DATAcated’s Post

Understanding the Data Science Process is crucial for any project. It’s the roadmap that guides you from start to finish, ensuring you don’t miss any essential steps. Let’s break it down: 1️⃣ Identify the Question This is where it all begins! Take the time to talk to stakeholders and define what they need. What questions are they trying to answer? This step is crucial to avoid scope creep and ensure everyone is on the same page. Spend about 10% of your project time here. 2️⃣ Prepare the Data The most time-consuming phase – and the least glamorous. You’ll gather data from various sources, clean it, check for anomalies, and ensure it’s ready for analysis. This step can take up to 70% of your time but is absolutely vital. 3️⃣ Analyze the Data Now, the fun begins! Apply machine learning, build models, and run text analytics. If your data preparation was thorough, this phase should be relatively quick – about 10% of your time. This is where the magic happens! 4️⃣ Visualize the Data It’s not just about finding insights; it’s about presenting them effectively. Create dashboards, graphs, and other visual tools to make your findings accessible. Allocate another 10% of your time here. 5️⃣ Present Your Findings This often-overlooked step is where you communicate your results to stakeholders. Explain the implications and recommended actions clearly and concisely. Surprisingly, this can take an additional 80% of your time, so plan accordingly! ❓ Why does this add up to more than 100%? Great question! Presenting your findings effectively often takes much more time than anticipated. After completing your main analysis, the effort to communicate insights and ensure stakeholders understand and act on them can be extensive. This "extra" time is crucial for the success of the project. Understanding this process helps keep the bigger picture in mind and ensures all components of data science fit together seamlessly. Ready to dive deeper into the data science process? Drop your questions below or let me know if you’d like to see more examples in action! 🚀 #DataScience #MachineLearning #BigData #DataScienceProcess

Calling all data professionals: learning to slice your data product stories finely is the key to delivering value! 🤯 This started with me trying to help a data team deliver more value and we talked about slicing up the work so that we could lower risk and deliver smaller increments earlier. We got to a certain point in our slicing effort but found we could go no further. My colleagues in the application development space talk about elephant carpaccio and Everest Engineering had already run some internal carpaccio workshops so Daniel Prager and I started to noodle about a concept we called Data Carpaccio - how do you slice data stories up really fine? At the time I thought Data was somehow different to Application Development. After all we have the whole Extract-Load process from multiple source systems, then there are the thousands of Transformation models from Staging to Mart, followed by the BI dashboard and reporting tool - so doing all of this in a single finely sliced story seemed hard. After we worked on it for a while we realised the principles (and even the techniques) are actually the same. Data Product user stories can (and should) be sliced fine!! 🎉 So now Everest Engineering has a workshop where we can help data engineers, analytics engineers, data analysts, data scientists and all others in the data community learn .... TO SLICE FINELY Links below

This article conveys the importance of data visualization and storytelling for data scientists to be highly informative and insightful. The article effectively communicates why data visualization and storytelling are crucial for data scientists. It emphasizes that data alone is not enough; it needs to be presented in a compelling and understandable manner to drive informed decision-making. https://lnkd.in/g2tKcZdi

🚀 Excited to share my latest article on Medium! 🚀 Many data challenges start before a single line of code is written. Understanding the nuances of data modeling is crucial, impacting costs and timelines significantly. In my latest article, I explore the world of data modeling, shedding light on key concepts and strategies that I learned in the first two weeks of Zach Wilson's boot camp! 🌐 Link to the full article: https://lnkd.in/deJ8ZPy8 Here are some key takeaways: 1️⃣ Consumer-Centric Modeling: Tailoring data models to different consumer archetypes (Analysts, Engineers, ML Models, Executives) is essential. Knowing your audience ensures efficiency in querying and analysis. 2️⃣ Snapshot vs. Cumulative Design: Delving into the pros and cons of snapshot dimensions and cumulative table designs, with insights on when to use each based on your data's nature and consumption patterns. 3️⃣ Slowly Changing Dimensions: Navigating the challenges of handling dimensions that change over time, with practical alternatives and considerations for scenarios like tracking changes in vehicle ownership. 4️⃣ Fact Data Modeling: Unpacking the intricacies of modeling events or occurrences, addressing questions of Who, Where, How, What, and When. Strategies for managing the high volume of fact data and reducing it for effective analysis. 5️⃣ Properties of Facts vs. Dimensions: Distinguishing between dimensions and facts, understanding their unique roles, cardinality, and volume, laying the foundation for accurate and effective data analysis. 6️⃣ Conclusion: Summarizing the importance of mastering data modeling concepts for informed decision-making, optimized costs, and enhanced process efficiency. Join the conversation! 🤝 I'd love to hear your thoughts and experiences in the comments. 🚀📊 #DataModeling #Analytics #dataengineering #DataScience #TechCommunity

Data Modeling Mastery Part 2 is here! Dive deep into data modeling techniques essential for today's data-driven strategies. 🔍 From Dimensional to Data Vault, and beyond. 💡 Unlocking insights through effective models. 🚀 Propel your data engineering to new heights. Read our latest article from Rameez Khan on harnessing the power of data modeling to fuel informed decisions and strategic growth. Read it here: https://lnkd.in/dDKgzCt2

Eye-Opener: Decoding Data Through Visualization hosted by the DataZen Club Introduction The DataZen Club, dedicated to promoting data literacy and analysis skills, is pleased to present a report on our recent event, "Eye-Opener: Decoding Data Through Visualization." This event aimed to demystify data visualization and empower participants to unlock the power of transforming raw data into compelling narratives. 📍Event Overview Held at GITAM (Gandhi Institute of Technology and Management), the event attracted a diverse audience of students from various disciplines. 📍The program included: 1. Keynote Address: A renowned data visualization expert delivered a captivating talk, highlighting the importance of visual storytelling in today's data-driven world. 2. Interactive Workshops: Our experienced club members conducted interactive workshops, guiding participants through the basics of data visualization principles and popular visualization tools. 3. Visualization Showcase: We showcased a collection of impactful data visualizations across different fields, sparking inspiration and igniting discussions about real-world applications. 4. Q&A Session: A dedicated Q&A session allowed participants to address their queries directly with the experts and gain insights. 📍"Eye-Opener" successfully achieved its objectives: 1. Increased Data Literacy: Participants gained a deeper understanding of data visualization concepts and its effectiveness in communicating complex information. 2. Enhanced Skillset: Through interactive workshops, attendees learned practical techniques for creating data visualizations using popular tools. 3. Fostered Creativity: The event encouraged participants to see data as a creative medium for storytelling. 4. Boosted Confidence: The workshops and showcase instilled confidence in participants to utilize data visualization in their academic and future professional endeavors. 📍Impact and Future Plans The event received overwhelmingly positive feedback from participants. We are thrilled to have contributed to fostering a data-driven culture at our university. 📍Moving forward, DataZen Club plans to: 1. Organize follow-up workshops: Building on the introductory sessions, we will offer more advanced workshops focusing on specific data visualization techniques and tools. 2. Host data visualization competitions: This will encourage students to apply their learned skills and showcase their creativity. 3. Collaborate with academic departments: We aim to integrate data visualization workshops into relevant courses, equipping students with valuable skills for their chosen fields.

"...the effectiveness of a business analytics strategy is more than just the volume of data or the sophistication of modeling techniques. Instead, it hinges on an often underrated yet immensely vital skill – data visualization." I couldn't agree more! I stumbled across the wonderful world of #datavisualization most of the way through grad school and got sucked in by the Data Literacy book "Learning to see data". I've held a variety of "#data {something}" titles since then. Talking about #machinelearning typically gets me in the door, but being able to shape data in a way that clearly articulates the knowledge my clients are after, or helps them communicate and drive #datadrivendecisions across their organization is by far the more influential skill.

Title: Enhancing Data Analytics Skills: My Journey Through the Back to Basic Challenge** I am thrilled to share my participation in the 6-week Back to Basic Challenge on Data Analytics, hosted by Olaoluwakiitan Olabiyi Founder of Data4Fashion & Team led webinar Folorunso Ajala, ASM . This transformative challenge, which commenced on October 21, 2023, aims to rejuvenate and enhance fundamental knowledge in crucial areas such as Data Cleaning, Data Quality, optimal chart selection for variables and visualization, statistical distribution, basic hypothesis testing, and much more. In my journey through this challenge, I recently triumphed over the Week 2 challenge. This milestone was achieved through active participation in a comprehensive webinar and successfully completing an assessment led by the knowledgeable @Isaac .o. Okunato. The focus of Week 2 was on Descriptive Statistics and Data Visualization—two pillars in the realm of data analytics. Descriptive statistics play a pivotal role in summarizing and presenting data in a manner that unveils its inherent meaning. The arsenal of measures includes the familiar mean, median, and mode, providing insights into central tendencies. Additionally, measures such as standard deviation and range come into play, offering a glimpse into the spread or variability within the dataset. This knowledge proves indispensable when seeking to comprehend the underlying patterns and nuances concealed within the numbers. Unleashing Insights through Data Visualization: Data visualization, on the other hand, emerged as a powerful ally in my analytical arsenal. It involves the graphical representation of data to unravel patterns, trends, and valuable insights. Charts, graphs, and maps serve as the artist's palette in this visual storytelling endeavor. By translating raw data into visual elements, we empower ourselves to communicate complex information effectively and facilitate a deeper understanding of the dataset. The synergy between descriptive statistics and data visualization is the key to unlocking the full potential of any dataset. While descriptive statistics provide a quantitative foundation, data visualization breathes life into these numbers, making them accessible and comprehensible. This dynamic duo is essential for anyone navigating the intricate landscape of data analytics. As I progress through the Back to Basic Challenge, I am eager to delve deeper into the intricacies of data analytics, armed with the knowledge gained during Week 2. Stay tuned for more updates on my journey to mastering the art and science of data analytics. *Keywords: Data Analytics, Back to Basic Challenge, Chart Selection, Descriptive Statistics, Data Visualization, Statistical Distribution, Hypothesis Testing.*

How to Build Your First Data Science Project! A Step-by-Step Guide👇 Are you a novice and ready to kickstart your first data science project? Today, I'll walk you through a step-by-step guide on how to create your very first data science project. Step 1: Choose Your Project Focus Start by selecting a data-related problem that interests you. It could be anything from predicting stock prices to analyzing customer data. Step 2: Gather Data Find a dataset related to your chosen problem. There are various resources online, like; →Kaggle https://lnkd.in/dsCRgyPk →UCI Machine Learning Repository https://lnkd.in/dfFCD-Gb Step 3: Data Cleaning and Preprocessing Clean the data by handling missing values, outliers, + formatting issues. This step sets the foundation for your analysis. Step 4: Exploratory Data Analysis (EDA) Dive into your data! → Visualize it, spot trends, + gain insights. EDA helps you understand your data better. Step 5: Feature Engineering Create new features or modify existing ones → to improve your model's performance. Step 6: Model Building Choose an appropriate machine learning algorithm ↳ build your predictive model. Step 7: Model Evaluation Assess your model's performance using metrics like; → accuracy → precision → recall. Step 8: Communicate Results Create clear + compelling visualizations ↳ share your findings with others. Step 9: Continuous Learning Data science is a journey of constant learning. ↳ Stay updated with new techniques & tools. Step 10: Share Your Project Don't forget to share your project on LinkedIn! It's a fantastic way to showcase your skills + connect with the data science community🌐. P.S. Remember, it's okay to make mistakes along the way. ↳ Your journey in data science is just beginning, and each project you tackle will make you a better data scientist. P.S.S. Please REPOST ♻️ if you find it valuable. #datascience #datacommunity #datascienceprojects #consistency

🌟 Introducing "Let's Talk Data Engineering"🌟 I'm excited to unveil "Let's Talk Data Engineering," a series poised to deepen and share our understanding of data engineering's vital aspects. Join me as we embark on this journey, commencing with the indispensable subject of Data Modeling and Design. This series is an avenue for collective growth and insight, providing a platform for both seasoned professionals and those new to the field to explore, discuss, and enhance their acumen. Today’s Focus: Data Modeling and Design Data Modeling and Design stands as the cornerstone of effective data engineering, shaping how data is structured, stored, and utilized. Mastery of this discipline is pivotal for transforming abstract data into tangible insights that drive informed business decisions. Core Concepts Unpacked: 1. Conceptual Modeling: A broad overview of data, highlighting the main components and how they're linked. Imagine it as a bird's-eye view of your data world, showing how different parts connect. 2. Logical Modeling: A detailed blueprint outlining the interconnections between data elements, essential for ensuring the design is adaptable across different database systems. 3. Physical Modeling: The practical application of the logical model, tailored to specific database technologies, emphasizing optimization and performance. Schemas Unveiled: 1. Star Schema: Simplifies data analysis with a central fact table linked to surrounding dimension tables. 2. Snowflake Schema: An enhanced version of the star schema, offering additional normalization to minimize redundancy. 3. Third Normal Form (3NF): Aims to reduce data duplication, ensuring consistency and integrity across the database. Join the Dialogue: · Share your experiences or insights on data modeling to enrich our collective understanding. · Encountering challenges with data modeling? Your queries could lead to enlightening discussions. · Have tool recommendations or resources for data modeling? Your expertise can guide and inspire our community. Let’s engage with fellow data enthusiasts, and advance our professional journey. Your participation will not only enhance our shared learning experience but also contribute to a dynamic and informed data engineering community. #DataEngineering #DataModeling #CareerDevelopment #DataCommunity #ProfessionalGrowth #DataInsights #ETL #BigData