Carlos Abraham Contreras Cisneros

Forecasting is not only about finding the perfect mathematical formula 🔮 Here are the 8 Steps I recommend following👇 1️⃣ Clarify your goals 🎯 - Why do you need a forecast? - Which level of temporality (per day? per week? month?) - Which level of granularity? Item, product category, department? - Which location? global level, country, store or factory? - What is your forecast horizon? - Who is responsible to forecast? Sales, Finance, SCM? 2️⃣ Collect & Transform Data ⛽️ - Identify the key data inputs (you don't need much) - Find the data source (or the right person to help you) - Extract the data - Clean the data - Merge the data into one unique base (avoid multiple files) - Don’t wait for data perfection to start forecasting ;-) 3️⃣ Choose the Forecasting Model(s) 🔮 - Top Down or Bottom up forecast? (I recommend both) - Assess your forecasting maturity level - Start simple with Excel, with naive & statistical forecasts - Train & Test 3 different forecasting models you already know - Track accuracy with demand history (train & test groups)  - Repeat the process with more products - Select the most accurate models 4️⃣ Forecast (Baseline, New Products, Promotions…) 👨‍💻 - Start with the level (like average demand per week) - Then include the trend of the product category ( +10% this year) - Then the seasonality cycle (Xmas peak) - Then start adding value to your forecast (FVA)  - Promotional events - New products  - Standardize all the data inputs from Sales, Marketing... 5️⃣ Measure, Analyze, Review 📊 - Track forecast accuracy (MAE/MAD or RMSE if ML) - Focus your review on the main errors in value, not % - Analyze root cause analysis and outliers - Improve your forecasting model and data inputs - Override the forecast if necessary - Repeat this process every week & month 6️⃣ Automate your Process ⚙️ - Automate data extraction  - Automate data cleaning & merging  - Automate the update of your file & forecast - Use your file as specifications to update your ERP - Or stay in Excel, it's ok if fully automated with Power Query 7️⃣ Empower your Teams 📣 - Don't stay behind your laptop, shine in your company - Communicate your progress (good & bad results) - Become the forecasting facilitator: the S&OP manager - Align all departments toward one unique forecast - Train & educate everyone about your new process - Collaborate with your big clients & suppliers: win-win approach - Empower your team and delegate 8️⃣ Prepare the Future 🤖 - Explore new methods of forecasting - Forecast with Python (Python just arrived in Excel ;-) - Forecast with Machine Learning - Try a forecasting software - Never stop learning with Experts 9️⃣ Celebrate each step of your progress with a good croissant 🥐 This 8 steps process is a quick overview of what we teach with our training Forecasting Expert. You can master this process in a few weeks by joining us below 👇 https://lnkd.in/eYv7RPEA #forecasting #demandplanning

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Discover our 8 Steps Forecasting Expert Process 👇 https://lnkd.in/em5JDRB2 #forecasting #operation # demandplanning

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Let’s talk about “convenience” in data projects! This is not discussed enough, all we hear about is optimization, The best tool, the best methods, and how to do things the best way posible. What I’m realizing is, very often convenience beats everything else. In the real world it is hard to makes things as perfect as they can be: We have: Convenient project, Convenient data sampling, Convenient data storage, Convenient data transformation and feature eng Convenient model Convenient trainning method Convenient test Convenient experiments Convenient deployment Convenient monitoring Etc. I’m sure most of us have a story where we used the convient methods/tools instead of the best one. #data, #machinelearning

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Essential Concepts for Mastering Data Analysis: From Theory to Practice https://ift.tt/PMexsXn Everything you wanted to know about Statistics and Data Analysis Photo of Myriam Jessier on Unsplash Understanding data nowadays is crucial. Statistics and Data Analysis have gained great importance in our society. They are far more than just numbers and graphs — they are the cornerstone of modern data-driven decision-making. Statistics and Data Analysis are far more than just numbers and graphs — it’s the cornerstone of modern data-driven decision-making. In this comprehensive guide, we’ll explore what Statistics and Data Analysis are, their fundamental concepts, and how they apply to real-world problems through practical examples. This article is an extract of my more detailed Essential Concepts for Mastering Data Analysis: From Zero to Expert with Step-by-Step Explanations and Practice guide, that you can find for a very reasonable price on Gumroad. I’m speaking about less than two dollars :) Essential Concepts for Mastering Data Analysis: From Zero to Expert [2024] I would be very grateful if you consider buying it. I work hard to create high-quality content, which is very important. Thank you so much in any case. At the end of this guide, you will be able to understand how Statistics and Data Analysis work and how you can master them. Whether you’re a business professional, researcher, or aspiring Data Analyst or Scientist, this guide will equip you with the essential knowledge to navigate the data-driven landscape confidently. In that guide, you will find Theoretical Statistics and Data Analysis Concepts such as: Descriptive and Inferential Analysis Population and Sample Categorical and Numerical Variables Mode, Mean, Median, and Standard Deviation Correlation Coefficient Practical Use Cases, Python Code, and Notebooks: - Agricultural Yield Analysis - Customer Satisfaction Analysis - Marketing Campaign Effectiveness All these concepts have Python Code and Python Notebook that you can quickly and easily run on Google Colab or your Computer for free. Instead, in today’s article here’s the Table of Contents: What is Statistics Data Analysis and Statistics Population and Sample The difference between Statistics/Data Analysis and Machine Learning Python Data Analysis Approach Machine Learning Approach using Python PRACTICAL USE CASE: Agricultural Yield Analysis Before we start, please consider following me on Medium or LinkedIn. Join my Medium Newsletter to receive updates on my articles. This is FREE! :) What is Statistics? Statistics is a sophisticated methodology for collecting, analyzing, interpreting, and drawing conclusions from information. It is a vast and versatile discipline that finds applications across numerous fields. It represents a comprehensive methodology for collecting, analyzing, and interpreting information to draw meaningful conclusions. It is a science that enables us to extract meaningful...

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New Project Alert ! Analysing iPhone Sales Trends with Pandas 🌟 Overview: The project aims to delve into iPhone sales data, applying robust data analysis techniques using Python's powerful library—Pandas for data manipulation. By leveraging these tools, the project extracts meaningful insights from iPhone sales data, identifies trends. Key Steps and Techniques: Data Collection and Preparation: -Gathered raw iPhone sales data from reliable sources or simulated data for analysis. -Employed Pandas to clean, preprocess, and organize the data, handling missing values or outliers if present. Feature Engineering and Transformation: -Engineered new features or transformed existing ones to extract more meaningful information. -Applied grouping, aggregations, or time-series analysis using Pandas to derive valuable metrics. Conclusion: The project culminates in a comprehensive analysis of iPhone sales data, offering valuable insights and actionable recommendations based on the observed trends and patterns. Key Learning Outcomes:             -Proficiency in utilizing Pandas for data manipulation, analysis, and computation. -Understanding of interpreting data trends and patterns to derive actionable insights in a business context. Github Reprository Link: https://lnkd.in/gVPQxfJE

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New #Tableau viz for #MakeoverMonday WK3 of 2023: Interstate and Cross-Border 2022 Migreation Patterns of Household Goods Link: https://buff.ly/3UmjbVY #dataviz #dataanalytics

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🔍 Tackling SKU Tracking Challenges in E-Commerce: A Practical Approach As professionals in the data field, we often face complex challenges that demand innovative solutions without stretching our resources. An example of one such problem that I had to face at Yoga Bar (Sprout Life Foods) is as follows 🛒 Problem Statement: We needed to monitor our SKUs to ensure: 1. #Availability 2. #Pricing 3. Understanding of #Competitor Pricing & #Visibility 4. Calculating Potential #OpportunityLost due to #StockUnavailability 💡 Condition: The solution is to be a no cost solution and no allocation of addional resources due to maximum bandwidth utilization. 🔨 Solution: Step 1: Breaking Down the #ProblemStatement. The core issue was tracking #SKUs across various websites. Essentially, we needed to scrape detailed information from specific e-commerce platforms. Step 2: Defining the #WebScraping Mechanism. Given the need to navigate through multiple pincodes (involving clicks and dynamic interactions), a static scraping tool like #BeautifulSoup4 wouldn't suffice. We opted for #Selenium in #Python, a powerful tool that replicates human interactions on web pages. Step 3: Understanding the Website's Elements and Building the #Code We meticulously analyzed the website's structure and built a robust code to scrape the necessary data. This involved: 1. #Automating the traversal through multiple pincodes 2. #CollectingData on SKU availability, pricing, and competitor information 3. Deriving a detailed #scientific approach in calculating the potential loss in #sales due to unavailability basis forecasting on historical trends. By leveraging Selenium, we efficiently tracked our SKUs without incurring extra costs or straining our existing resources. 🚀 Key Takeaway: When faced with resource constraints, leveraging powerful tools like #Selenium can provide an effective solution for #complexdata tracking and scraping tasks. It's all about breaking down the problem, choosing the right tool, and meticulously crafting your approach. If you want to have a look at my #Github respository drop a comment with the required platform! Scrape through #Instamart, #Blinkit, #Amazon, #Flipkart, #BigBasket & #Supermart with ease! #DataScience #Ecommerce #SKUtracking #WebScraping #Selenium #ProblemSolving #Innovation #Yogabars #DataAnalytics #ResourceOptimization

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🚀 Diving Back into R Shiny Development! 🚀 After a break, I'm excited to jump back into my #RShiny journey on PositCloud! 🌐 So far, I’ve made great progress building the UI 🎨, but now it's time to tackle the next big challenge: the SERVER and the App architecture! 💻 The power of R Shiny lies in how easily it can turn data analysis into interactive dashboards 📊. I’m looking forward to completing this manual and sharing more insights soon! Stay tuned for updates 🔥. 💡 If you're working on R Shiny or thinking about starting, feel free to reach out! #DataScience #RShiny #Analytics #PositCloud #TechJourney #ShinyDashboard #DataVisualization #Programming #RStats #DataAnalysis #MachineLearning #CodingLife #Innovation #UIUX #OpenSource #TechUpdates

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Last week I attended a code-along webinar titled "Getting Started with BigQuery" organized by DataCamp.The instructor was Eduardo Oliveira, and with him was Richard Cotton. I've been hearing about BigQuery for a while now and even seeing it as a requirement in some data analyst job postings. So, this kept me wondering whether or not the toolkit of a data analyst was never going to end. Some of my takeaways from the code-along webinar include the following: 1. WHAT IS BIGQUERY? Google BigQuery is one of the most popular cloud data warehouses. It is familiar with PostgreSQL in terms of the language used to interact with it. It is being used by data analysts, data engineers, etc. 2. DIFFERENTIATING BETWEEN PROJECT, DATASET, AND TABLE During this code-along session, a public dataset was used named "Google cloud release notes." The difference between projects, datasets, and tables was stated. In the case of querying, a table is to be queried and not a dataset. 3. Google Cloud Products At the point of querying the table present in the dataset of the Google Cloud Project, it was discovered that Google Cloud has many products, such as Looker, Kubernetes, BigQuery, etc. 4. OTHER TABS BEYOND THE QUERY OUTPUT There are other tabs such as "Job Information," "Executive Details," "Charts," etc. All of which have their respective uses. 5. DATA VISUALIZATION INTEGRATION At some points, there will be a need to go beyond basic charts. Then one can nicely integrate Looker Studio or Google Sheets. Which, after being done, can be saved and shared. 6. CAUTION I should avoid writing queries that run a large amount of data because this will result in problems. 7. AS A DATA ANALYST Basically, I need BigQuery to query BigQuery itself and to be comfortable with the BigQuery console. 8. WHERE BIGQUERY SITS IN THE DATA ANALYTICS STACK 9. BIGQUERY ARCHITECTURE 10. QUERY OPTIMIZATION  I need to be careful about the number of columns I select. Also, in cases where I will need a subset of big data often to perform analysis, rather than repeating lines of query, I can transfer the subquery into a new table that can be created using BigQuery itself. 11. CREATING A TABLE IN BIGQUERY Tables can be created the normal way. Also, there can be what are called nested columns that are used to store related records. 12. THE USE OF THE STRUCT() DATATYPE This was used to accompany the nested column when inserting values. I can say that the code-along webinar was worth it. #datacamp #bigdata #datawarehouse #googlecloud #bigquery #sql

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My latest AI programming task, cleverly requested by a global customer, can offer an incredible pannel of uses, defining specific detection areas for "moving objects" : - cointing cows entering a stable - counting moving microscopic cells - counting people queueing on a restaurant - counting people entering into a disco etc... at the very low hardware set-up cost of just a camera and a perspnal computer. Gotta love #AI #artificialintelligence #deeplearning #supervisedlearning #unsupervisedlearning #frenchtech #biotech #swisstech #switzerland #it #programming #objectdetection #vevey #smi #lausanne

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Master Forecasting with ML in Excel: Uncover Hidden Patterns and Nonlinear Trends Tired of spending days crunching numbers for your forecasts? It's time to revolutionize your approach! Discover how to leverage the power of Machine Learning directly within Excel to predict future trends with unparalleled accuracy. From uncovering hidden patterns to handling complex nonlinear relationships, you can now achieve in minutes what used to take days. I'm excited to share my expertise on this topic and guide you through the process. Stay tuned for more insights and practical tips! Ready to dive deeper? Subscribe to my YouTube channel for in-depth tutorials and real-world examples. Link in bio! https://lnkd.in/dwWQ_r2W #forecasting #machinelearning #excel #datanalysis #analytics #datascience #productivity #automation #businessintelligence #youtubetutorial

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I don't know how many times I've committed myself to working on some projects before fully scoping the user requirement and understanding whether a Dashboard will truly address the user's needs. #ExperienceTeaches! #DataAnalytics #DataScience #Data #AI #ML #dfcuBank

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Learning about root-cause analysis will turn you into a problem-solving expert. Here’s how to uncover the real reasons behind your issues! 1. 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Start by clearly stating the issue. For example, “We’ve observed a significant decrease in sales in France over the last few days.”     2. 𝗚𝗮𝘁𝗵𝗲𝗿 𝗗𝗮𝘁𝗮: Collect the relevant data like order processing times, customer service interactions, inventory levels, and active marketing campaigns.     3. 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮: Use tools like SQL, Python, or Excel to analyze the data. Look for patterns, trends, and anomalies that could point to the root cause.     4. 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗮𝘂𝘀𝗲𝘀: Brainstorm all possible reasons for the issue. Use methods like the 5 Whys technique to dig deeper into each potential cause.     5. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗛𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗲𝘀: Test your hypotheses against the data. Does the data support your assumptions? If not, refine your hypotheses and test again.     6. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: Once you’ve identified the root cause, support the business by showing possible solutions to address it. Monitor the results to ensure the issue is resolved. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: You notice an increase in customer lead time. Here’s how you might tackle it: • 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: “Customer lead time has increased by 20% in the last three months.”    • 𝗚𝗮𝘁𝗵𝗲𝗿 𝗗𝗮𝘁𝗮: Collect data on order processing, sales deviations from the forecast, and shipping times.    • 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮: You find that the actual sales are in line with the forecast and  shipping times have remained constant, but order processing times have increased significantly.    • 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗮𝘂𝘀𝗲𝘀: Consider factors like outages in the warehouses,  staffing issues due to high sickness rates, or process inefficiencies due to operating close to the maximum capacity.    • 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗛𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗲𝘀: Data reveals that a spike in the sickness rate has reduced the available workforce.    • 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: You propose an increase in capacity planning puffer from 5% to 10% and the hiring of additional temporary workers to fix the situation in the short term. Monitor lead times to ensure improvement. By learning how to perform proper root-cause analysis, you will become an important problem-solving partner for your stakeholders. How do you ensure you’re addressing the root cause of an issue and not just the symptoms? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanalytics #datascience #rootcauseanalysis #problemsolving #careergrowth

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This is kind of Frequent Scenario and argument with Data Engineers 😅 Until the business understands how that creates value, it sounds like a cost center and barrier to AI. Data teams need people who can translate complex data concepts into plain language.

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I have been this question asked many times - Is JupyterLab recommended for data analytics for FP&A (Financial Planning and Analysis) professionals? Should, a FP&A professional invest his or her time and resources in learning JupyterLab over Microsoft Excel for data analytics needs? Read my latest blog published by CFO.University which answers some basic and critical questions the FP&A professionals have regarding JupyterLab. Let me know your thoughts and feedback. Here is the link to the blog. https://lnkd.in/ghwWR-Eg CC: Steve Rosvold Gary Cokins DBP-Institute Tariq Munir Tobias Zwingmann Asif Masani Anders Liu-Lindberg Abdul Khaliq ****** I write, speak, and advise on: 👉 D2A2 (digital, data, analytics, and AI) on business performance. 👉 Entrepreneurship and Authentic Leadership 👉 Growth Mindset and Minimalistic Living ✅ Connect or Follow me in #LinkedIn.

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