips for Starting Data Science with R Programming

R Programming has been a popular statistical analysis language for analysts at corporations, government agencies, and research organizations. But as a single threaded language R programming capabilities have been limited to local usage on laptops. With libraries and cloud platform options, analysts can now save time in running R programs and create a stable workflow.

This brief talk will show how attendees can conduct data exploration in R programming. Attendees will learn about libraries for data exploration, tips for working with other professionals in examining data, and the latest features in R Studio (1.4) and for R (4.0).

Raiders of the Lost Query: Learning Best Practices for Exploratory Data Analysis in R Programming

Indiana Jones had his hat, his whip, and his wits to save the day. But developers and managers need a lot more to program data models.

With so many dataset tools for data science available, managers and developers can create statistical programming models, but are overwhelmed as to how to best explore the dataset. Most professionals conducting data science spend a majority of their time exploring and cleaning data. Databases are increasingly containing semi-structured data, thanks to varied sources such as social media, mobile devices, geolocation, and attributes describing real-world structures. Being able to blend data from a range sources and create useful correlation require some knowledge as to know when to apply exploratory steps effectively.

This brief talk will show how attendees can better plan for speedier analysis of datasets so that developer/manager teams can develop better regression and machine learning models. This session will cover the querying features in popular data repositories (Kaggle, data.world), data exploration techniques using libraries and functions in R Programming, and ideas to systematically communicate with team members on the data exploration process.

The end result is a faster means to establish a better quality dataset, leading to better analysis for regressions, machine learning models, and other data science projects.

Now That Everybody's Selling Online: Digital Marketing Tactics For Startup Developer Teams

The COVID-19 pandemic has ushered online commerce into a new era. Competition to gain and retain customer attention is high, while consumers who have increased social media usage have raised all kinds of service expectations from the brands they work with. Marketers of all kinds must adjust how to demonstrate a product or service that benefits customers' needs.

But for a startup team, creating a marketing team and scaling an audience with a large marketing budget is not an option. Yet the few marketers in your start-up may be overwhelmed to reach your customers. What can your team do to keep the messaging around your app or services on track?

This session will cover tactics that will keep the team on track. By using analytics and project management wisely, a small team can get a better handle on where to invest their many digital marketing options. They can also explore techniques for improving communication within their team, partners, and customers. They will learn ideas for engaging with customers the right way that supports genuine interest in a product or service - all of which leads to better quality sales.

Three main themes will be explored:
1. The changing digital landscape – what’s new in digital media, creator trends among influencers, digital marketing tools, and project management tools for collaborations 

2. Tips for applying analytics tools to manage media images and information effectively 

3. Tips for learning where to efficiently partner with marketers that leads to building an enthusiastic community of customers

Topics will be covered with an emphasis on organizing your time across social media platforms, digital analytics (Google Analytics), written content ideas to support a product or service under development, and marketing options. Attendees will also learn the order to approach analytics reports in social media and for their app via Google Analytics, learn what to expect to work with reports on a regular basis, and how developer teams can organize collaboration with marketers as needed.

No specific prior knowledge of business or marketing will be expected for attendance.

Learning Objectives
- Crafting a message that complements a customer need for a product or service 

- Brainstorming blog posts, email, and video ideas against your product or service development schedule
- Learning the definition of paid, earned media, owned media and how they influence sales
- Tips for managing images and video, including a content calendar

- Learn which reports in Google Analytics can guide users to the content that is engaging customers and ideas to create more or less content based on the topics
- Dashboard tips to plan content regularly
Tools like GitHub Projects and Notion to keep marketer teams informed of mechanical changes that impact customer experiences with apps and solutions

Takeaways

- Attendees will learn how to delve into analytics reports to determine what evergreen content is best suited for further engagement
-Attendees will learn how social commerce trends from the COVID-19 will impact their social media and digital marketing strategies
- Attendees will learn how to organize a marketing workflow with a small team to leverage tools and communicate the next steps without straining team resources

Placing The Right Machine Learning Bets: Understanding MonteCarlo Markov Chain in R programming

Markov Chain is a probability theory that has been gaining popularity in modeling the likelihood of customer behavior. Monte Carlo Markov Chain (MCMC) is a particularly useful variation in marketing and finance problems involving a probability analysis of data, especially when machine learning is a factor in delivering a solution. In this presentation, attendees will learn about creating a Monte Carlo Markov Chain model using R programming libraries. Attendees will learn the best use cases to apply these techniques for marketing and financial data models.

This session is meant for developers using R with an interest in statistical data models or machine learning. The session will cover a few basic data type concepts, thus having prior experience with R or data science is not necessary.

The Google Analytics Suite - From Tag Manager to Data Studio

Many business owners and teams know they need analytics for their marketing, but become overwhelmed with the choices available. This session will explain the choices that arise from applying Google Tag Manager for email, social media, and e-commerce marketing, and Google Analytics & Google Data Studio for reporting and understanding the results.

Aimed at startups, small businesses, online retailers, and independent developers, this workshop will help businesses better understand their marketing results, gain meaningful sales and build their business for the long haul. Come to learn the basic steps from Google Analytics reports to the advanced analytics available.

Learning R Programming and Python Together - Data Science Primer for Everyone

Both R programming and Python have become workhorse languages in the field of data science. They have allowed practitioners to apply statistical frameworks to a vast array of data models. Today, new tools allow a combination of principles from both languages. How can tech professionals gain the right overview, especially as tools like Generational AI, Posit Cloud, VSCode, and Python In Excel introduce exciting and provocative ways to use of R and Python.

This workshop will go through the key functions of R programming and Python with respect to data models. Tips and lessons will include many of the latest libraries and how to develop models to explore data for business applications * The basic programming protocols for importing data * The basics of RStudio (the IDE used for R as well as Python via reticulate * Incorporating * Data sources and ideas for creating models, from regression to sentiment analysis * Data visualizations * Tips for using AI tools ethically * Best data model practices