Data Engineering’s Post

Post #3 of the "Pareto's Principle Applied to Data Engineering" Series Undoubtedly, Spark is a Must-Have Skill for Every Data Engineer! Spark is the superhero of data engineering! Why, you ask? Because in a world drowning in data, the ability to swiftly sift through and analyze this digital gold is not just a fancy skill—it's your data engineering superpower! Unmatched Speed: Imagine processing massive data lakes at the speed of light, turning complex data analysis into a walk in the park. It's the right tool for tackling vast data landscapes. PySpark - Unleashing Python's Power: Combine Python's simplicity and Spark's brawn, and what do you get? PySpark. This powerhouse duo enables you to prototype and develop intricate data pipelines at warp speed. A Universal Passport: From startups to tech titans, Spark is the golden ticket. Mastering it doesn't just add a skill to your resume; it opens doors to cutting-edge projects and organizations around the globe. The Key to Data Mastery: In the kingdom of data, being fluent in various data formats, especially JSON, is your crown. Spark doesn't just handle data; it makes you capable of unlocking insights from the most intricate data structures. For example, would you be able to read the data from the nested json below using PySpark?

Here are sure shot ways to build a rock solid profile for yourself after you complete a course in Data Science. [1] Build small and real projects : Do you have multiple credit cards? Write a python script to extract transaction data from all card statements and build a visualization tool on top of it, deploy it on Stream lit. It's not cutting edge LLM project. But you will learn a lot. [2] Write a detailed blog : Take a new / emerging topic or model and write a very detailed blog on it. Example of detailed blog : Illustrated Transformer by Jay Alammar . Going very detailed on an algorithm / topic is good skill. For example, how word piece tokenizer works or what led to xgboost being fast. [3] Talk to Data Scientists : Don't ask for career mentorship. Advice is everywhere. Ask an Data scientist what projects did he do in last 6 months, what business use cases did he solve, what challenges faced etc. Try to ponder over and make notes or replicate them. [4] Explore Govt Data : Today, a lot of govt data is raw and public. It can be attendance of MPs in Parliament or electricity bills of streets - every dataset has a story to tell - dig it up and write. [5] Read classic research papers : Word Embeddings, Transformer architecture, CNN Xgboost etc. Write notes and re-read them again. How are you building your profile ?

🚀 Reflecting on 2023: The Year in Data Science Skills 🚀   As we settle into 2024, it's the perfect time to look back at the data science skills that defined 2023. Understanding the skills that captured employers' attention last year gives us invaluable insights into the evolving landscape of data science and what to anticipate in the year ahead.   Top 10 Data Science Skills of 2023:   🤖 Machine Learning - The art and science of algorithms and techniques for predictive modeling. 📊 Data Visualization - Transforming data into compelling visual stories. 🐍 Python Programming - The go-to language for data manipulation and modeling. 🔢 Statistical Analysis - The backbone of data-driven decision-making. 🌐 Big Data Analytics - The skill to wrangle massive datasets into actionable insights. 🔍 Data Mining - Digging deep into data to uncover patterns and insights. ⚙️ Data Engineering - Architecting the backbone for data processing and storage. 📖 Data Storytelling - Merging data with narrative to inform and persuade. 💬 Natural Language Processing - Making sense of human language through technology. ☁️ Cloud Computing - Leveraging the cloud for scalable data solutions. These skills highlight the industry’s shift towards machine learning, analytics, and the increasing importance of not just analyzing data, but making it accessible and understandable through visualization and storytelling. The emphasis on Python, statistical analysis, and big data reflects a continued focus on robust, scalable data solutions.   🔍 Looking Ahead: 2024 and Beyond 🔍   The trends of 2023 set the stage for an exciting 2024. As data science continues to evolve, staying ahead means diving deep into these skills, exploring new technologies, and always being ready to learn and adapt.   At Dataforce, we're committed to equipping professionals and organizations with the insights and talent needed to thrive in this dynamic field. Whether you're looking to advance your career or seeking the right talent for your team, understanding these skills is crucial.   Let’s embrace the challenges and opportunities of 2024 together, armed with the knowledge and skills that will shape the future of data science.   #DataScience #MachineLearning #BigData #Python #CloudComputing #DataVisualization #Dataforce

𝗘𝘃𝗲𝗿 𝘄𝗶𝘀𝗵𝗲𝗱 𝗳𝗼𝗿 𝗮 𝗯𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁 𝘁𝗼 𝗺𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗱𝗮𝘁𝗮 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴? 𝗦𝘁𝗼𝗽 𝗿𝗶𝗴𝗵𝘁 𝘁𝗵𝗲𝗿𝗲, 𝗜 𝗵𝗮𝘃𝗲 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗳𝗼𝗿 𝘆𝗼𝘂! 1. SQL - it's the ABCs of data. Master the art of GROUP BY aggregations, get cozy with Joins (be it INNER, LEFT, FULL OUTER), Window functions, and Common table expressions. 2. Dive deep into data modeling. Familiarize yourself with data normalization and 3rd normal form. Understand fact, dimension, and aggregate tables. Explore efficient table designs, like cumulative, and complex data types like MAP, ARRAY, and STRUCT. 3. Discover the magic of Python. Master loops, if statements and flex your skills with robust libraries like Pandas, numpy, scikit-learn, and Great Expectations. 4. Quality control? Absolutely! How to write a thorough data check, or implement the write-audit-publish pattern in your pipelines? Yes, you'll need those. 5. Venture into the world of distributed compute. Read about MapReduce and see its influence on today's distributed computing. Learn about partitioning, skew, and spilling to disk. 6. Take a shot at job orchestration. Understand the nuances of CRON, and try out a scheduler like Airflow or Prefect. 7. Bring it all together as you apply distributed compute principles. Start with a free trial of Snowflake or BigQuery. If feeling adventurous, go for Spark + S3. Embarking on a journey in data engineering? Or just curious about what it takes to become one? This roadmap is your guide. Now, unleash your potential and hit the ground running. Let's conquer data engineering together!

Big data and data science are two intertwined concepts that are revolutionizing the way we live and work. Big data refers to massive datasets that are too large and complex for traditional data processing tools. Data science, on the other hand, is the field that deals with extracting knowledge and insights from this data. Data scientists possess a unique blend of skills that allow them to wrangle, analyze, and visualize big data. These skills include: * Programming languages like Python and R * Statistical analysis * Machine learning * Data visualization Big data opens up a world of possibilities for organizations of all sizes. By harnessing the power of big data, businesses can gain a competitive edge by: * Identifying new customer trends * Improving operational efficiency * Developing new products and services * Making data-driven decisions If you're interested in a career in data science, there are a number of resources available to help you get started. With the right skills and training, you can become a valuable asset in the ever-growing field of big data.

Hey Folks👋, 🚀 Sharing a Project with my learning on Knowledge Graphs! 🧠✨ App Link : https://lnkd.in/gQwp8FiD TLDR: This post is part 2 of a series on using the Streamlit framework to create knowledge graphs. It walks through building a project that visualizes data science tools, categorizing them into "Collection", "Cleaning", "EDA", "Model Building", and "Model Deployment". The setup involves creating a virtual environment, installing dependencies, and defining the graph's nodes and edges. Future improvements include adding node images and more resources. 🤖 Topic Covered: 📌 Project details 📌 Project Setup 📌 Future improvement ◉ Get Involved in my Github: 👉 GitHub: https://lnkd.in/g6sbU9cR 🚀 Contribute, Clone, and Share! ◉ Find my Youtube🎥: https://lnkd.in/esW5M3vb ◉ About me: ----------------------------------------------------------------- ✅I am a "Decision Scientist at Mu Sigma Inc.👷" || Kaggler (Highest World Rank - 236/200,000+) 🏵|| Medium Content writer✍️|| YouTuber 📷(100k+ views), 🌺 Learning and exploring how Math, Business, and Technology can help us to make better decisions in the field of data science. ◉ Find my all handles🔁: https://lnkd.in/dCTfTneV ◉ Find my Kaggle📓: https://lnkd.in/g_XeR9J2 ◉ Find my Medium✍️: https://lnkd.in/dERdbeNi ◉ Subscribe to my newsletter📃: https://lnkd.in/gEVgycBc ----------------------------------------------------------------- #Streamlit #DataScience #KnowledgeGraph #Visualization #DataTools #Python #ProjectTutorial #GraphVisualization #DataAnalysis #DataCleaning #EDA #ModelBuilding #ModelDeployment #VirtualEnvironment #GitHub #AppDevelopment

Embark on your data science journey with advice on building a strong foundation, mastering programming languages, gaining practical experience, and cultivating strong communication skills to succeed in this evolving field! #DataScienceTips Essential Advice for Embarking on Your Data Science Journey! #DataScience #DataCareers

👩💻Join the dark side of data, Conquer the Dataverse with Data Engineers! What is the Data side of data❓ - Data Silos: Scattered information, locked away in different formats and locations, frustrates collaboration and analysis. - Data Inconsistency: Inaccurate or incomplete data leads to skewed results and wasted time. - Scalability Woes: As data volumes explode, managing and processing it becomes a herculean task. - Technology Overload: Choosing the right tools from a myriad of options. ➡️Embrace the light and avoid the fearful journey with my experience - ✅Do: Master the fundamentals – SQL, Python, and data structure are your lightsaber moves. ❌Don't: Get lost in tools – learn how they work, not just how to click buttons. ✅Do: Be curious – explore different data challenges and experiment with solutions. ❌Don't: Be afraid to fail – mistakes are learning opportunities, not setbacks. ✅Do: Collaborate – share knowledge, learn from others, and build your Jedi network. ❌Don't: Go it alone – the dataverse is vast, teamwork makes the dream work. 👉Here's how I began my upskilling journey, why don't you give it a try? 1️⃣Lay the Foundation: Start with the basics of Python programming, data structures, and algorithms. 2️⃣Dive into Databases: Learn SQL and explore relational and NoSQL databases. Embrace the Cloud: Get familiar with cloud platforms and their data services. 3️⃣Master ETL Pipelines: Understand the concepts and tools for building data pipelines. 4️⃣Explore Big Data: Learn about distributed computing frameworks like Hadoop and Spark. 5️⃣Specialize and Experiment: Choose a niche (e.g., cloud-based data engineering, machine learning) and explore different tools and technologies. ♐️Remember, the journey of a data engineer is like a marathon, not a sprint. Be patient, embrace the challenges, and most importantly, have fun exploring the fascinating world of data! #data #engineering

From Batchlor of Engineer to Data Engineer Transformation Course in 8 Week for Rs 8k daily 8hr of practical work offline in office course starting 15 July 2024, a journey vast, A path to data dreams at last. With circuits and code, the start is made, In the world of tech, where dreams cascade. #Week One: Foundations Lay, Maths and logic pave the way. Learn to program, Java and Python, building blocks for dreams to latch on. #Week Two: The Depths We Seek, Data structures, algorithms peak. Understanding how data flows, Through binary trees and linked list rows. #Week Three: The Digital Dance, Microprocessors take a stance. Embedded systems, circuits tight, Engineer's canvas, painted in light. #Week Four: Networks Wide, Protocols and data side by side. Packets journey, data's flight, In the ether, day and night. #Week Five: The Database Loom, SQL queries, tables bloom. Storing bytes, in structured rows, Data’s secrets, now disclosed. #Week Six: The Software Craft, Object-oriented, agile path. Version control, Git in hand, Collaborating with a dev command. #Week Seven: The Data’s Call, Big data rises, we heed the call. Hadoop clusters, Spark's bright flame, Data lakes, where knowledge claims. #Week Eight: The Final Run, Projects shine under the sun. Analytics, ML in stride, Graduation's near, dreams abide. #From BE to DE: The Master's Quest, Advanced degrees to be the best. Statistical models, AI’s might, Deep learning takes its flight. Into the World: The DE Role, ETL pipelines, data’s soul. Transform, load, extract the gold, in data’s story, truth unfolds. #Clouds and Storage: A Sky So Vast, AWS, Azure, knowledge amassed. Data warehouses, lakes anew, Engineer's dream, coming true. #Visualize: The Final Scene, Power BI, Tableau gleam. Insights gleaned from data's core, Stories told, forever more. From circuits small to data grand, An engineer’s journey, hand in hand. With each week, the vision clears, From BE to DE, a path endeared.

Okay, it's time to confess how and why I transitioned into data science. Sit with me by the fireplace, grab a cup of hot chocolate and listen to my story Like many of you know, I studied physics and decided I wanted to be a game developer, so I enrolled in a program focused on software and data engineering. - From the get-go, I knew that I'll have some data centric courses like probability and Statistics, Machine learning, NOSQL databases and a few more. On my studies, I learned Python and some more tech. We often worked in groups so each time we built some project I was in charge of backend and database infrastructure - my first introduction to data integrity and security. Halfway through my sophomore year one course covered business intelligence, data engineering and data science concepts. That's when I've become interested. I somehow had a clear vision that this field will reinforce AI and affect many things in the future. I saw data for what it really is - a product and that's when I realized I had to be there and use my knowledge for doing good - something I still adhere to. In April 2019, I published a review paper of GPT-2 and its potential to be abused for creation of fake news and propaganda. It was just a student work but I realized I wanted to stay in this field and that it's my future. The pandemic slowed down that process, along with two losses in the family and financial problems. Still, I kept this account as a testament of my transition to data. I took courses, solved challenges, subscribed to yearly programs, and read a lot of books and articles to support my transition. Today, I'm a self-employed data scientist, working on separate projects. I'm enrolled in a master's program in data science and I'm paving my way to a career in consulting. If you're looking to learn data science and AI follow me. Check the roadmaps in my bio to get started! Good luck! 💜