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Step-by-Step Guide: The Art of Winning Stakeholders as a Data Scientist to Drive Impact

🚀 Excited to share my first project at CodeClause! 📊 As a Data Scientist, I recently completed a comprehensive Stock Analysis project, and I'm thrilled to showcase the insights and visualizations I developed during this journey. 🔍 Throughout the project, I performed Exploratory Data Analysis (EDA) on stock data from both the Indian and Chinese exchanges. By analyzing various key performance indicators (KPIs), I gained valuable insights into market trends and patterns. Some of the KPIs I focused on were: 📈 Open and Closed Prices: Examined the opening and closing prices of stocks in both the Indian and Chinese markets, highlighting the variations and trends. 📊 Total Volume Traded: Analyzed the total volume of stocks traded in both exchanges, identifying trading patterns and liquidity levels. 📉 Moving Averages: Calculated and visualized moving averages, enabling a better understanding of stock price trends over time. 📈 OHLC (Open, High, Low, Close): Explored and visualized the OHLC data, providing a comprehensive view of price movements within a given timeframe. 📊 Stock Volatility: Investigated the volatility of stocks in both markets, identifying periods of high and low volatility and their potential impact on investment decisions. 🔗 Correlation Between Both Exchanges: Explored the correlation between the Indian and Chinese exchanges, uncovering any dependencies or divergences that may exist. ⚖️ Stock Return: Calculated and visualized the returns on stocks, allowing for a comparative analysis of investment performance in different markets. 📊 Additionally, I leveraged Power BI to create an interactive and insightful dashboard. Some of the key visualizations included in the dashboard were: 📈 Maximum High Price by Region: Highlighted the highest stock prices by region, providing an overview of potential investment opportunities. 📉 Return on Investment by Exchange: Visualized the return on investment for each exchange, enabling comparison and assessment of profitability. 📈 Sum of Percentage Change by Currency: Demonstrated the cumulative percentage change in stock prices by currency, revealing the overall market movements. 📉 Average Closing Price by Exchange: Presented the average closing prices of stocks in both exchanges, aiding in understanding the general price levels. 📈 Average Closing Price by Year: Visualized the average closing prices of stocks on a yearly basis, identifying long-term trends and performance. 🌟 I'm proud of the insights gained and the visualizations created during this project, and I invite you to explore the code and results in my GitHub repository:https://lnkd.in/dydge-9a .If you're interested in discussing my work or exploring potential collaborations, feel free to reach out! #DataScience #StockAnalysis #EDA #Visualizations #PowerBI #Insights #CodeClause #DataDrivenDecisionMaking #dataanalysis #data #project #trading #datascientist

New chapter out! More than just appealing graphics… Explore the “art of conveying a compelling narrative through data and deriving profound insights from” data visualizations. You can now access Chapter 6, "Data Visualization" of Julia for Data Science by İlker Arslan at http://mng.bz/MBlB. #julialang #julia #DataScience

🚀 Exciting Opportunity to Explore AI-Generated Books! 📚 In the ever-evolving landscape of technology, AI has become an indispensable tool that shapes our daily lives. As the saying goes, it doesn't replace our jobs but empowers those who embrace it. Inspired by this philosophy, I've harnessed the power of AI to create insightful e-books on Python, ML, DL, and LLMs. (https://lnkd.in/gWM_-Tnf) The heart of this endeavor lies in the invaluable feedback from the human perspective. Now, it's time to put these AI-generated books to the test, and I invite you to be a part of it! Here's the deal: 📌 Comment below indicating: 🖋 Your preferred book from the list below 🖋 Your email address for a direct delivery (If you're not comfortable sharing your email publicly, feel free to privately message me after commenting your book choice.) What's in it for you: 📖 Receive the chosen book for FREE 🚀 Bonus book on Data Science 📬 Subscription to the AI & Entrepreneurship newsletter  (https://lnkd.in/gR74PEgv) 💰 60% discount code for any other 5 books on the list 💬 Please vote for the books and share your thoughts in the comments. 👉 Python Books: Python 1 - Python Fundamentals for Absolute Beginners: Mastering the Basics Python 2 - Data Structures and Algorithms in Python for Novices Python 3 - Python for Web Development for Entry-level: Flask and Django Python 4 - Python Data Science Essentials for Rookies: Pandas and NumPy Python 5 - Python Visualization Mastery for Dummies: Matplotlib and Seaborn 👉 Machine Learning Books: ML 1 - Machine Learning Foundations for Absolute Beginners: Concepts and Techniques ML 2 - Supervised Learning Techniques with Python for Novices ML 3 - Unsupervised Learning and Dimensionality Reduction for Entry-level ML 4 - Ensemble Learning and Random Forests in Python for Rookies ML 5 - Support Vector Machines and Kernel Methods for Dummies 👉 Deep Learning Books: DL 1 - Understanding Neural Networks and Deep Learning for Absolute Beginners DL 2 - Building Your First Neural Network with Python for Novices DL 3 - Training Models with TensorFlow and Keras for Entry-level DL 4 - Convolutional Neural Networks for Image Classification for Rookies DL 5 - Recurrent Neural Networks for Sequential Data for Dummies 👉 LLM Books: LLM 1 - Large Language Models and Their Architectures for Dummies LLM 2 - How to Preprocess, Tokenize, and Pretrain Language Models for Rookies LLM 3 - The Transformer Model, Self-Attention, and BERT for Novices LLM 4 - GPT, GPT-2, and GPT-3: Generative Pre-trained Transformers for Text Generation for Entry-level LLM 5 - RoBERTa, DistilBERT, ELECTRA, ALBERT, and XLNet: Variants and Improvements of BERT for Absolute Beginners 📚 Note: There are 10 books in each category. You can find the full list here: https://lnkd.in/gWM_-Tnf 📄 Discover more AI-generated tech content on my Medium page: medium.com/@a.kubratas Thank you in advance for joining us on this AI adventure. 🌐🤖

📊🔍 Excited to embark on an Exploratory Data Analysis (EDA) project using the captivating Netflix dataset! 🎬🌟 With this project, I aim to dive deep into the vast ocean of Netflix's content, unraveling insights and patterns that lie within.From analyzing viewership trends across different regions to uncovering the most popular genres, this journey promises to be both enlightening and enriching. By leveraging various analytical techniques and data visualization libraries, I looked forward to uncovering hidden gems and untold stories buried within the dataset. 🎓 Steps involved in the project: ▫ Reading and inspecting the data. ▫ Data Cleaning: ▪ Handling missing values ▪ Standardization ▫ Univariate analysis ▫ Bivariate analysis

This week on The Confident Commit: Gideon Mendels, Co-Founder and CEO of Comet, shares thoughts on how to work effectively with ML teams as a software engineer. For software engineers and leaders, there is plenty of pressure at the moment to add AI-powered capabilities into existing products. Unfortunately, the complexities of jumping into this new space can be overwhelming. Even with an in-house data science or ML team, it can be hard to know what to ask. In this episode, Gideon Mendels uses his experience as both a software engineer and data scientist to bridge the divide between these disciplines so we can all build great things together. Distilling it down to some key concepts for folks new to the space, Gideon highlights a few things that are really helpful to know: 👉 The basic types of models that are in use and where you might apply them 👉 The classic risk of overfitting and how to manage for your use case 👉 Ultimately, knowing what kinds of problems have readily available solutions and what kinds of problems are going to require deep research Gideon also shares thoughts on how to start a data science team if you don't have one already. If you're an engineer or a leader trying to make sense of this fast-moving space, this high-level perspective from Gideon will definitely help you navigate. I learned a ton.

👋 Meet Nicolas Decavel-Bueff, a respected #data scientist on our team. In a spotlight about Nicolas’s background and passions, he reveals the importance of thorough problem solving, data quality, and realistic testing. 👀 He even shares some inspiring advice for budding data scientists. Check out the full Q&A session with Nicolas here. https://hubs.li/Q01V7nkq0

All of my recent talks on #largelanguagemodels and #generativeai are now available in one place! Check them out at https://lnkd.in/gnJhSpNt 👩💻 Generative AI for Developers: Covers a high-level introduction to generative AI and LLMs, and getting started with the OpenAI API, and frameworks like HuggingFace, and Olllama 🎇 Natural Language Processing at Scale with SparkNLP: A brief introduction to NLP on Big Data with Spark, named entity recognition (NER), and using a SparkNLP pipelines 📅 A Year of Generative AI - Looking Back on 2023: An overview of the field of LLMs, key players and developments in the year GenAI took off, and predictions of what lies ahead. 🏛 A Survey of Foundation Models: Brief intro to LLMs and overview of models like BERT, GPT, LLaMA 2, Falcon, MPT, Mistral and others. 🔠 GDG NLP Talks: Presentations on #naturallanguageprocessing for Google Developer Groups

🗣️ "MLOps and feature stores are indispensable when it comes to building machine learning applications!" That's what our Data Architect, Jiri Steuer, explains in a 5-minute interview in cooperation with Hobsworks, hosted by Rik Can Bruugen! 👉 See the whole video or read the written version of the interview!

In case you missed it, we've joined Medium! Be sure to follow + subscribe to catch this series of blog posts written by Digitate's top-notch team of data scientists. 📈 And while you're at it, read this interesting piece by Pushpam Punjabi on what word embeddings are and how you can learn the "word2vec approach".