Podcasts about data analyst

Break into data analytics EVEN without a degree, just like our guest for today's episode! He's Ryan Ponder, a Data Analytics Accelerator program student who transitioned from Loan Officer to Data Analyst within his company-- without a degree. He shares how he leveraged internal opportunities and attained his new role. Tune in and learn actionable steps for making an internal pivot and overcoming career challenges!

Need any advice or information, message us.We sit down with James Neale, Data Analyst at Costa Rica Investments, to break down the latest trends in the vacation rental market. He shares insights on where occupancy remains strong, where average daily rates are slipping, and where he still sees promising investment opportunities across Costa Rica.Free 15 min consultation:  https://meetings.hubspot.com/jake806/crconsultContact us: info@investingcostarica.com

Melody Santos has successfully transitioned from a physical therapist to a revenue analyst in a few months! In this episode, she shares three main steps that expedited her journey, her struggles with imposter syndrome, and offers valuable advice for anyone looking to pivot into a data career.

If you're thinking about doing the Google Data Analytics Certificate, you need to hear this: DON'T. In this episode, I list five reasons why it is a waste of time.The ONLY Framework to Become a Data Analyst in 2025 (SPN Method): https://youtu.be/XUxWQgh3soo?si=v3SQV3zJ4h0jH1uQ

Anne-Margot Rodde, Founder & CEO, Creators Corp.For nearly two decades, Anne-Margot Rodde has been a pioneering force in video games, digital media, and the metaverse, holding strategic roles at leading agencies and driving high-profile projects for brands like Microsoft Xbox and PlayStation. Margot launched her first gaming venture with experiential agency WePlay, serving clients such as EA, Riot Games, Nexon, Epic Games, and IGN. After the pandemic opened new doors, she transitioned from the agency world to launch Creators Corp., an award-winning Fortnite Creative studio at the intersection of the creator economy and the metaverse. Creators Corp. specializes in designing original games that inspire and engage global audiences, partnering with IP holders and content creators across entertainment and gaming, while thoughtfully integrating brands where it adds value.Jake Laumann, Data Analyst at Creators Corp. As a former esports leader at Major League Baseball and video game expert, Jake Laumann holds the position of data analyst at Creators Corp., where he supports the team with gameplay optimization and on-platform marketing. 

In this episode, we explore the transformative impact of generative AI on the digital advertising ecosystem. Grant Gudgel, SVP of Marketing at Verve, joins IAB Europe's Data Analyst & Sustainability Lead, Dimitris Beis, to unpack how AI is reshaping consumer behaviour, disrupting traditional search models, and redefining the relationship between advertisers and publishers.Tune in to learn how stakeholders across the ecosystem can adapt and thrive in this new AI-powered era.

Charlotte Ledoux est une experte Data & IA Gouvernance qui crée du contenu sur LinkedIn avec beaucoup de succès (+35K abonnés). Dans ce 3ème épisode ensemble, Charlotte nous fait une Masterclass sur la mise en place d'un framework data domain.On aborde :

Maud est passée d'un rôle de Data Analyst à un rôle de Data Analyst Full-Stack en Freelance. Dans cet épisode, elle nous parle de sa transition, de sa formation et de son nouveau rôle.On aborde :

Megan Bowers took an unconventional path to break into the data world. Starting from a self-guided Data Science Bootcamp, she shared her journey through blogging and gained millions of views, and then BOOM! Job offers and monetization opportunities flooded. This is her story.

Samya est Generative AI Research Lead chez Pigment, la nouvelle licorne française qui a levé +230 millions de dollars.On aborde :

Learn all about how a Content Manager uses data to solve the world's problems, one blog post at a time.This week on the Data Podcast for Nerds!, I talked to Megan Bowers, who pivoted into data analytics after realizing she loved working with data in Excel. She took a bootcamp, landed a role using Power BI and Alteryx, and started blogging about her journey - sharing tips, project insights, and lessons learned. Some of her most popular blog posts:✅ "The Life of a Data Analyst" - series✅ "How Do You Know Your Analysis is 'Right'?"✅ "Data Science Concepts Explained to a Five-year-old"That blog led to her current job at Alteryx where she gets to write all about analytics to help others navigate the field... AND... hosts the Alteryx podcast, Alter Everything.We also talked about the various ways people have used Alteryx for fun personal projects. She mentioned a couple of her own projects, one involving Taylor Swift!If you're into data, storytelling, and exploring fun analytics use cases, you won't want to miss this episode.Where to find MeganLinkedIn, Medium, Alteryx Community, Alteryx Podcast******Data Career Coaching⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Book a call⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to learn moreMERCH!!Grab your ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠nerdnourishment swag⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠SupportIf you like what you see, consider ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠buying me a broccoli⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ (it fuels my creativity)

On this week's episode, Tim has a conversation with Justin Ray, a Golf Data Analyst who has made himself well-known across the Golf world. Tim and Justin discuss Justin's story that started at the University of Missouri, how he analyzes the game, and who he likes this week at Augusta. Please support our sponsors:Mark Hannah – Evergreen Wealth StrategiesJames Carlton Agency (State Farm)Design Aire Heating & CoolingFollow us on Social Media: @TMASTL on Twitter, @tma_stl on Instagram Learn more about your ad choices. Visit podcastchoices.com/adchoicesSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

On this week's episode, Tim has a conversation with Justin Ray, a Golf Data Analyst who has made himself well-known across the Golf world. Tim and Justin discuss Justin's story that started at the University of Missouri, how he analyzes the game, and who he likes this week at Augusta. Please support our sponsors: Mark Hannah – Evergreen Wealth Strategies James Carlton Agency (State Farm) Design Aire Heating & Cooling Follow us on Social Media: @TMASTL on Twitter, @tma_stl on Instagram Learn more about your ad choices. Visit podcastchoices.com/adchoices

A new report from energy think tank Ember reveals a global clean energy milestone: low-carbon sources supplied 40.9% of the world's electricity in 2024, driven by record-breaking growth in solar power. In contrast, ASEAN generated just 26% of its electricity from clean sources, and clean generation in the region is no longer keeping pace with demand. With massive solar and wind potential still largely untapped, experts say the region is well-positioned to accelerate its clean energy transition — if the right policies are in place. We speak to the co-authors of the report, Euan Graham (Global Electricity and Data Analyst, Ember) and Nicholas Fulghum (Senior Data Analyst, Ember) to explore the turning points, challenges, and opportunities shaping the future of electricity—especially in Asia.Image Credit: ShutterstockSee omnystudio.com/listener for privacy information.

Cindy Clifford, a seasoned educator of 25 years, refused to let age or past career define her. She used her skills honed as a teacher and pivoted to data analytics! If you feel you're too old to pivot and become a data analyst, it's never too late-- dive into Cindy's story.

Entrepreneurial Data-Driven Growth Strategies are no longer optional—they're essential for scaling and sustaining a successful business. In this episode, we sit down with James Childress, a seasoned CPA and growth advisor, to explore how entrepreneurs can harness data to drive profitability, efficiency, and sustainable growth.For founders, startup leaders, and small business owners looking for answers to critical business challenges, this conversation is a goldmine. James helps you understand and implement Financial Systems for Entrepreneurs that support long-term success. He explains the power of Data-Driven Business Decisions and shares actionable insights on Scaling a Business with Systems, all rooted in decades of experience.We also explore the foundational wisdom of W. Edwards Deming Business Principles, and how they still apply in today's age of Big Data, Artificial Intelligence, and real-time analytics. If you're searching for guidance on Profitability Optimization for Startups, Entrepreneurship and Financial Planning, or Strategic Forecasting for Entrepreneurs, this episode answers your questions with clarity and expertise.Whether you're studying Enterprise Growth Strategies Class 12 Entrepreneurship, developing Growth Strategies in Entrepreneurship, or working in Data Analysis as a Data Analyst, this conversation will deepen your understanding and give you tools to grow.You'll walk away knowing how to align your data systems, improve Decision Making, and embrace Data-Driven Marketing strategies—all while building a more resilient and impactful business.

Hosts Simon and Jillian discuss how you can uncover hidden trends and make data-driven decisions - all through natural conversation, with Amazon Q in Quicksight, plus, more of the latest updates from AWS. 00:00 - Intro, 00:22 - Top Stories, 02:50 - Analytics, 03:35 - Application Integrations, 04:48 - Amazon Sagemaker, 05:29 - Amazon Bedrock Knowledge Bases, 05:48- Amazon Polly, 06:46 - Amazon Bedrock, 07:31 - Amazon Bedrock Model Evolution LLM, 08:29 - Business Application, 08:58 - Compute, 09:51 - Contact Centers, 10:54 - Containers, 11:12 - Database, 14:21 - Developer Tools, 15:20 - Front End Web and Mobile, 15:45 - Games, 16:04 - Management and Governance, 16:35 - Media Services, 16:47 - Network and Content Delivery, 19:39 - Security Identity and Compliance, 20:24 - Serverless, 21:48 - Storage, 22:43 - Wrap up Show Notes: https://dqkop6u6q45rj.cloudfront.net/shownotes-20250404-184823.html

ABC15 data analyst Garrett Archer joins us to explain what happened in the stock market yesterday as a result of Trump's new tariffs, and what this means for our economy next week and into the future.

Recent avian flu hits & spring migration Egg supply & current demand  A sneak peek at eggs being covered at our Spring Seminar McKeany-Flavell's 2025 Spring Market Seminar: Industry Trends & Consumption Live online event! Free for all clients! Wednesday, April 23, 2025 Have you registered? Visit mckeany-flavell.com to get it done! Host: Eric Thornton, Senior Commodity Advisor Expert: Marilyn Adutwum, Data Analyst

Lou Welgryn est Secrétaire Générale chez Data For Good, l'association qui réunit 7000 bénévoles qui travaillent sur des projets data & IA à impact. Avant ça elle était Head of Product chez Carbon4 Finance (monté par Jean-Marc Jancovici).On aborde :

Jen Hawkins went from delivering pizzas to becoming a six-figure data analyst at a FAANG company in just 17 weeks. In our chat, she shares her Data Accelerator Program journey, how she used her background and new skills to stay motivated, land job offers, and eventually achieve her dream role.

Back for Part 2!Our host, Gareth McGlynn, continues the conversation with Andrew Pitcher, Data Analyst and Integrations Manager at Bartlett Cocke General Contractors, as they pick up where they left off—talking practical tools, systems, and strategies that are helping preconstruction teams stay ahead.Discussion Highlights:Integrating software to streamline communication and information sharing between teams.Inside Bartlett Cocke's custom cost forecasting tool – how it connects to their data warehouse.Making data work: how estimators and preconstruction managers can use it to support better decision-making.Tackling knowledge transfer – the most common (and most frustrating) pain point across teams.And much, much more.If you missed Part 1, you can catch it here: https://www.nichessp.com/pre-construction-podcast/139-andrew-pitcherAnd you can connect with Andrew via his LinkedIn: https://www.linkedin.com/in/andrew-pitcher-71006b47/

Highlights from this week's conversation include:AI in Transcription Services (1:11)The Future of AI Companies (5:09)Potential Risks of AI Tools (8:57)Learning vs. Dependency in Programming (10:17)The Journey of a Data Analyst (12:07)AI and Coding Skills (14:06)Abstraction in Data Tools (16:59)Data Design and AI (19:07)User Experience vs. AI Automation (22:10)AGI and Data Mesh (24:36)Blank Screen Interaction Challenges (27:10)Understanding User Value in Data Platforms (32:22)AI's Role in Simplifying Data Interaction (34:04)Final Thought and Takeaways (35:05)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

I talk with job search expert Steve Dalton about his radical approach to landing your dream job-- WITHOUT applying online! As the author of 'The Job Closer' and 'The 2-Hour Job Search, Steve advocates for a networking-based strategy and explains the importance of asking for advice rather than referrals.

Genevieve Hayes Consulting Episode 56: How a Data Scientist and a Content Expert Turned Disappointing Results into Viral Research It’s known as the “last mile problem” of data science and you’ve probably already encountered it in your career – the results of your sophisticated analysis mean nothing if you can’t get business adoption.In this episode, data analyst Dr Matt Hoffman and content expert Lauren Lang join Dr Genevieve Hayes to share how they cracked the “last mile problem” by teaming up to pool their expertise.Their surprising findings about Gen AI’s impact on developer productivity went viral across 75 global media outlets – not because of complex statistics, but because of how they told the story.Here’s what you’ll learn:Why the “last mile” is killing your data science impact – and how to fix it through strategic collaboration [01:00]The counterintuitive findings about Gen AI that sparked global attention (including a 40% increase in code defects) [13:02]How to transform “disappointing” technical results into compelling business narratives that drive real change [17:15]The exact process for structuring your insights to keep executives engaged (and off their phones) [08:31] Guest Bio Dr Matt Hoffman is a Senior Data Analyst: Strategic Insights at Uplevel and holds a PhD in Physics from the University of Washington. Lauren Lang is the Director of Content for Uplevel and is also a Content Strategy Coach for B2B marketers. Links Connect with Matt on LinkedInConnect with Lauren on LinkedInCan Generative AI Improve Developer Productivity? (Report) Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE Read Full Transcript [00:00:00] Dr Genevieve Hayes: Hello, and welcome to Value Driven Data Science, the podcast that helps data scientists transform their technical expertise into tangible business value, career autonomy, and financial reward. I’m Dr. Genevieve Hayes, and today I’m joined by Lauren Lang and Dr. Matt Hoffman. Lauren is the Director of Content for Uplevel and is also a Content Strategy Coach for B2B marketers.[00:00:26] Matt is a Data Analyst and Product Manager at Uplevel and holds a PhD in Physics from the University of Washington. In this episode, we’ll uncover proven strategies for transforming complex technical findings into compelling business narratives that drive real organizational change. So get ready to boost your impact, earn what you’re worth, and rewrite your career algorithm. Lauren, Matt, welcome to the show.[00:00:55] Lauren Lang: Hi Genevieve, thank you so much.[00:00:57] Dr Matt Hoffman: Thanks for having us. Excited to be here.[00:01:00] Dr Genevieve Hayes: In logistics, there’s a concept known as the last mile problem. Which refers to the fact that the last stage of the delivery process of people or goods is typically the most complex and expensive while also being the most essential. For example, it’s typically easier and cheaper to fly a plane full of packages from Australia to the U.[00:01:22] S. than it is to transport those packages by road to their final destinations within the U. S. Yet if you can’t distribute those packages once they arrive in the U. S., they may as well have never left Australia. It’s for this reason that supply chain managers typically focus a disproportionate amount of effort on planning those final miles.[00:01:43] Data scientists also face their own last mile problem. Despite many data science projects requiring sophisticated modelling and analysis techniques, the most difficult part of data science is often communicating the results of those projects to senior management and gaining adoption of the project from the business.[00:02:04] That is the final stage. Yet, unlike in logistics, This is also the stage where data scientists typically focus the least amount of effort, much to the detriment of their work and their careers. Lauren and Matt, the reason why we’ve got both of you as guests in today’s episode is because you’ve recently backed this trend and pooled your combined experience in communications and data science with outstanding results.[00:02:33] And this is actually the first time I’ve come across a data scientist working directly with the communications expert to address the data science last mile problem. Although, it probably should be far more common. So to begin with, Matt, can you give us an overview of the data science project you were working on and how you came to team up with Lauren when delivering the results?[00:02:57] Dr Matt Hoffman: So we work at Uplevel and Uplevel is a company that pulls in data about software engineers and we help tell those data stories to our customers. Senior leaders of engineering, like software engineering firms so that they can make data driven decisions and drive change within their organizations.[00:03:17] One of the things that’s really come up in the past year is this full topic of gen. AI software engineers being able to talk to an AI assistant to help them write code and the thinking was, oh, this is a silver bullet. We’re just going to be able to. Turn on this system. Our developers are going to be more productive.[00:03:36] Instantly. The code is going to get better. There’s going to be nothing but greenfield. If we just turn this on, it’s a no brainer, we heard those questions and we don’t develop our own gen AI tool. But what we do have is data about software engineers and how they spend their time, the effectiveness of their work.[00:03:54] Are they able to deliver more? Are they getting more things done? How’s the bug rate of their code? So it was natural for us to go explore that problem and really try to understand what is the impact of Gen AI on software engineers. That’s the problem that we were facing. So I work with our data science team.[00:04:13] I’m not actually on our data science team, but worked with them to go do this analysis to really try to understand how do people compare to themselves and what changes do we see within this. And then we pulled in Lauren to go start showing off what we found. And that’s where that story kicked off.[00:04:32] Dr Genevieve Hayes: Prior to working with Lauren, what are some of the challenges you encountered in communicating the results of your analysis?[00:04:38] Dr Matt Hoffman: Well, it’s always a tricky one when the answer is complicated. The real fundamental place that we at Uplevel are at is that this is human data. While we may be able to measure timestamps to a millisecond, This is all still predicated that this is people data and people do weird things. And the data is messy and the data is muddy.[00:05:03] So there’s the constant battle of, well, what can we trust? We’re looking for correlations and, you know, you squint to see if like, there’s something there you peel back a layer and then there’s something more, but people data is hard to work with. So that’s really a skill of our data science team to help pull that back.[00:05:20] But we were. Kind of struggling to make heads and tails of what were the real conclusions. And Lauren really helped clarify that story for us and get that communication there.[00:05:30] Dr Genevieve Hayes: People are irrational. I mean that’s the big problem with us. Before you did this, had you ever made some massive mistake because you just assumed people were rational when they worked?[00:05:44] Dr Matt Hoffman: It’s funny stuff so sometime when some work’s becoming delayed and you go ask for the root cause and it’s like, oh, someone’s saying, I thought I did that and I forgot. Like, I never hit the button. That’s the kind of, people data that we see is that, like, yeah, that happened.[00:05:59] It was late, but that was just because you forgot to hit the button. People’s behavior is really funny. So yeah, we just have to kind of take that into account that everybody’s different. That’s okay. And we need to bake that into our analysis, that people work differently and not try to over fit one model that applies to everybody .[00:06:18] Dr Genevieve Hayes: Yeah, I actually wrote a LinkedIn post a while ago saying, people are a problem with data and wouldn’t it be nice to just be dealing with mechanical processes? And I had someone reply to that post who works at a water agency where they don’t deal with people, it’s, water going through pipes, and they said, well actually mechanical processes are just as annoying, they just are annoying in different ways because you have the sensors malfunctioning and all this.[00:06:44] You can dream about not dealing with people but Machines cause problems too .[00:06:48] Dr Matt Hoffman: Yeah, that’s exactly right. So you just have to know that going in and know that it’s going to be messy. And plan for that.[00:06:56] Dr Genevieve Hayes: So Lauren, in your content strategy coaching work you’ve done a lot of work with software as a service companies. And as Matt said, Up Level itself is a company that Works with engineers and probably has a lot of engineers as its employees. So, I’d imagine you’ve worked with a lot of very technical people throughout your career.[00:07:20] Lauren Lang: I have. Yes.[00:07:21] Dr Genevieve Hayes: What are some of the biggest issues you’ve noticed in how technically minded people, especially data scientists and data analysts, present their findings to business stakeholders?[00:07:33] Lauren Lang: It’s very funny because I think that there is a lot of similarities actually between how data scientists might present their findings and how a lot of marketers present their findings. And you would think like, Oh, marketing is so much more. We have our thumb on the pulse of the business.[00:07:48] And, marketers are so much more business driven, but I think, anyone who is looking at data as marketers, we look at data too. We are. Not data scientists, but there’s a fair amount of data science, sometimes in marketing. And there’s a lot of data analysis that happens. And I think there is just this tendency sometimes to.[00:08:07] Get very myopic and get very focused on your own specific context in looking at the data and forgetting that there is probably a larger story that the data existed to tell. I see this a lot. 1 of the. Challenges that I see a lot is, marketers will go into a meeting with a CEO and they will have dashboard after dashboard and chart after chart.[00:08:31] And there is a very sort of distinct look on an executive space when. You’ve shown them three charts in a row or three dashboards and it’s like a completely blank look and you know that they are literally anywhere else. but in the conversation and it’s a little bit of like a death now.[00:08:51] And so I think for anyone who likes to geek out on data, whatever part of the business you’re in, you have to remember that there is this larger value story that you need to be telling, and you need to be showing that data and be mindful of the context in which you’re showing that data.[00:09:08] To what end? Rather than just taking people down the rabbit hole with you. I think sometimes there’s an assumption that everyone should be as interested about all of the nuances and slight, variances in the data as you are, and that’s not always the case.[00:09:24] Dr Genevieve Hayes: Yeah the way you’re describing that death knell face, yeah, I’ve seen that before. And worse than that is when the people you’re presenting to start playing with their phones. Then you definitely know that you’ve failed.[00:09:35] Lauren Lang: Might as well call it right there.[00:09:37] Dr Genevieve Hayes: Yeah, , just pack up and walk out of the room at that point.[00:09:39] Lauren Lang: That’s right. That’s right.[00:09:42] Dr Genevieve Hayes: So, I assume you’ve pointed out these issues to technical people who you’ve worked with. How do they typically respond when you say, hey, not everyone’s as geeky as you?[00:09:53] Lauren Lang: I think there’s a way to couch that in a way, because I have a lot of empathy for it. Geeky people are excited about what we do. I mean, there’s a passion there. And so you don’t want to not communicate that passion.[00:10:05] I think that’s really important. And, there’s some exciting results or, even. Not exciting results that you didn’t think were going to pan out, but there’s always a story to tell, but it’s just, can you tell it maybe at a slightly more abstract level of specificity, maybe? Or can you tell it with an understanding of the context in which your audience exists[00:10:28] I think there’s just a lot of tendency to Just forget that not everyone brings the same experiences and the same understanding and the same depth of knowledge to the table. And so the best way that the stories we tell with data can be impactful is to tell them in context and to be able to pull out the important parts that really can bring the message home.[00:10:50] Dr Genevieve Hayes: So, put yourself in the shoes of your audience,[00:10:53] Lauren Lang: absolutely. You should always have empathy with the person you’re trying to communicate to. I think it was Kim Scott said that communication happens at the listener’s ear and not the speaker’s mouth. That’s where meaning is made. It’s really important to keep that in mind as you are stepping into the shoes.[00:11:09] Of the communicator,[00:11:11] Dr Genevieve Hayes: so, I’d like to now take a deep dive into the project that the two of you collaborated on so Matt, how did you determine which insights from your analysis were most relevant for communicating with management? Are[00:11:24] Dr Matt Hoffman: So we have a set of measures at up level that are kind of part of our standard suite of analysis. So 1st, because if you can’t go explore the data for yourself and understand where your team’s at, then that’s a really unsatisfying experiment. So we knew that we wanted to look at some of these measures.[00:11:43] We’ve also been doing this for a few years now, so we do have a pretty good grasp on. You know, what are appropriate measures to look at for software engineers? And then what is completely inappropriate? That’s like, this is just not a good measure. You shouldn’t use it. It’s problematic for 1 reason or another.[00:12:01] So choosing those measures that we think. Are kind of universally applicable, are good proxies of how this experience may look, and then really trying to see what’s going to move and shift when we look at these. Those were kind of the criteria. We had a few hypotheses that we went in for how we thought things were going to move once you introduced Gen AI to the mix.[00:12:22] And we were surprised by our hypotheses, and we had to reject some of them, which was really fun. And it makes you really challenged that you’re doing it right. And then finding that this actually does go against what we thought would happen.[00:12:36] Dr Genevieve Hayes: you able to share any examples of these?[00:12:39] Dr Matt Hoffman: One of the things that we wrote about and we can share the link to our study was the general thinking was, hey, if you’re going to use Gen AI, you’re going to be able to ask questions and Jenny is going to help you write better code. So one of the things we looked at was. What’s the defect rate of code that gets merged and then it needs to get fixed later?[00:13:02] So how often does that happen? You would think that that would go down if the code is going to be of higher quality because Gen AI is helping you. Now what we found was that actually the defect rate went up. Another organization seemed to find the same thing, saying that the result of Gen AI was that there’s larger changes to code.[00:13:23] And then more things are going to get missed because the batch size is getting larger. So you might find things. four bugs, but there’s five because you’re writing bigger and bigger code changes. So we saw that the defect rate for the cohort that was using Gen AI went up by 40 percent compared to themselves, which is a pretty market change.[00:13:43] So that was one that , we were very surprised to see and are really interested to see what happens next with that as all these tools get better and better and better.[00:13:53] Dr Genevieve Hayes: insight you just described, that doesn’t surprise me because my own personal experience I’ve found with writing code using Gen AI, you can produce the code really, really fast. You’re spending. twice as long or three or four times as long debugging it, because there are all these bugs in it that would not be in there if you’d written it yourself.[00:14:14] And you’re just not used to having that many bugs to fix.[00:14:19] Dr Matt Hoffman: Yeah, and it might be not stylistic, like, the way that you think that you should write your code it might pull some solution that looks reasonable at first pass, but it’s pretty hard to debug if it’s the right thing when it, looks right, smells right, but then under the hood, there’s something wrong with it.[00:14:36] Also, Jenna, I doesn’t understand the context of the problem that you’re trying to go write code for. You have that in your head, you know where you’re at and where the destination is, and it’s going to help you write some code. But you have that.[00:14:49] Dr Genevieve Hayes: Yeah. And I’ve found it creates. Non existent Python packages and non existent Python functions, which is fun, because then you spend half an hour trying to find this package that doesn’t even exist.[00:15:02] Dr Matt Hoffman: It’s tricky. It really is. The other one that I would just briefly say that we looked at is we thought people would write code faster. That’s the statement that you just said. How quickly does it take to get from commit to merge? Does that really pick up? Because you’re using Gen AI.[00:15:16] And we found that it didn’t make much of a tangible impact. That there’s still a lot of time that’s spent when you’re trying to understand the problem of what you’re trying to solve, how you might approach it, the architecture of it. None of those things are going to go away.[00:15:31] Bottlenecks of having another human review your code, that doesn’t change whether they both have Gen AI or not. You’re still working with other people. So those structural factors do tend to be very important in this problem. And those are ones that you need to pursue and kind of conventional means of understanding how your teams work and doing better.[00:15:51] So that one didn’t move at all. And we thought that that would speed up. That was our hypothesis.[00:15:56] Dr Genevieve Hayes: Yeah, doesn’t surprise me. So, Lauren, how did you take these insights and structure them into a narrative that maximized their impact?[00:16:04] Lauren Lang: well, it was funny because even before we had done the research, we knew we wanted to do this research and we wanted to publish it. And looking from a content marketing perspective, I think original research right now is one of the most, potentially impactful formats for creating content.[00:16:23] And some of that is that, there is so much out there. That is just really bland. And I is not helping. Jenna is not helping with that. There’s a lot of content. That is just not special. It’s not differentiated. It’s not helping to educate or inform anybody or share anything new. And so when you have the opportunity to sort of lend something new to the conversation, that’s an important opportunity.[00:16:46] So we knew going in that we were going to do it. What we were not expecting were the results that we got. And I laughed a little bit when we got these results. I had a meeting with our data science team and with Matt, and., we all are sitting down and I’m like, lay it on me tell me what the results were and they were a little bit disappointed and they said, it’s kind of we’re not seeing, a big thing from Impact perspective or a data perspective, like, it’s just not that exciting.[00:17:15] And I said, oh, no, actually, this is very exciting because there were a number of factors. I think that really made this a really impactful report. 1st was just having some new original research on this topic. That is maybe the hot topic of the decade.[00:17:31] I think was really exciting. So it was like, listen, we know that people are very interested in this. We know that this is the question that they are asking, especially engineers and engineering leaders, the people who we serve from a business standpoint. They want to know is gen AI actually helping my developers be more productive.[00:17:48] And we have like some. Things that we can show around that. And then also the fact that we were able to then bring a little bit of a spiky and contrarian point of view about this because a lot of the research that’s been published already is either survey based. So, a lot of developers reporting whether or not they feel more productive.[00:18:11] Which is data as well, but, this is we’re bringing some quantitative data to bear or some of the other data was published by the. AI tools themselves, so you have to take that with a grain of salt. So, we came in[00:18:27] with this sort of interesting and different point of view. And that really, really took off for folks. And we found that some people were surprised. We found a lot of developers and engineers like you, Genevieve, who are not who said, I have been saying this all along. And this feels very validating because I think there is some anxiety among engineers that, Hey, like leadership just thinks that can be replaced.[00:18:50] But it really kicked off a really big conversation in the industry where we just said, Hey, you know, there’s a little bit of a hype cycle right now. We don’t know for sure. , we have results from one sample. There’s no big claims that we can make about the efficacy in the long run.[00:19:06] And things change very quickly. Gen AI is improving all the time, but. We do have some data points that we think are interesting to share and it really took off and it was great for us from a business perspective. It really helped take the work that we do into that last mile. And it helped make the work that we do feel very tangible and accessible for folks.[00:19:29] Dr Genevieve Hayes: So it sounds like, rather than taking a whole bunch of statistics and graphs, which would have been the output of Matt’s work. You translated those statistics and graphs into a narrative that could be understood by a person who wasn’t a data scientist or wasn’t a data analyst. Is that right?[00:19:49] Lauren Lang: Yes, we did. And our audience is primarily engineering leaders, engineering leaders are not data scientists, but they’re technical. So we identified three main takeaways. And we presented that we shared a little bit about our methodology.[00:20:03] And we shared essentially Some thoughts about what does this mean, what is the larger significance of what we found? What does this mean for you as an engineering leader does this mean that we think that you should stop adopting AI?[00:20:17] Does it mean that, right?, you should be more controlling of how your engineers are experimenting with AI. And, we don’t believe that’s the case at all. But it allowed us to sort of share some of our perspective about, how you build effective engineering organizations and what role we think I may have to play in that.[00:20:35] And, that is the larger story where data becomes very interesting because there’s sharing the data and then they’re sharing the so what around the data. So, what does this mean for me as an engineering leader? And so we really tried to bring those 2 elements together in the report.[00:20:51] Dr Genevieve Hayes: How was this report ultimately received by the audience?[00:20:55] Lauren Lang: Very well. We issued a press release around it. And I think we were picked up globally by somewhere between 50 and 75 media outlets, which. For a small engineering analytics platform, I’m pretty happy about that. It was in some engineering forums, it really became a big topic of discussion. We went sort of medium level viral. And it felt really good. It’s like, this is a really interesting topic. We accept that it’s an interesting topic.[00:21:22] We think that we have something that is very interesting to add to the conversation. So, yeah, it was good and some folks to it was great, you know, because engineering leaders are naturally skeptical. This is 1 of the most fun parts about marketing to engineering leaders that engineering leaders hate marketing.[00:21:38] So we got a few emails of folks who are like, tell us more about your methodology. And they really sort of wanted to, see behind the scenes and really, really dig in. And, that is par for the course. And we would expect nothing less[00:21:51] It was a really positive impact. I’m really glad we did it.[00:21:53] Dr Genevieve Hayes: So with all that in mind, I’d like to ask this of each of you. What is the single most important change our listeners could make tomorrow to accelerate their data science impact and results?[00:22:05] Dr Matt Hoffman: I. am very fortunate to have Lauren as an editor even when we collaborate on writing, an article I think having someone who can help you clarify and simplify your story is so important. You really do want to edit and bounce back and forth and try to distill down the most important bits of what you’re doing.[00:22:28] I tend to want to share, like, Everything, all of the details, all the gritty stuff, the exact perfect chart and it’s like, let’s simplify, simplify, simplify. And part of that conversation is also, who’s going to be receiving this? And what’s their persona? At what level are we going to explain this work?[00:22:47] Are they going to be familiar with, the methodology that we’re using? Or do we need to explain that too? So, how do we write everything at the most appropriate level and understand the life cycle of? This report that we’re doing. So having an editor would be my big one and understanding your audience would be the other.[00:23:06] Lauren Lang: I absolutely agree with everything Matt said. I think that the more that you make Sharing the results of your research, a team effort and a team sport, the more you’re likely going to succeed at it. But I think probably, and I’ll just come at it from, more of a technical perspective.[00:23:23] When you are presenting information, 1 of the things that could be very helpful is to present it at various levels of detail. So, making sure that you are presenting key takeaways or abstracts at 1 level and then. People can always double click into things and dive deeper and, you can include appendices or include links to , more of the detailed research.[00:23:47] But I think sort of having these executive summaries and really sort of being able to come at things from a very high level Can help sort of get that initial interest so that people understand quickly. what did the research find? What is the impact? And what is the context that this research was performed in?[00:24:06] Where is the business value, so, being able to connect the dots for your audience in terms of not only did we find this, but here’s what it means. And that thing that it means is actually very impactful to you and the job that you are trying to accomplish .[00:24:19] Dr Genevieve Hayes: So for listeners who want to get in contact with each of you, what can they do?[00:24:23] Lauren Lang: I live on LinkedIn. So they can look me up on LinkedIn. I think my little handle there is ask Lauren Lang.[00:24:31] Dr Matt Hoffman: Likewise, I don’t know what my LinkedIn handle is, but I’m on there. That would be the easiest way to get a hold of me on that.[00:24:39] Lauren Lang: You obviously need to spend more time on LinkedIn than Matt.[00:24:42] Dr Genevieve Hayes: Yes. And there you have it. Another value packed episode to help turn your data skills into serious clout, cash, and career freedom. And if you enjoyed this episode, why not make it a double? Next week, catch Lauren and Matt’s Value Boost, a five minute episode where they share one powerful tip for getting real results real fast.[00:25:08] Make sure you’re subscribed so you don’t miss it. Thanks for joining me today, Lauren and Matt.[00:25:12] Lauren Lang: Thank you so much for having us.[00:25:14] Dr Matt Hoffman: Thank you. It was really lovely.[00:25:16] Dr Genevieve Hayes: And for those in the audience, thanks for listening. I’m Dr. Genevieve Hayes, and this has been value driven data science. The post Episode 56: How a Data Scientist and a Content Expert Turned Disappointing Results into Viral Research first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

In this episode I'll show you what it takes to land data analyst jobs! I'll provide in-depth insights and tips for six data analyst positions with salaries ranging from $35K to $200K-- and why should you apply even if you don't meet all the requirements.

Moderne KI-Tools sind Meister im Strukturieren, Analysieren und Organisieren. Aber wie bringt man den KI-Datenexperten in den Alltag? Gregor und Fritz nehmen sich Anwendungsfälle aus Privatleben und Berufswelt vor und erforschen die Fähigkeiten von einfachen Sprachmodellen bis hin zu spezialisierter Datensoftware in großen Unternehmen. Über die Hosts: Gregor Schmalzried ist freier Tech-Journalist, Speaker und Berater, u.a. beim Bayerischen Rundfunk. Fritz Espenlaub ist freier Journalist und Ökonom. Er arbeitet unter anderem für den Bayerischen Rundfunk und das Tech-Magazin 1E9. In dieser Folge: 0:00 Der beste KI-Use-Case aller Zeiten 2:50 Einfache Sprachmodelle 11:00 "Reasoning”-Sprachmodelle 23:30 Data Analyst und Claude Artifacts 30:50 SAP und Palantir 35:30 Was haben wir mit KI gemacht? Links: Bilder bearbeiten mit Google Gemini - Link zur "Was haben wir mit KI gemacht?”-Rubrik https://www.br.de/nachrichten/netzwelt/gemini-flash-viel-wirbel-um-googles-neue-bild-ki,UfjG4Pn Podcast-Tipp: 1 plus 1 - Freundschaft auf Zeit https://www.ardaudiothek.de/sendung/1-plus-1-freundschaft-auf-zeit/10710985/ o1 findet Fehler in Pfannenwender-Studie https://x.com/emollick/status/1868329599438037491 ChatGPT o1: Die KI grübelt jetzt https://www.br.de/nachrichten/netzwelt/chatgpt-o1-die-ki-gruebelt-jetzt,UOF5SB1 "Deep Research": Welche KI recherchiert am besten? https://www.br.de/nachrichten/netzwelt/deep-research-welche-ki-recherchiert-am-besten,Udj4dWQ SAP übertrifft eigene Ziele - und will weiter wachsen https://www.tagesschau.de/inland/regional/badenwuerttemberg/swr-ki-cloud-konzernumbau-sap-uebertrifft-eigene-ziele-102.html Palantir: Schafft die Polizei den gläsernen Bürger? https://www.tagesschau.de/investigativ/br-recherche/polizei-analyse-software-palantir-101.html Erwähnte Chatbots https://chatgpt.com/ https://claude.ai/ https://chat.mistral.ai/chat Redaktion und Mitarbeit: David Beck, Cristina Cletiu, Chris Eckardt, Fritz Espenlaub, Elisa Harlan, Franziska Hübl, Marie Kilg, Mark Kleber, Gudrun Riedl, Christian Schiffer, Gregor Schmalzried Kontakt: Wir freuen uns über Fragen und Kommentare an kipodcast@br.de. Unterstützt uns: Wenn euch dieser Podcast gefällt, freuen wir uns über eine Bewertung auf eurer liebsten Podcast-Plattform. Abonniert den KI-Podcast in der ARD Audiothek oder wo immer ihr eure Podcasts hört, um keine Episode zu verpassen. Und empfehlt uns gerne weiter!

In this hour of Ready, Set, Bet!, hosts Matt Brown and Geoff Schwartz are joined by Justin Perri, Data Analyst, Shot Quality, as they give a betting preview of the remaining college basketball games for today. Also in the show, the hosts give betting updates on the action going on in college basketball and dive into the odds to make the Final Four.See omnystudio.com/listener for privacy information.

In this episode, I uncover the nine biggest LIES about landing a data job. Maybe what's stopping you from pursuing a data career is just a big lie.No College Degree As A Data Analyst YT Playlist: https://www.youtube.com/playlist?list=PLo0oTKi2fPNjHi6iXT3Pu68kUmiT-xDWsDon't Learn Python as a Data Analyst (Learn This Instead):https://www.youtube.com/watch?v=VVhURHXMSlA

In Agents in Production [Podcast Limited Series] - Episode Four, Donné Stevenson and Paul van der Boor break down the deployment of a Token Data Analyst agent at Prosus—why, how, and what worked. They discuss the challenges of productionizing the agent, from architecture to mitigating LLM overconfidence, key design choices, the role of pre-checks for clarity, and why they opted for simpler text-based processes over complex recursive methods.Guest speakers: Paul van der Boor - VP AI at Prosus GroupDonne Stevenson - Machine Learning Engineer at Prosus GroupHost: Demetrios Brinkmann - Founder of MLOps Community~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity][https://x.com/mlopscommunity] or LinkedIn [https://go.mlops.community/linkedin] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkm

I tested DeepSeek-- an emerging AI platform that makes ChatGPT look ancient! I asked it to outline a comprehensive roadmap for becoming a data analyst. What it said scared me (Spoiler: it basically copied my SPN Method)!Listen to NEXT: My interview with StatQuest!https://www.youtube.com/watch?v=nqtQUg4mZ9I

Our host, Gareth McGlynn, sits down with Andrew Pitcher, Data Analyst and Integrations Manager at Bartlett Cock, for the first of a two-part series on the power of data in construction.Discussion Points:Andrew's background in Construction Science from Texas A&M UniversityHis Master of Science in Data AnalyticsHow to extract, organize, and display data effectivelyThe key data points every general contractor should be trackingExtracting data from Excel, text, images, and videosBuilding a scalable data and tech stack for long-term successPart 2 coming soon!

Meet @SundasKhalid: High school dropout, immigrant, and now a powerhouse in data at Google! She shares pivotal tips for breaking into data, invaluable financial literacy insights, and how she champions salary negotiation by helping others secure higher pay.Special offer for Data Career Podcast viewers:Use the code AVERY 20 to avail of HUGE discounts from Sundas' Negotiation Masterclass: https://sklab.io/p/salaryWhat's Sundas' REAL 6-Figure Tech Salary After 10 Years? https://youtu.be/EjJm_rcUOxY?si=YTOtXT_fLyqWzU1I

Cocoa arrival numbers and weather tell of a promising season ahead Major cocoa and chocolate producers are concerned about how cocoa prices can affect their financial performance and projections for the year Q4 2024 grind showed a decline in demand: Will Q1 2025 grind help with the trajectory? McKeany-Flavell's 2025 Spring Market Seminar: Industry Trends & Consumption Live online event! Wednesday, April 23, 2025 Registration is now open! Host: Eric Thornton, Senior Commodity Advisor Expert: Marilyn Adutwum, Data Analyst

How do you make data analytics fun and engaging? In this episode, I chat with YouTube sensation Thu Vu. We discuss Python's growing significance, trends in the data job market, plus get a sneak peek into her new initiative, Python for AI Projects.

Tu peux soutenir sur le podcast en mettant 5⭐️ sur Apple Podcasts ou Spotify !Romain est le Head of Product Design & User Research du journal Le Monde.Après le lycée, Romain fait un DUT Services et Réseaux de Communication un peu par hasard. Autour de lui, il n'y a que des personnes qui ont déjà du design ou des sites internet. Il ne se sent pas forcément à sa place. En deuxième année, il découvre Flash et s'y intéresse fortement. Mais c'est le développement, plus que le design qui l'intéresse alors dans cette technologie. Il passe alors un entretien de développeur Flash en agence. Mais c'est un poste de designer qui lui est proposé. Poste qu'il accepte. Il doit alors faire de l'animation sur Flash.Il rejoint ensuite Les Gobelins à Annecy, mais postule dans le parcours de développeur. Mais une fois accepté, il fait en sorte d'être de suivre le parcours designer. Il suit ses études en alternance, en travaillant au service Communication & Marketing de Salomon. C'est là où il découvre un nouveau monde entre le design et le numérique : l'UX Design. Il est également formé au fait que son design doit être objectivé et avoir un impact sur le business.A la suite de ses études, Romain part en agence, à Paris. Il imagine des campagnes publicitaires numériques et les décline ensuite en wireframe.Ensuite, il rejoint la start-up Keecker qui veut créer un robot multifonctions pour la maison. Romain s'occupe alors de la création de l'interface de contrôle depuis un téléphone. Il revient sur ce qu'il a réussi à mettre en place et ce qu'il aurait aimé faire autrement.En 2017, lorsque le robot sort, Romain rejoint Deezer : la création d'un object physique est long et il est le seul designer. Il a alors l'opportunité de travailler en équipe sur un produit qui évolue vite. Il design alors l'expérience cœur de l'application (la navigation, l'application desktop ou Xbox). Après un an, il devient Lead et passe du design au management, d'une équipe de 6 personnes.Au bout de 2 années, Romain se fait débaucher par le groupe Accor. Un aventure de courte durée : le design est une partie infime du travail de Romain, il doit majoritairement faire de la politique, ce qui ne lui plait pas forcément. Au même moment, il découvre une offre pour rejoindre le journal Le Monde…… qu'il rejoint en tant que Head of Product Design. Il arrive dans une équipe bien établie, mais qui va devoir grossir. Son équipe passe alors de 4 à 10 personnes et composées de Product Designers, User Researchers, une Visual Designer et une Data Analyst.Dans cet épisode, Romain nous explique l'organisation de son équipe, ses méthodes de travail, ses rituels, ce qu'ils ont mis en place pour garder de la cohérence, etc.On aborde aussi la relation entre l'équipe Product Design et les journalistes : l'évangélisation de la User Research grâce à la mise en place d'un CMS interne, la mise en place de designs en fonction des besoins journalistiques - comme le soir des élections -, la réflexion sur de nouveaux concepts à destination des journalistes…On discute également de la difficile équation entre améliorer l'expérience utilisateur et favoriser le business model du journal centré autour de la publicité et de l'abonnement.Romain revient également sur ce qui est mis en place au Monde depuis 2 ans pour améliorer l'accessibilité et les résultats obtenus dans le temps.Enfin, on parle de ce qui arrive pour la suite du Monde et pour l'équipe de Romain.Les ressources de l'épisodeLe MondeThe Culture Code, Daniel CoyleHow to Win the Premier League, Ian GrahamLes autres épisode de Design Journeys#41 David Duhamel, Lead UX Designer @ Radio France#71 Nicolas Morand, Head of Design & Innovation @ Lunii#85 Rémi Guyot, Co-fondateur @ Discovery DisciplineCase Study#2 Discovery Discipline avec Rémi Guyot & Tristan Charvillat Pour contacter Romain LinkedIn

Help us become the #1 Data Podcast by leaving a rating & review! We are 67 reviews away! Starting a career in data can be tough, but it doesn't have to be a guessing game. Learn how to combine skills, projects, and connections to create real opportunities.

In this story, we follow a data analyst who turns his knack for numbers and love of sports into a profitable business. We explore how he developed fantasy sports prediction algorithms that helped him reach his first $1,000 in earnings. Side Hustle School features a new episode EVERY DAY, featuring detailed case studies of people who earn extra money without quitting their job. This year, the show includes free guided lessons and listener Q&A several days each week. Show notes: SideHustleSchool.com Email: team@sidehustleschool.com Be on the show: SideHustleSchool.com/questions Connect on Instagram: @193countries Visit Chris's main site: ChrisGuillebeau.com Read A Year of Mental Health: yearofmentalhealth.substack.com If you're enjoying the show, please pass it along! It's free and has been published every single day since January 1, 2017. We're also very grateful for your five-star ratings—it shows that people are listening and looking forward to new episodes.

Best things about the cold, DeRush-Hour Headlines ft. literacy and FRIDAY THE 13TH - IS IT REALLY UNLUCKY? Jason speaks with Rickard Dahlo, the Business Systems and Data Analyst for Hennepin Emergency Medical Services (HEMS)

Rickard Dahlo, the Business Systems and Data Analyst for Hennepin Emergency Medical Services (HEMS) joins Jason to chat about Friday the 13th and the science behind luck.

Meet Nirav Shah and Peter NicholsonNirav Shah has been working in the ERP space for the past 20 years. During those years, he has worked primarily to help customers of all various sizes and industries implement advanced warehousing and manufacturing processes. He also has an MBA in Accounting which helps him work closely with finance teams to melt proper finance rules and guidelines to manufacturing/distribution processes. Recently, he started a VAR to help companies realize the benefits of cloud ERP solutions specifically in the B2B manufacturing and distribution industries.Peter Nicholson has long been fascinated by the synergy of data and technology, yet found himself stuck in sales roles. Since 2019, he has been with ITW, gaining knowledge in how a business operates in various departments such as sales, finance, warehousing, and factory operations. In 2024, he pivoted his career into IT, now serving as the Division Systems and Data Analyst across eight countries. It's an exciting fusion of his sales background with his passion for data and technology, allowing him to drive impactful change within the organization.LinksAI at Work Is Here. Now Comes the Hard PartConnect with Nirav and Peter!Nirav's LinkedInnirav.shah@adcirruserp.comPeter's LinkedInpetenicholson.co.ukTheir PodcastThe ABCs of ERP & BeyondThe ABCs of ERP & Beyond YouTubeHighlights00:00 Introduction to the Manufacturing Quiz Show00:15 Question 1: Dow Jones Industrial Average Addition01:00 Question 2: Rock Band Founder and Polaroid02:08 Question 3: Manufacturer's Headquarters Relocation03:14 Introduction to ERP Experts04:42 Peter's Insights on Sales and IT07:34 The Future of AI in Manufacturing13:16 Benefits of ERP Systems Over QuickBooks19:04 When to Consider Implementing an ERP System22:09 Identifying Business Process Bottlenecks23:09 Evaluating the Role of Technology in Business Processes23:56 Steps to Streamline Business Processes24:59 Considering ERP Upgrades and Cloud Solutions25:45 The Benefits of Cloud-Based ERP Systems27:51 Interesting Facts and Insights29:05 AI Skills in the Job Market31:06 Book Recommendations from Bill Gates32:36 Rescue Implementation Success Story33:48 Mind-Blowing Space and Tree Facts37:15 Contact Information and Closing RemarksConnect with the broads!Connect with Lori on LinkedIn and visit www.keystoneclick.com for your strategic digital marketing needs! Connect with Kris on LinkedIn and visit www.genalpha.com for OEM and aftermarket digital...

Help us become the #1 Data Podcast by leaving a rating & review! We are 67 reviews away! Steven Tran went from tech support to analytics pro in just three months, and he's spilling the tea on how he made it happen.

Frelund talks Cardinals, Kyler Murray, the defense, and where they stand in the NFC West.

In this episode, Mary Sullivan, co-founder of Sweet but Fearless, talks with IIana Wechsler, Founder of Teach Traffic and Pay Per Click Marketing expert about her transition from the corporate world to forging her path as an entrepreneur. She dives into her journey of resilience and grit, sharing hard-won insights on creating opportunities where luck can find you and developing the courage to overcome self-doubt. From building multiple income streams to pushing through fears and embracing new challenges, she shares her blueprint for success and offers valuable tips. Ilana Wechsler is a Pay Per Click (PPC) Marketing Professional with over 15 years' experience in Information Technology working for many large international corporations. She started her career as a Data Analyst and transitioned to becoming a full-fledged PPC expert. She has worked at many of the global financial institutions but switched when she finally gave in to her passion for PPC, IT, and entrepreneurship. If you found this episode inspiring, please subscribe, like, and leave a comment.   MORE ABOUT ILANA WECHSLER LinkedIn: Ilana Wechsler Website: Teach Traffic YouTube: Teach TrafficInstagram: teachtraffic1   ABOUT SWEET BUT FEARLESS: Website - Sweet but Fearless LinkedIn - Sweet but Fearless

Dan spoke to Football Data Analyst, Jimi (@borbely_imre) to find out what the underlying numbers behind Liverpool target Martin Zubimendi tell us about the Spaniard.Support this show http://supporter.acast.com/redmentv. Hosted on Acast. See acast.com/privacy for more information.