As cloud adoption accelerates, not all analytics workloads are heading in the same direction. This blog explores three strategic options for data and IT leaders. Published at: https://www.eckerson.com/articles/are-you-cloud-bound-the-case-for-migration-repatriation-or-keeping-your-analytics-projects-on-premises

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. 💌 Join 10k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩‍💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com/interviewsimulator ⌚ TIMESTAMPS - How to Become a Data Analyst w/o Applying 1000 Jobs 00:00 - Introduction 02:18 - Steps to effective job searching 05:06 - The 2-Hour Job Search 10:54 - Asking strangers for advice vs. applying online 18:35 - Earned referrals vs. online referrals 20:24 - PremiumDataJobs.com and DataFairy.io 24:37 - Effective outreach messages 27:18 - The Role of AI in Job Searching 28:16 - The 6-Point Email 34:00 - Ed Bernier's "Three-Hour Rule" 38:57 - Advice for job seekers

🔗 CONNECT WITH STEVE 🤝 LinkedIn: https://www.linkedin.com/in/daltonsteve/ 📸 Instagram: https://www.instagram.com/dalton_steve/ 🎵 X: https://x.com/dalton_steve 💻 Website: https://2hourjobsearch.com/

🔗 CONNECT WITH AVERY 🎥 YouTube Channel: https://www.youtube.com/@averysmith 🤝 LinkedIn: https://www.linkedin.com/in/averyjsmith/ 📸 Instagram: https://instagram.com/datacareerjumpstart 🎵 TikTok: https://www.tiktok.com/@verydata 💻 Website: https://www.datacareerjumpstart.com/ Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!

To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more

If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong.

👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa

Investing in these especially turbulent times is intrepid. No better person to navigate the ups and downs in global stock, bond, crypto and other asset markets than UBS’s CIO, Mark Haefele. Mark discusses his new book, in which he considers the broad global trends – the 5 Ds – investors should embrace. And he offers a few investment nuggets along the way. For Mark Haefele's book, The New Rules of Investing, click here Guest: Mark Haefele – Chief Investment Officer, UBS Global Wealth Management Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, and Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X', BlueSky or LinkedIn @MarkZandi, Cris deRitis on LinkedIn, and Marisa DiNatale on LinkedIn

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Collaboration is key in analytics, but working with product, sales, and developer teams can often feel like navigating different worlds. In this episode, Eric Sims—Sr. Data Scientist at NRG Energy—joins us to share proven strategies for building successful cross-functional relationships. We'll also dive into some of Eric's most interesting projects in manufacturing, utilities, and finance, highlighting how analytics drives value in these industries. Whether it's aligning with product priorities, supporting sales goals, or partnering with developers, Eric brings practical advice to help you thrive in cross-functional teams. If you're an analyst or data professional looking to bridge gaps, improve communication, and deliver projects that matter, this episode is for you. What You'll Learn: How to collaborate effectively with product, sales, and developer teams. Key challenges and opportunities for analytics in manufacturing, utilities, and finance. Real-world examples of projects that delivered cross-functional success. Tips for communicating insights and aligning with business priorities. Strategies to set yourself up for success as an analytics professional.   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

Jason's co-author, Barry Green, joins this next episode as they discuss the release of the second edition of "Data Means Business" on 25th March.  Returning for his third conversation on the podcast, Barry shares updates on the evolving landscape of data and AI, the impact of generative AI, and the importance of business capabilities. They delve into the changes since the first edition, including the role of the Chief Data Officer and the significance of adaptability in today's fast-paced world. Tune in to hear their thoughts on driving transformational change and delivering value with data & AI. The second edition of Data Means Business will be out on 25th March and will be available on Amazon. *****    Cynozure is a leading data, analytics and AI company that helps organisations to reach their data potential. It works with clients on data and AI strategy, data management, data architecture and engineering, analytics and AI, data culture and literacy, and data leadership. The company was named one of The Sunday Times' fastest-growing private companies in both 2022 and 2023 and recognised as The Best Place to Work in Data by DataIQ in 2023 and 2024. Cynozure is a certified B Corporation. 

A challenge I frequently hear about from subscribers to my insights mailing list is how to design B2B data products for multiple user types with differing needs. From dashboards to custom apps and commercial analytics / AI products, data product teams often struggle to create a single solution that meets the diverse needs of technical and business users in B2B settings. If you're encountering this issue, you're not alone!

In this episode, I share my advice for tackling this challenge including the gift of saying "no.” What are the patterns you should be looking out for in your customer research? How can you choose what to focus on with limited resources? What are the design choices you should avoid when trying to build these products? I’m hoping by the end of this episode, you’ll have some strategies to help reduce the size of this challenge—particularly if you lack a dedicated UX team to help you sort through your various user/stakeholder demands. 

Highlights/ Skip to 

The importance of proper user research and clustering “jobs to be done” around business importance vs. task frequency—ignoring the rest until your solution can show measurable value  (4:29) What “level” of skill to design for, and why “as simple as possible” isn’t what I generally recommend (13:44) When it may be advantageous to use role or feature-based permissions to hide/show/change certain aspects, UI elements, or features  (19:50) Leveraging AI and LLMs in-product to allow learning about the user and progressive disclosure and customization of UIs (26:44) Leveraging the “old” solution of rapid prototyping—which is now faster than ever with AI, and can accelerate learning (capturing user feedback) (31:14) 5 things I do not recommend doing when trying to satisfy multiple user types in your b2b AI or analytics product (34:14)

Quotes from Today’s Episode

If you're not talking to your users and stakeholders sufficiently, you're going to have a really tough time building a successful data product for one user – let alone for multiple personas. Listen for repeating patterns in what your users are trying to achieve (tasks they are doing). Focus on the jobs and tasks they do most frequently or the ones that bring the most value to their business. Forget about the rest until you've proven that your solution delivers real value for those core needs. It's more about understanding the problems and needs, not just the solutions. The solutions tend to be easier to design when the problem space is well understood. Users often suggest solutions, but it's our job to focus on the core problem we're trying to solve; simply entering in any inbound requests verbatim into JIRA and then “eating away” at the list is not usually a reliable strategy. (5:52) I generally recommend not going for “easy as possible” at the cost of shallow value. Instead, you’re going to want to design for some “mid-level” ability, understanding that this may make early user experiences with the product more difficult. Why? Oversimplification can mislead because data is complex, problems are multivariate, and data isn't always ideal. There are also “n” number of “not-first” impressions users will have with your product. This also means there is only one “first impression” they have. As such, the idea conceptually is to design an amazing experience for the “n” experiences, but not to the point that users never realize value and give up on the product.  While I'd prefer no friction, technical products sometimes will have to have a little friction up front however, don't use this as an excuse for poor design. This is hard to get right, even when you have design resources, and it’s why UX design matters as thinking this through ends up determining, in part, whether users obtain the promise of value you made to them. (14:21) As an alternative to rigid role and feature-based permissions in B2B data products, you might consider leveraging AI and / or LLMs in your UI as a means of simplifying and customizing the UI to particular users. This approach allows users to potentially interrogate the product about the UI, customize the UI, and even learn over time about the user’s questions (jobs to be done) such that becomes organically customized over time to their needs. This is in contrast to the rigid buckets that role and permission-based customization present. However, as discussed in my previous episode (164 - “The Hidden UX Taxes that AI and LLM Features Impose on B2B Customers Without Your Knowledge”)  designing effective AI features and capabilities can also make things worse due to the probabilistic nature of the responses GenAI produces. As such, this approach may benefit from a UX designer or researcher familiar with designing data products. Understanding what “quality” means to the user, and how to measure it, is especially critical if you’re going to leverage AI and LLMs to make the product UX better. (20:13) The old solution of rapid prototyping is even more valuable now—because it’s possible to prototype even faster. However, prototyping is not just about learning if your solution is on track. Whether you use AI or pencil and paper, prototyping early in the product development process should be framed as a “prop to get users talking.” In other words, it is a prop to facilitate problem and need clarity—not solution clarity. Its purpose is to spark conversation and determine if you're solving the right problem. As you iterate, your need to continually validate the problem should shrink, which will present itself in the form of consistent feedback you hear from end users. This is the point where you know you can focus on the design of the solution. Innovation happens when we learn; so the goal is to increase your learning velocity. (31:35) Have you ever been caught in the trap of prioritizing feature requests based on volume? I get it. It's tempting to give the people what they think they want. For example, imagine ten users clamoring for control over specific parameters in your machine learning forecasting model. You could give them that control, thinking you're solving the problem because, hey, that's what they asked for! But did you stop to ask why they want that control? The reasons behind those requests could be wildly different. By simply handing over the keys to all the model parameters, you might be creating a whole new set of problems. Users now face a "usability tax," trying to figure out which parameters to lock and which to let float. The key takeaway? Focus on addressing the frequency that the same problems are occurring across your users, not just the frequency a given tactic or “solution” method (i.e. “model” or “dashboard” or “feature”) appears in a stakeholder or user request. Remember, problems are often disguised as solutions. We've got to dig deeper and uncover the real needs, not just address the symptoms. (36:19)

UVA School of Data Science graduates pursue many career paths, including government, health care, technology, retail, and... finance. In this episode, we hear from two UVA data science alumni who put their data science degrees to work every day in their roles at Octus, a financial services company that uses data to provide insights to its clients in banking and legal services.

They discuss the integration of AI into various industries, the challenges of information overload, and the role of human expertise.We welcome Charu Rawat and Yihnew Eshetu, who earned their M.S. in Data Science degrees from UVA in 2019 and 2021, respectively, and Ben Rogers, vice president of AI and advanced analytics at Permira. 

Firas Saleh, director of product management at Moody's, joins the Inside Economics team to discuss the increasing risk of wildfires and floods. He highlights the growing frequency and intensity of natural disasters and the significant economic losses they cause. The conversation then shifts to the insurance industry, focusing on how rising insurance premiums affect individual property owners and real estate markets. Although markets will adapt to these evolving risks, the transition may be challenging. For Cris and Firas's research on flood risk click here, for their research on wildfire risk click here To learn more about Moody's wildfire risk modeling click here Guest: Firas Saleh – Director of Product Management, Moody's Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, and Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X', BlueSky or LinkedIn @MarkZandi, Cris deRitis on LinkedIn, and Marisa DiNatale on LinkedIn

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

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. 💌 Join 10k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩‍💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com/interviewsimulator No College Degree As A Data Analyst Playlist: https://youtu.be/mSWtnjq4LRE?si=FlfChqSxIPBXc_Lb ⌚ TIMESTAMPS Data Analyst Jobs: How Much $$$ Could You ACTUALLY Make??? 00:00 - Introduction 00:21 - Data Analyst Job #1: Data Specialist ($35k) 04:00 - Data Analyst Job #2: Business and Data Analyst ($55k) 07:48 - Data Analyst Job #3: Data Visualization Analyst ($75k) 10:21 - Data Analyst Job #4: Senior Financial Analyst ($90k) 13:04 - Data Analyst Job #5: Senior Investment Operations Data Analyst ($125k) 14:35 - Data Analyst Job #6: Business Intelligence Engineer ($107k to $189k) 🔗 CONNECT WITH AVERY 🎥 YouTube Channel: https://www.youtube.com/@averysmith 🤝 LinkedIn: https://www.linkedin.com/in/averyjsmith/ 📸 Instagram: https://instagram.com/datacareerjumpstart 🎵 TikTok: https://www.tiktok.com/@verydata 💻 Website: https://www.datacareerjumpstart.com/ Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!

To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more

If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong.

👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa

In the retail industry, data science is not just about crunching numbers—it's about driving business impact through well-designed experiments. A-B testing in a physical store setting presents unique challenges that require careful planning and execution. How do you balance the need for statistical rigor with the practicalities of store operations? What role does data science play in ensuring that test results lead to actionable insights?  Philipp Paraguya is the Chapter Lead for Data Science at Aldi DX. Previously, Philipp studied applied mathematics and computer science and has worked as a BI and advanced analytics consultant in various industries and projects since graduating. Due to his background as a software developer, he has a strong connection to classic software engineering and the sensible use of data science solutions. In the episode, Adel and Philipp explore the intricacies of A-B testing in retail, the challenges of running experiments in brick-and-mortar settings, aligning stakeholders for successful experimentation, the evolving role of data scientists, the impact of genAI on data workflows, and much more. Links Mentioned in the Show: Aldi DXConnect with PhilippCourse: Customer Analytics and A/B Testing in PythonRelated Episode: Can You Use AI-Driven Pricing Ethically? with Jose Mendoza, Academic Director & Clinical Associate Professor at NYUSign up to attend RADAR: Skills Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

The Inside Economics crew is joined by Matt Colyar to discuss February’s CPI report and a rapidly changing U.S. economic environment. Primarily, the conversation focuses on tariffs and the on-again, off-again chaos coming out of D.C. The group also discusses investors and U.S. trade partners’ increasingly evident fatigue and whether orthodox macroeconomic principals will eventually re-emerge as a guidepost for policymaking.  Guest: Matt Colyar – Assistant Director, Moody's Analytics Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, and Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X', BlueSky or LinkedIn @MarkZandi, Cris deRitis on LinkedIn, and Marisa DiNatale on LinkedIn

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

How do you make sense of massive, interconnected datasets across time? In this episode of Data Unchained, we sit down with Ben Steer, Founder and CTO of Pometry, to explore the power of temporal graph analytics, a revolutionary approach called, "Big Data, Small Box," and how data can help prevent fraud and black market trading.

DataUnchained #EnterpriseData #CIO #CTO #CISO #DataStrategy #DigitalTransformation #BigData #CloudComputing #GraphAnalytics #AI #MachineLearning #DataEngineering #DataSecurity #BusinessIntelligence #TechLeadership #TechInnovation #AIinBusiness #ITStrategy #CyberSecurity #HPC #CloudCostOptimization #DataScience #Podcast #TechPodcast #BusinessPodcast #DataPodcast #Innovation

Cyberpunk by jiglr | https://soundcloud.com/jiglrmusic Music promoted by https://www.free-stock-music.com Creative Commons Attribution 3.0 Unported License https://creativecommons.org/licenses/by/3.0/deed.en_US Hosted on Acast. See acast.com/privacy for more information.

If you're interested in AI but not sure if you're leveraging it to its full potential, this show is for you. Michael Tiffany, Founder and CEO of Fulcra Dynamics, is here to help you get your head around AI, its best use cases today, and where things are heading in the future. You'll leave the show with a better understanding of AI, trends to keep an eye on, and how you can take full advantage of exciting new technology in your work and your personal life. What You'll Learn: AI use cases you should have on your radar at work How to leverage AI effectively to augment your personal life Where AI is headed and how you can make sure you're not falling behind   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

Over the past 1000 days, I've interviewed some of the brightest minds in the data world. And in today’s episode, you’ll hear genius career advice from 6 of my favorite female data analysts. They’ll teach you what it’s like working in data, and help you learn what it takes to actually land a data job. 💌 Join 10k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩‍💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com/interviewsimulator ⌚ TIMESTAMPS 00:00 - Introduction 00:45 - Sundas Khalid: Keep going, no matter what. 05:26 - Cole Knaflic: It's less about analysis and more about presentation. 11:38 - Rachael Finch: Always be networking. 23:22 - Jess Ramos: Be proactive in your job search and networking. 28:44 - Hana M.K.: Avoid Shiny Object Syndrome! 32:41 - Erin Shina: The importance of having projects. Check out the full episodes from this compilation! 1. How This High School Drop Out Became a $500k Data Analyst (Sundas Khalid) - https://datacareerpodcast.com/episode/148-how-this-high-school-drop-out-became-a-500k-data-analyst-sundas-khalid 2. Meet The Woman Who Changed Data Storytelling Forever (Cole Knaflic) - https://datacareerpodcast.com/episode/142-meet-the-woman-who-changed-data-storytelling-forever-cole-knafflic 3. How She Landed a Business Intelligence Analyst Job in Less than 100 Days (w/ Rachael Finch) - https://datacareerpodcast.com/episode/125-how-she-landed-a-business-intelligence-analyst-job-in-less-than-100-days-w-rachael-finch 4. Navigating Your Data Career Journey w/ Jess Ramos - https://datacareerpodcast.com/episode/49-navigating-your-data-career-journey-w-jess-ramos 5. Presenting for Data Analysts w/ Hana M.K. - https://datacareerpodcast.com/episode/84-presenting-for-data-analysts-w-hana-mk 6. From Music to Spreadsheets: Erin Shina’s 90-Day Transformation from Music to Financial Data Analyst - https://datacareerpodcast.com/episode/65-from-music-sheet-to-spreadsheets-erin-shinas-90-day-transformation-from-music-to-financial-data-analyst 🔗 CONNECT WITH AVERY 🎥 YouTube Channel: https://www.youtube.com/@averysmith 🤝 LinkedIn: https://www.linkedin.com/in/averyjsmith/ 📸 Instagram: https://instagram.com/datacareerjumpstart 🎵 TikTok: https://www.tiktok.com/@verydata 💻 Website: https://www.datacareerjumpstart.com/ Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!

To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more

If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong.

👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa

This guide provides a step-by-step framework to assess vendors, align priorities, and make informed decisions about enterprise data and analytics tools. Published at: https://www.eckerson.com/articles/the-buyer-s-guide-to-selecting-the-right-enterprise-data-analytics-tool

Moody’s Analytics colleague, Dante DeAntonio joins the podcast to discuss the February jobs report, and the team shares their angst about potential cracks in the labor market. The conversation then turns to the potential impact of DOGE cuts to the federal workforce and the economic implications of the on-again, off-again tariffs that has developed in recent weeks. Finally, Dante celebrates his clean sweep in the stats game and Mark and Cris revise their recession probabilities.   Guest: Dante DeAntonio, Senior Director of Economic Research, Moody's Analytics Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, and Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X', BlueSky or LinkedIn @MarkZandi, Cris deRitis on LinkedIn, and Marisa DiNatale on LinkedIn

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

The rise of A-B testing has transformed decision-making in tech, yet its application isn't without challenges. As professionals, how do you navigate the balance between short-term gains and long-term sustainability? What strategies can you employ to ensure your testing methods enhance rather than hinder user experience? And how do you effectively communicate the insights gained from testing to drive meaningful change within your organization? Vanessa Larco is a former partner at NEA where she led Series A and Series B investment rounds and worked with major consumer companies like DTC jewelry giant Mejuri, menopause symptom relief treatment Evernow, and home-swapping platform Kindred as well as major enterprise SaaS companies like Assembled, Orby AI, Granica AI, EvidentID, Rocket.Chat, Forethought AI. She is also a board observer at Forethought, SafeBase, Orby AI, Granica, Modyfi, and HEAVY.AI. She was a board observer at Robinhood until its IPO in 2021. Before she became an investor, she built consumer and enterprise tech herself at Microsoft, Disney, Twilio, and Box as a product leader. In the episode, Richie and Vanessa explore the evolution of A-B testing in gaming, the balance between data-driven decisions and user experience, the challenges of scaling experimentation, the pitfalls of misaligned metrics, the importance of understanding user behavior, and much more. Links Mentioned in the Show: New Enterprise AssociatesConnect with VanessaCourse: Customer Analytics and A/B Testing in PythonRelated Episode: Make Your A/B Testing More Effective and EfficientSign up to attend RADAR: Skills Edition - Vanessa will be speaking! New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

This episode is a special edition in honour of International Women's Day on 8th March. Host Jason Foster is joined by Lou Hutchins, Director of Data Culture & Literacy at Cynozure, and Rose Attridge, Strategy Advisor at Cynozure. Together, they explore gender diversity in data and AI, the importance of sponsorship and allies, and challenges in male-dominated industries. They also discuss the role of data and AI in driving change, the need for role models, early engagement, and company action.    *****    Cynozure is a leading data, analytics and AI company that helps organisations to reach their data potential. It works with clients on data and AI strategy, data management, data architecture and engineering, analytics and AI, data culture and literacy, and data leadership. The company was named one of The Sunday Times' fastest-growing private companies in both 2022 and 2023 and recognised as The Best Place to Work in Data by DataIQ in 2023 and 2024. Cynozure is a certified B Corporation. 

Learn the technical and soft skills you need to succeed in your career as a data analyst. You’ve learned how to use Python, R, SQL, and the statistical skills needed to get started as a data analyst—so, what’s next? Effective Data Analysis bridges the gap between foundational skills and real-world application. This book provides clear, actionable guidance on transforming business questions into impactful data projects, ensuring you’re tracking the right metrics, and equipping you with a modern data analyst’s essential toolbox. In Effective Data Analysis, you’ll gain the skills needed to excel as a data analyst, including: Maximizing the impact of your analytics projects and deliverables Identifying and leveraging data sources to enhance organizational insights Mastering statistical tests, understanding their strengths, limitations, and when to use them Overcoming the challenges and caveats at every stage of an analytics project Applying your expertise across a variety of domains with confidence Effective Data Analysis is full of sage advice on how to be an effective data analyst in a real production environment. Inside, you’ll find methods that enhance the value of your work—from choosing the right analysis approach, to developing a data-informed organizational culture. About the Technology Data analysts need top-notch knowledge of statistics and programming. They also need to manage clueless stakeholders, navigate messy problems, and advocate for resources. This unique book covers the essential technical topics and soft skills you need to be effective in the real world. About the Book Effective Data Analysis helps you lock down those skills along with unfiltered insight into what the job really looks like. You’ll build out your technical toolbox with tips for defining metrics, testing code, automation, sourcing data, and more. Along the way, you’ll learn to handle the human side of data analysis, including how to turn vague requirements into efficient data pipelines. And you’re sure to love author Mona Khalil’s illustrations, industry examples, and a friendly writing style. What's Inside Identify and incorporate external data Communicate with non-technical stakeholders Apply and interpret statistical tests Techniques to approach any business problem About the Reader Written for early-career data analysts, but useful for all. About the Author Mona Khalil is the Senior Manager of Analytics Engineering at Justworks. Quotes Your roadmap to becoming a standout data analyst! An intriguing blend of technical expertise and practical wisdom. - Chester Ismay, MATE Seminars A thoughtful guide to delivering real-world data analysis. It will be an eye-opening read for all data professionals! - David Lee, Justworks Inc. Compelling insights into the relationship between organizations and data. The real-life examples will help you excel in your data career. - Jeremy Moulton, Greenhouse Mona’s wide range of experience shines in her thoughtful, relevant examples. - Jessica Cherny, Fivetran