Monday, 29 April 2024

Students use data science to improve food access


 Food access is a serious problem that affects all kinds of communities, including the Binghamton region. Using data science, Binghamton University students are doing what they can to help.

The digital and data studies program at Binghamton University has teamed up with a local organization to address food insecurity. The mutually beneficial connection has helped make great strides in improving food access to the local area.

It all started when the Broome County Food Council surveyed community members on food-related issues. The organization collected a lot of data—1,300 people responded—but needed to analyze it.

That’s where Faculty Engagement Associate Barrett Brenton stepped in. He connected the council with what he saw as an ideal partner: a class in the digital and data studies program.

The program, which is less than two years old, serves as a minor for students. According to coordinator Melissa Haller, the goal of the program is to teach students valuable tech skills. The true emphasis, however, is to think about how students can use those skills to benefit their communities and do social good with data. She found this project as a perfect opportunity to do so.

“It gives them the opportunity to work with a real-world organization — the Broome County Food Council — and to really see the kind of impact the work that they do can have on the local community,” said Haller, who teaches the class.

As for the students, they are also reaping the benefits of this experience.

“At first, we saw Binghamton as our place of study and where we come to work,” said Julia Gnad, a math major and student in the class. “But, I think this project made it more of a community for us. I look back on this and it’s just made this area more to me. I take a lot of pride in helping this community that I’ve called home for four years. I think we were all really grateful for how this project helped us to realize what a career in data analytics looks like and what it could be for us.”

Gnad is one of three students in the class, along with Kajsa Kenney and Brianna Sexton. The class ended after the fall semester, but the trio was so passionate about helping the council that they continued to work with it throughout the spring semester.

According to the survey, two of the biggest obstacles to food access in the area are income and transportation issues. Food Council Coordinator Theresa Krause explained that getting a handle on this issue was especially complex, due to Broome County being “very high rural, very high urban and very unique.”

The end goal is to develop a food access plan for the entire community. The strong analysis of this survey has started to answer some key questions in order to do that, Krause said.

For Haller, the overwhelming success of this project has inspired her to teach a course titled “Community Practice” in the fall. In this course, analysis with the Council will continue, and the class will also help other organizations in the community.

Haller and Brenton both attribute the success of this project to students and believe that it is a good representation of what the digital and data studies program, and other programs like it, can do.

“The analysis done by students in this capstone project is really the kind of exemplar that we want to show how the University can utilize expertise,” Brenton said. “Again, not coming in as experts, but utilizing the expertise we have at the University campus to then engage with the broader community.”

The council will keep working on getting food to the people and people to the food using the information provided by the students, Krause said. The data analysis will be incorporated at numerous future meetings. In September, the council will prevent the food access plan that is currently being developed to the community.

“I see that they brought their gifts and their talents to the table. That spoke to the organization of the council, and this created an excitement and it created a movement going forward – a trust. This is exactly what the council is doing in the community. It was very inspiring. And as we move forward with the food access plan for Broome and developing the strategic initiatives, it just continues to grow and gain roots of foundation,” Krause said.

Haller said she has been amazed by both the students’ work and the strong partnership with the council. She greatly appreciates that the true goal of the new program — to make positive changes with data — is being accomplished through this project.

“I think when you’re learning how to analyze data, it’s very easy to see the work that you do as just numbers,” she said. “And I think one of the incredible benefits of getting to work with the Food Council is that the students have been able to see firsthand what those numbers mean.”

Compiled by Bhumika Sharma

Sunday, 28 April 2024

Top 12 AI podcasts to listen to.


 Artificial intelligence is a hot topic in every business and household today.

While AI was first developed in the 1950s, it has taken on a new life in recent years -- in large part due to the launch of OpenAI's ChatGPT in 2022.

Many companies have already adopted AI in some way -- including marketing, customer service and data analysis -- with more companies joining the fray each day. But with AI technology changing so fast, it can be hard to stay up to date on the latest developments and news.

Podcasts are an effective way to stay current on news in the AI world. There are AI podcasts to meet the needs of listeners of all levels. Some break down the latest AI news into easy-to-digest bites. Others give listeners access to the top minds in AI and related industries. TechTarget's Targeting AI is just one of many podcasts available to listeners. It features evergreen content on the world of AI as well as current AI developments.

Here are 12 of the top AI podcasts available on Apple, Spotify and other platforms.

Each of these podcasts was chosen from searches on Google, Spotify and Apple podcasts. All have above a 4-star rating and some are award-winning. All are hosted by long-time tech journalists, industry experts or researchers.

AI Breakdown

From The Breakdown Network, AI Breakdown is a daily podcast focused on AI news analysis. Host Nathaniel Whittemore guides listeners through the latest news in AI and examines what these changes mean for advancements in human creativity, disruptions to work and industries, and the ever-changing relationships between humans and computers.

Where to listen: Spotify, Apple and YouTube.

Average episode length: 25 minutes.

AI in Business

The AI in Business podcast is presented by Emerj, a publishing and marketing research company focusing on enterprise AI ROI. Hosted by Daniel Faggella, this podcast is geared toward nontechnical business leaders who want to integrate AI into their business practices to accelerate growth and deliver ROI. Through interviews with leaders from companies such as Facebook, Mastercard and IBM, Faggella uncovers use cases, best practices and the keys to success in implementing AI in business.

Where to listen: Apple, Spotify and Soundcloud.

Average episode length: 27 minutes.

AI Today Podcast

Hosts Kathleen Walch and Ronald Schmelzer, both founders and managing partners of AI at Cognilytica, discuss the latest AI news in the AI Today Podcast. Discussions include cutting-edge AI technology and interviews with expert guests in easily digestible content that can be applied to real-world issues in the AI and tech industries.

Where to listen: Apple and Spotify.

Average episode length: 20 minutes.

Data Skeptic

Data Skeptic is an interview-based podcast hosted by Kyle Polich that discusses topics including AI, machine learning, data science and statistics. It features themed seasons and offers a bingeable season of AI-related content -- including discussions on large language models, brain-inspired AI and safety concerns with AGI.

Where to listen: Apple and Spotify.

Average episode length: 40 minutes.

Eye on AI

The Eye on AI podcast focuses on an AI expert in each episode. Host Craig S. Smith -- an award-winning correspondent for The New York Times -- interviews AI researchers, tech business leaders and other experts who are leading trends in the AI and machine learning world. The podcast covers many topics including the use of AI in advanced robotics, harnessing AI for synthetic biology and the potential risks of AI use.

Where to listen: Apple and Spotify.

Average episode length: 50 minutes.

Hard Fork

Hard Fork is a technology podcast from The New York Times, hosted by journalists Kevin Roose and Casey Newton. The podcast covers the latest stories in tech and frequently features news and discussions related to AI. Hard Fork won the 2024 iHeart Podcast Award for best in tech.

Where to listen: Apple, Spotify and YouTube.

Average episode length: 70 minutes.

Lex Fridman Podcast

Lex Fridman is an AI researcher at MIT with current research in robot-human interaction and machine learning. He hosts his self-named podcast, which discusses AI, history and current world events. It features in-depth interviews with expert guests, including Sam Altman, Yann LeCun and Elon Musk.

Where to listen: Apple, Spotify and YouTube.

Average episode length: 2+ hours.

Me, Myself and AI

From MIT Sloan Management Review and Boston Consulting Group, Me, Myself and AI is a podcast that asks the question: Why aren't more companies finding success with AI? Hosts Sam Ransbotham and Shervin Khodabandeh attempt to answer this question and more through interviews with leaders from organizations that have found success through AI adoption such as NASA, Volvo and Duolingo.

Where to listen: Apple and Spotify.

Average episode length: 28 minutes.

Practical AI

Practical AI is a weekly podcast, presented by Changelog, that takes a refreshing look at the world of AI and machine learning. Chris Benson and co-host Daniel Whitenack break down the latest trends in AI through discussions with technology professionals, students and industry experts. Taking a practical approach, Benson and Whitenack tackle diverse topics including machine learning, neural networks, generative adversarial networks and large language models in an accessible way that lets listeners apply this information to real-world situations.

Where to listen: Apple and Spotify.

Average episode length: 45 minutes.

The AI Podcast

Host Noah Kravitz interviews guests who are making a difference in their industry through AI. Guests have included a doctor developing AI-powered technology to detect potential heart disease and a startup CEO using AI-driven dubbing to break down language and cultural barriers. Each episode of Nvidia's AI Podcast showcases a unique story about AI and its effect on the world.

Where to listen: Apple, Spotify and Soundcloud.

Average episode length: 30 minutes.

The Artificial Intelligence Show

The Artificial Intelligence Show -- formerly known as The Marketing AI Show -- aims to make AI accessible to the business sector. Host Paul Roetzer is the founder and CEO of the Marketing AI Institute and creator of the Marketing AI Conference. He and co-host Mike Kaput break down the latest AI news to give business owners and professionals actionable insight to accelerate business and career growth.

Where to listen: Apple, Spotify and YouTube.

Average episode length: 65 minutes.

The TWIML AI Podcast

Hosted by AI industry analyst and thought leader, Sam Charrington The TWIML AI Podcast (formerly This Week in Machine Learning & AI) covers topics including machine learning, AI, deep learning, neural networks and natural language processing. Each episode gives tech-savvy business and IT professionals access to the minds of experts and leaders from the machine learning and AI industry. With more than 7 million downloads, The TWIML AI Podcast is a leading voice in the ML/AI industry.

Where to listen: Apple and Spotify.

Average episode length: 45 minutes.


Compiled by Bhumika Sharma

Saturday, 27 April 2024

Data Scientist vs Data Analyst

 


Data analysts and data scientists both work with data, but they have different roles and skill sets:
  • Data analysts
    Use descriptive and prescriptive analytics to interpret existing data and help businesses answer questions. They use tools like Excel, SQL, and business intelligence programs to collect, process, and analyze large data sets, develop databases, and present insights to stakeholders. Data analysts typically need to understand basic statistics, descriptive statistics, and foundational math, and know SQL and some Python.
  • Data scientists
    Use predictive analytics and machine learning to create frameworks and algorithms to capture, store, manipulate, and analyze data to generate value for organizations. They develop new ways to ask and answer business questions, and create sophisticated models to predict future trends. Data scientists typically need to know advanced statistics, linear algebra, calculus, SQL, scripting languages like Python and R, and tools like Jupyter notebook, Google collab notebooks, and Our Studio. 

Which is better data analyst or data scientist?

Neither role is universally "better" as it depends on individual interests and skills. Data analysts typically interpret existing data to help businesses make informed decisions using tools like Excel and SQL. Data scientists, however, often create sophisticated models to predict future trends and require skills in programming languages like Python and machine learning techniques. The best role for you depends on your career goals and technical inclination.

Similarities Between Data Analysts and Data Scientists

Aspect

Data Analysts

Data Scientists

Core Focus

Data

Data

Primary Objective

Analyze data to find actionable insights

Analyze and model data to predict and optimize outcomes

Key Skills

  • Statistical analysis
  • Data visualization
  • Advanced statistical analysis
  • Data visualization

Tools Used

  • SQL
  • Excel
  • Basic analytics tools (e.g., R)
  • SQL
  • Python/R (used for advanced analytics)
  • Advanced analytics tools

Work Environment

- Collaborative, often part of a data team

- Collaborative, often part of a data or cross-functional team

Decision Making

- Supports business decisions through insights

- Drives business decisions through predictive analytics and insights

Business Impact

- Helps businesses understand and utilize data

- Helps businesses forecast, optimize, and innovate using data

Continuous Learning

- Requires staying updated with current analytics trends and tools

- Requires keeping up with advancements in machine learning, AI, and big data technologies

Communication

- Must effectively communicate findings to stakeholders

- Must explain complex models and predictions to non-technical stakeholders

Conclusion

Choosing between a career in data science and data analysis ultimately depends on your interests and strengths. If you are inclined towards more technical, algorithmic challenges and enjoy delving deep into machine learning and predictive modeling, data science might be the right path. It requires strong programming skills and a robust understanding of advanced statistics. On the other hand, data analysis could be a better fit if you prefer exploring clear insights from data and presenting them in an impactful way, with a lesser focus on heavy coding and complex algorithms. This path involves mastering data manipulation, visualization tools, and statistical analysis but doesn't usually require as deep a dive into programming as data science. Assessing your aptitude for mathematics, enthusiasm for technological innovation, and career goals will help guide your decision.

Complied by Bhumika Sharma

Friday, 26 April 2024

Recommender system : What is it ??


 Have you ever wondered how come Facebook suggests you the friends whom you know somehow or you heard about them, or how come Netflix showing you different home screens with different movies you may like and you can not stop yourself to watch those movies, or blogging site like medium keep suggesting/showing you the blogs of your interest and keep you engage with the site, these all because of the very strong recommendation algorithm used by these sites.

In This case study, we’ll learn what is recommendation system?how it helps the various industry .

What is recommendation system ?

It is a machine learning algorithm that suggests relevant items to users, It is a kind of filtering system that seeks to predict the rating or the preference a user might give to an item and based on that predicted rating or preference system starts recommending the product to users, Eg: In the case of Netflix which movie to watch, In the case of e-commerce which product to buy, or In the case of kindle which book to read, etc.

Recommendation systems help to improve the user’s on-site experience by creating customized recommendations for every kind of user. This will give the user a better product search experience and will result in more user engagement on the site. There are 2 types of recommendation.

I. Content Based Filtering



In this type of recommendation system, relevant items are shown using the content of the previously searched items by the users. Here content refers to the attribute/tag of the product that the user like. In this type of system, products are tagged using certain keywords, then the system tries to understand what the user wants and looks in its database and finally tries to recommend different products that the user wants.

The advantage of this is that it doesn’t need data of other users since recommendations are specific to a single user and It makes it easier to scale to a large number of users.The model can Capture the specific Interests of the user and can recommend items that very few other users are interested in.

But there is also a disadvantage of this model in that it can only make recommendations based on the existing interest of a user. In other words, the model has limited ability to expand on the user’s existing interests.

II. Collaborative Filtering



Recommending the new items to users based on the interest and preference of other similar users is basically collaborative-based filtering. For eg:- When we shop on Amazon it recommends new products saying “Customer who viewed this also viewed” as shown below.

The advantage of this model is that it helps the users to discover a new interest in a given item but the model might still recommend it because similar users are interested in that item. But biggest disadvantage of this model is that It cannot handle new items because the model doesn’t get trained on the newly added items in the database. This problem is known as Cold Start Problem.

There are 2 types of collaborative filtering

a) User-Based Collaborative Filtering



Rating of the item is done using the rating of neighbouring users. In simple words, It is based on the notion of users’ similarity.

b) Item-Based Collaborative Filtering


The rating of the item is predicted using the user’s rating on neighbouring items. In simple words, it is based on the notion of item similarity.



Compiled by Bhumika Sharma 

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