Data Analysis for Business Decisions: A Laboratory Manual

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Data Analysis for Business Decisions: A Laboratory Manual

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Text Analytics for Business Decisions: Mastering Techniques for Insightful Data Interpretation through a Case Study Approach

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Python 3 and Machine Learning Using ChatGPT / GPT-4: Harness the Power of Python, Machine Learning, and Generative AI

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Data Visualization for Business Decisions: Transforming Data into Actionable Insights

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Python 3 and Data Analytics Pocket Primer: A Quick Guide to NumPy, Pandas, and Data Visualization

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Dealing With Data Pocket Primer: A Comprehensive Guide to Data Handling Techniques

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Python Tools for Data Scientists Pocket Primer: A Quick Guide to Essential Python Libraries for Data Science

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Data Science Fundamentals Pocket Primer: An Essential Guide to Data Science Concepts and Techniques

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Data Structures and Program Design Using Python: A Self-Teaching Introduction to Data Structures and Python

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Market Research and Analysis: Mastering Market Research: Advanced Methods, Design, and Data Analysis

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Data Science Tools: Comprehensive Guide to Mastering Fundamental Data Science and Statistics Techniques

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Computer Concepts and Management Information Systems: A Comprehensive Guide to Modern Computing and Information Management

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Large Language Models An Introduction: Understanding the Fundamentals and Applications of Generative AI

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Pandas Basics: Mastering Data Analysis with Pandas

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Embedded Vision: Mastering Advanced Techniques for Real-Time Image Processing and Analysis

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Data Structures and Program Design Using Java: A Self-Teaching Introduction to Data Structures and Java

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Data Science for IoT Engineers: Master Data Science Techniques and Machine Learning Applications for Innovative IoT Solutions

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Data Analytics: Master the Art of Data Analytics with Essential Tools and Techniques

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Python: An Introduction to Python Programming

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Tech Trends of the 4th Industrial Revolution: Navigating the Future of Technology in Business

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Introduction to Statistical and Machine Learning Methods for Data Science

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Access 365 Project Book: Hands-On Database Creation

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Data Literacy With Python: A Comprehensive Guide to Understanding and Analyzing Data with Python

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Game Testing: Mastering the Art of Quality Assurance in Game Development

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Microsoft Access 2021 Programming Pocket Primer: A Comprehensive Guide to Mastering Access VBA

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Access 2021 / Microsoft 365 Programming by Example: Mastering VBA for Data Management and Automation

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Python 3 Data Visualization Using Google Gemini: Unlock the Power of Python and Google Gemini for Stunning Data Visualizations

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Artificial Intelligence, Machine Learning, and Deep Learning: A Practical Guide to Advanced AI Techniques

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Prompt Engineering Using ChatGPT: Crafting Effective Interactions and Building GPT Apps

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Flowchart and Algorithm Basics: Learn the Art of Programming through this Guide to Selection, Looping, Arrays, and File Processing

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Company Rules: Or Everything I Know About Business I Learned from the CIA

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Crucial Conversations Tools for Talking When Stakes Are High, Second Edition

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Powerful Phrases for Dealing with Difficult People: Over 325 Ready-to-Use Words and Phrases for Working with Challenging Personalities

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Law of Connection: Lesson 10 from The 21 Irrefutable Laws of Leadership

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Never Split the Difference: Negotiating As If Your Life Depended On It

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The Five Dysfunctions of a Team: A Leadership Fable, 20th Anniversary Edition

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Crucial Conversations: Tools for Talking When Stakes are High, Third Edition

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Who Moved My Cheese: An A-Mazing Way to Deal with Change in Your Work and in Your Life by Spencer Johnson | Key Takeaways, Analysis & Review

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Summary of J.L. Collins's The Simple Path to Wealth

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On Writing Well, 30th Anniversary Edition: An Informal Guide to Writing Nonfiction

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Becoming Bulletproof: Protect Yourself, Read People, Influence Situations, and Live Fearlessly

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The Intelligent Investor, Rev. Ed: The Definitive Book on Value Investing

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Collaborating with the Enemy: How to Work with People You Don't Agree with or Like or Trust

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The Richest Man in Babylon: The most inspiring book on wealth ever written

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Capitalism and Freedom

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Your Next Five Moves: Master the Art of Business Strategy

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Grant Writing For Dummies

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High Conflict: Why We Get Trapped and How We Get Out

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Robert's Rules Of Order: QuickStudy Laminated Reference Guide

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Real Artists Don't Starve: Timeless Strategies for Thriving in the New Creative Age

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Mind Mapping: Improve Memory, Learning, Concentration, Organization, Creativity, and Time Management: Mind Hack, #5

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The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers

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How to Get Ideas

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Tools Of Titans: The Tactics, Routines, and Habits of Billionaires, Icons, and World-Class Performers

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The Confidence Code: The Science and Art of Self-Assurance---What Women Should Know

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Buy, Rehab, Rent, Refinance, Repeat: The BRRRR Rental Property Investment Strategy Made Simple

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Robert's Rules of Order: The Original Manual for Assembly Rules, Business Etiquette, and Conduct

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The Energy Bus: 10 Rules to Fuel Your Life, Work, and Team with Positive Energy

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How to Grow Your Small Business: A 6-Step Plan to Help Your Business Take Off

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Just Listen: Discover the Secret to Getting Through to Absolutely Anyone

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Safely Test Your Applications And Analytics With Production Quality Data Using Tonic AI: The most interesting and challenging bugs always happen in production, but recreating them is a constant challenge due to differences in the data that you are working with. Building your own scripts to replicate data from production is time consuming and error-prone. Tonic is a platform designed to solve the problem of having reliable, production-like data available for developing and testing your software, analytics, and machine learning projects. In this episode Adam Kamor explores the factors that make this such a complex problem to solve, the approach that he and his team have taken to turn it into a reliable product, and how you can start using it to replace your own collection of scripts.

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RAG Quality Starts with Data Quality // Adam Kamor // #262

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Platform Engineering at a FAANG Company

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Build Better Tests For Your dbt Projects With Datafold And data-diff: Data engineering is all about building workflows, pipelines, systems, and interfaces to provide stable and reliable data. Your data can be stable and wrong, but then it isn't reliable. Confidence in your data is achieved through constant validation and testing. Datafold has invested a lot of time into integrating with the workflow of dbt projects to add early verification that the changes you are making are correct. In this episode Gleb Mezhanskiy shares some valuable advice and insights into how you can build reliable and well-tested data assets with dbt and data-diff.

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Build A Data Lake For Your Security Logs With Scanner: Monitoring and auditing IT systems for security events requires the ability to quickly analyze massive volumes of unstructured log data. The majority of products that are available either require too much effort to structure the logs, or aren't fast enough for interactive use cases. Cliff Crosland co-founded Scanner to provide fast querying of high scale log data for security auditing. In this episode he shares the story of how it got started, how it works, and how you can get started with it.

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Composable Data Analytics

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Aligning Data Security With Business Productivity To Deploy Analytics Safely And At Speed: As with all aspects of technology, security is a critical element of data applications, and the different controls can be at cross purposes with productivity. In this episode Yoav Cohen from Satori shares his experiences as a practitioner in the space of data security and how to align with the needs of engineers and business users. He also explains why data security is distinct from application security and some methods for reducing the challenge of working across different data systems.

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X-Ray Vision For Your Flink Stream Processing With Datorios: Streaming data processing enables new categories of data products and analytics. Unfortunately, reasoning about stream processing engines is complex and lacks sufficient tooling. To address this shortcoming Datorios created an observability platform for Flink that brings visibility to the internals of this popular stream processing system. In this episode Ronen Korman and Stav Elkayam discuss how the increased understanding provided by purpose built observability improves the usefulness of Flink.

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Data Sharing Across Business And Platform Boundaries: Sharing data is a simple concept, but complicated to implement well. There are numerous business rules and regulatory concerns that need to be applied. There are also numerous technical considerations to be made, particularly if the producer and consumer of the data aren't using the same platforms. In this episode Andrew Jefferson explains the complexities of building a robust system for data sharing, the techno-social considerations, and how the Bobsled platform that he is building aims to simplify the process.

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MLOps Coffee Sessions #10 Analyzing the Article “Continuous Delivery and Automation Pipelines in Machine Learning" // Part 2

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How Column-Aware Development Tooling Yields Better Data Models: Architectural decisions are all based on certain constraints and a desire to optimize for different outcomes. In data systems one of the core architectural exercises is data modeling, which can have significant impacts on what is and is not possible for downstream use cases. By incorporating column-level lineage in the data modeling process it encourages a more robust and well-informed design. In this episode Satish Jayanthi explores the benefits of incorporating column-aware tooling in the data modeling process.

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Jeremiah Lowin – Machine Learning in Investing – [Invest Like the Best, EP.105]: My guest this week is one of my best and oldest friends, Jeremiah Lowin. Jeremiah has had a fascinating career, starting with advanced work in statistics before moving into the risk management field in the hedge fund world. Through his career he has studi

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Building A Self Service Data Platform For Alternative Data Analytics At YipitData: An interview with the YipitData team about how they built a self service platform for building analytics products on alternative data sets to power investment strategies.

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Zenlytic Is Building You A Better Coworker With AI Agents: The purpose of business intelligence systems is to allow anyone in the business to access and decode data to help them make informed decisions. Unfortunately this often turns into an exercise in frustration for everyone involved due to complex workflows and hard-to-understand dashboards. The team at Zenlytic have leaned on the promise of large language models to build an AI agent that lets you converse with your data. In this episode they share their journey through the fast-moving landscape of generative AI and unpack the difference between an AI chatbot and an AI agent.

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Establish A Single Source Of Truth For Your Data Consumers With A Semantic Layer: Maintaining a single source of truth for your data is the biggest challenge in data engineering. Different roles and tasks in the business need their own ways to access and analyze the data in the organization. In order to enable this use case, while maintaining a single point of access, the semantic layer has evolved as a technological solution to the problem. In this episode Artyom Keydunov, creator of Cube, discusses the evolution and applications of the semantic layer as a component of your data platform, and how Cube provides speed and cost optimization for your data consumers.

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Designing Data Platforms For Fintech Companies: Working with financial data requires a high degree of rigor due to the numerous regulations and the risks involved in security breaches. In this episode Andrey Korchack, CTO of fintech startup Monite, discusses the complexities of designing and implementing a data platform in that sector.

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An Exploration Of Tobias' Experience In Building A Data Lakehouse From Scratch: Five years of hosting the Data Engineering Podcast has provided Tobias Macey with a wealth of insight into the work of building and operating data systems at a variety of scales and for myriad purposes. In order to condense that acquired knowledge into a format that is useful to everyone Scott Hirleman turns the tables in this episode and asks Tobias about the tactical and strategic aspects of his experiences applying those lessons to the work of building a data platform from scratch.

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Practical First Steps In Data Governance For Long Term Success: Modern businesses aspire to be data driven, and technologists enjoy working through the challenge of building data systems to support that goal. Data governance is the binding force between these two parts of the organization. Nicola Askham found her way into data governance by accident, and stayed because of the benefit that she was able to provide by serving as a bridge between the technology and business. In this episode she shares the practical steps to implementing a data governance practice in your organization, and the pitfalls to avoid.

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Engineering for Data Privacy: Navigating Infrastructure, Security, and Compliance with Skyflow’s Roshmik Saha: In this episode Roshmik Saha, Head of Engineering at Skyflow, dives into the fascinating realm of data privacy and security solutions. Whether you're considering building your own privacy solution or seeking insights into the infrastructure requireme...

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Adding Anomaly Detection And Observability To Your dbt Projects Is Elementary: Working with data is a complicated process, with numerous chances for something to go wrong. Identifying and accounting for those errors is a critical piece of building trust in the organization that your data is accurate and up to date. While there are numerous products available to provide that visibility, they all have different technologies and workflows that they focus on. To bring observability to dbt projects the team at Elementary embedded themselves into the workflow. In this episode Maayan Salom explores the approach that she has taken to bring observability, enhanced testing capabilities, and anomaly detection into every step of the dbt developer experience.

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Reduce Friction In Your Business Analytics Through Entity Centric Data Modeling: For business analytics the way that you model the data in your warehouse has a lasting impact on what types of questions can be answered quickly and easily. The major strategies in use today were created decades ago when the software and hardware for warehouse databases were far more constrained. In this episode Maxime Beauchemin of Airflow and Superset fame shares his vision for the entity-centric data model and how you can incorporate it into your own warehouse design.

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End-to-End Data Science to Drive Business Decisions at LinkedIn with Burcu Baran - TWiML Talk #256: In this episode of our Strata Data conference series, we’re joined by Burcu Baran, Senior Data Scientist at LinkedIn. At Strata, Burcu, along with a few members of her team, delivered the presentation “Using the full spectrum of data science to...

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Realtime Data Applications Made Easier With Meroxa: Real-time capabilities have quickly become an expectation for consumers. The complexity of providing those capabilities is still high, however, making it more difficult for small teams to compete. Meroxa was created to enable teams of all sizes to deliver real-time data applications. In this episode DeVaris Brown discusses the types of applications that are possible when teams don't have to manage the complex infrastructure necessary to support continuous data flows.

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Machine in Production = Data Engineering + ML + Software Engineering // Satish Chandra Gupta // MLOps Coffee Sessions #16

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Making Sense Of The Technical And Organizational Considerations Of Data Contracts: One of the reasons that data work is so challenging is because no single person or team owns the entire process. This introduces friction in the process of collecting, processing, and using data. In order to reduce the potential for broken pipelines some teams have started to adopt the idea of data contracts. In this episode Abe Gong brings his experiences with the Great Expectations project and community to discuss the technical and organizational considerations involved in implementing these constraints to your data workflows.

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Better Done Than Perfect. Using Surveys for Customer Success with Moritz Dausinger: Today we have another episode of Better Done Than Perfect. Listen in as we talk with Moritz Dausinger, founder of Refiner. Moritz shares the story behind his survey tool, when and how to survey your users, and many other tips for making the most of the survey data.

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Use Consistent And Up To Date Customer Profiles To Power Your Business With Segment Unify

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[AI Breakdown] Summer AI Technical Roundup: a Latent Space x AI Breakdown crossover pod!

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Eliminate The Overhead In Your Data Integration With The Open Source dlt Library: Cloud data warehouses and the introduction of the ELT paradigm has led to the creation of multiple options for flexible data integration, with a roughly equal distribution of commercial and open source options. The challenge is that most of those options are complex to operate and exist in their own silo. The dlt project was created to eliminate overhead and bring data integration into your full control as a library component of your overall data system. In this episode Adrian Brudaru explains how it works, the benefits that it provides over other data integration solutions, and how you can start building pipelines today.

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[Bite] Documenting Data Science Projects