Shubham Bhalala

Before you jump into Spark, understand what big data is and why the older technologies struggled to store and process them. Then read about ACID and BASE concepts, especially the difference between consistency and eventual consistency. Understand partitioning and sharding. After this understand the Hadoop architecture, Hadoop ecosystem and programming the Map Reduce way. Now you might say why Hadoop? It's old and slow. If you do not understand the drawbacks of Hadoop and how Spark overcomes them, then you may not understand the need for Spark over Hadoop. Then try practicing writing code in the map-reduce logic. Read how the Machine Learning models can be implemented in Map Reduce and try implementing them. This will help you understand how distributed systems work and how to write optimized code. After doing all this, learn Spark. #dataengineering #dataengineer #spark #hadoop

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Hello LinkedIners, I hope everyone is doing great. I graduated from Michigan State University with a master's in Business Data Science and Analytics in December 2023. I am actively looking for full-time opportunities and would appreciate your support. Allow me to provide a brief overview of my background: 1. With a Master's degree in Business Data Science Analytics from MSU and a Bachelor's degree from Birla Institute of Technology and Science, Pilani, coupled with a track record of excelling in national-level competitive exams, I bring a robust academic foundation and a demonstrated ability to tackle challenges head-on. 2. I am engaged in a National Science Foundation (NSF) Science Foundation-funded research project where I undertake tasks ranging from Exploratory Data Analysis to employing advanced ML techniques like Clustering and Statistical Modeling. This endeavor aims to identify potential research areas geared towards enhancing project team efficiency and fostering workforce development, thereby addressing industry challenges comprehensively. 3. Throughout my master's program, I actively participated in both corporate and academic projects, delving into intricate datasets and harnessing Machine Learning algorithms, Big Data analytics, and Statistical Modeling techniques. Proficiency in programming languages such as Python, R, and SQL, along with adeptness in visualization tools like Power BI, Tableau, and Google Analytics, empowers me to deliver impactful insights and results. 4. My professional journey, punctuated by a tenure at the Bank of India, provided me with firsthand experience in strategic lending, wherein I navigated key challenges, employed financial modeling techniques, and championed risk management methodologies to optimize project outcomes. Notably, my ability to bridge communication gaps, engage diverse stakeholders, and foster seamless cross-functional collaboration has been instrumental in driving success. What distinguishes me is not solely my technical acumen but also my adeptness in interpersonal dynamics and communication. I possess a proven ability to collaborate effectively with cross-functional teams, ensuring alignment toward shared objectives and driving tangible outcomes. I firmly believe that my amalgamation of academic achievements, professional experiences, and innate problem-solving acumen renders me well-suited for the dynamic ecosystem at any organization Thank you in advance for any connections, advice, or opportunities forwarded my way. #DataScience #OpenToWork #Opportunities #JobSearch

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I'm glad to have gone through this course. I learned about the various regression and classification models, how to assess model performance, data preprocessing, and much more. I would love to dive even deeper and explore more possibilities with machine learning and artificial intelligence. Onward we go! Thank you DataCamp and Blossom Academy for the opportunity. #DataScience #MachineLearning #AI #LifelongLearner

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🎧 Spotify Data Pipeline Project 🎧 I recently completed an automated ETL data pipeline project using the Spotify API to collect, clean, and transform song, artist, popularity, and genre data, leveraging AWS and Snowflake for seamless data integration and reporting! 🚀 🛠️ Tools & Technologies: AWS S3 & AWS Lambda for automated data storage, transformation, and serverless computing. Python for data cleaning, transformation, and scripting. Snowflake for robust data warehousing and analysis. IAM & AWS Glue for secure access management and data formatting. 🌐 Pipeline Process: Raw Spotify data is collected via API in JSON format and stored in the raw_data folder in AWS S3. AWS Lambda is triggered daily to automatically clean and transform the raw data into a table format, storing the results in the transformed_data folder. Using Snowflake's storage integration with AWS, the transformed data is continuously pipelined into the Snowflake warehouse for analysis and reporting. 🔒 Security: Implemented AWS IAM roles for secure access to services and ensured smooth integration between Snowflake and AWS S3. 📈 Outcome: This project improved my understanding of cloud-based data pipelines, AWS serverless architecture, and Snowflake's powerful data warehousing capabilities. It provided a robust solution for automating daily data processing and generating insightful reports from Spotify's data. 🚧 Challenges: Efficiently managing data transformation workflows using AWS Lambda. Ensuring secure and consistent data transfer between AWS and Snowflake. 🔍 Conclusion: This project was a fantastic learning experience, deepening my skills in AWS, Snowflake, Python, and automated data pipelines. Looking forward to working on similar cloud-based data engineering projects in the future!

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Hiring a Data Scientist be like : 🕰️2014 Recruiter: We need someone familiar with data science. Me: I know Python and can build a linear regression model. Recruiter: Welcome aboard! 🕕2024: Me: My specialty is statistical analysis. I'm proficient in SQL and Python. I have experience with AWS, GCP, and Azure. I'm skilled in Tableau, PowerBI, Looker, and Excel. On weekends I build Deep Learning models for fun. Recruiter: Hmm, I'm not sure that's quite enough... #DataScience #Hiring #machinelearning #AI #artificialintelligence #businessanalytics #businessintelligence

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🚀 Day 75/100 - Data Engineering Journey Continuing from our discussion yesterday, let's delve into advanced techniques for real-time data visualization with Grafana and Apache Kafka. Dynamic Dashboard Customization Grafana offers advanced features for customizing dashboards dynamically. Users can leverage variables, queries, and template variables to create interactive dashboards that adapt to changing data conditions in real-time. This allows for more flexible and responsive monitoring of streaming data sources. Threshold-based Alerting and Anomaly Detection In addition to basic alerting mechanisms, Grafana supports threshold-based alerting and anomaly detection for real-time data streams. Users can define custom thresholds and triggers based on streaming data metrics, enabling proactive monitoring and timely response to critical events or anomalies in Kafka topics. Integration with External Data Sources Grafana's integration capabilities extend beyond Kafka to include a wide range of external data sources such as databases, cloud services, and IoT devices. By integrating external data sources with Grafana, users can enrich their real-time dashboards with contextual information and gain deeper insights into the relationships between different data streams. To ensure optimal performance of real-time dashboards, it's essential to optimize queries, reduce data processing overhead, and minimize dashboard refresh intervals. Grafana provides tools for optimizing dashboard performance, such as caching, query optimization, and data aggregation, to enhance the responsiveness and scalability of real-time monitoring solutions. Best Practices for Real-Time Visualization 1. Keep dashboards focused and concise, displaying only the most relevant metrics and KPIs for monitoring streaming data. 2. Leverage Grafana's built-in visualization options, such as time series graphs, histograms, and gauges, to convey insights effectively. 3. Regularly review and refine alerting rules and thresholds to ensure they remain relevant and effective in detecting anomalies and critical events. By applying these advanced techniques and best practices, organizations can harness the full potential of Grafana and Apache Kafka for real-time data visualization, monitoring, and analysis. As we continue our journey through the realm of real-time data processing, let's explore these advanced techniques to unlock actionable insights and drive innovation. #Grafana #ApacheKafka #RealTimeData #DataVisualization #AdvancedTechniques #Day75 #100DaysOfDataEngineering

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#Apache Airflow Interview Questions: 𝗕𝗮𝘀𝗶𝗰 𝗟𝗲𝘃𝗲𝗹: • What is Apache Airflow, and why is it used? • Explain the concept of Directed Acyclic Graphs (DAGs) in Airflow. • How do you define tasks in Airflow? • What are the different types of operators in Airflow? • How can you schedule a DAG in Airflow? 𝗜𝗻𝘁𝗲𝗿𝗺𝗲𝗱𝗶𝗮𝘁𝗲 𝗟𝗲𝘃𝗲𝗹: • How do you monitor and manage workflows in Airflow? • Explain the difference between Airflow Sensors and Operators. • What are XComs in Airflow, and how do you use them? • How do you handle dependencies between tasks in a DAG? • Explain the process of scaling Airflow for large-scale workflows. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗟𝗲𝘃𝗲𝗹: • How do you implement retry logic and error handling in Airflow tasks? • Describe how you would set up and manage Airflow in a production environment. • How can you customize and extend Airflow with plugins? • Explain the process of dynamically generating DAGs in Airflow. • Discuss best practices for optimizing Airflow performance and resource utilization. • How do you manage and secure sensitive data within Airflow workflows?

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Through my experience from several recent interviews for data science and machine learning internship positions, I have developed the following hypothesis: Most interviewers focus on fundamental algorithms such as linear regression and logistic regression. The questions typically revolve around deriving the beta coefficients, understanding their importance or significance, and explaining how these relate to feature importance. While implementing machine learning algorithms using the scikit-learn library simplifies the process, explaining the model or understanding the significance of features requires a solid foundation in statistical analysis. In this blog, I aim to explain correlation, bivariate analysis, and regression analysis for two variables through hand calculations and detailed explanations. I welcome your comments and thoughts on this. #DataScience #MachineLearning #Internship #OpenToWork #Statistics

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Understanding Parameters vs. Hyperparameters in Machine Learning 🔍 Parameters vs. Hyperparameters in Machine Learning: What’s the Difference? In the world of machine learning, terms parameters and hyperparameters often come up. While they sound similar, they play very distinct roles in model training and optimization. Let’s break it down: 1️⃣ Parameters: The Model's Learning What are they? Parameters are the values the model learns during training. These include weights (w) and biases (b) in algorithms like Linear Regression, Logistic Regression, and Neural Networks. For example, in a linear model: y=w⋅x+b The model adjusts w and b during training to minimize the loss function. How are they found? The algorithm uses optimization techniques like Gradient Descent to iteratively adjust parameters, minimizing the error between predictions and actual outputs. Parameters are directly influenced by the input data. 2️⃣ Hyperparameters: The Architect’s Decisions What are they? Hyperparameters are the external settings we configure before training the model. These define the learning process rather than being learned by the model. Examples of hyperparameters include: 1 Learning rate (η): Controls how quickly the model adjusts its parameters. 2 Number of layers/neurons in Neural Networks. 3 Number of trees in Random Forests. 4 Regularization strength in Logistic Regression. Why are they important? Choosing the right hyperparameters can drastically improve model performance. Poor choices may lead to underfitting (model is too simple) or overfitting (model is too complex). 3️⃣ How Do We Find the Optimal Hyperparameters? This is where Cross-Validation (CV) comes in! Cross-validation helps us evaluate how well a model generalizes to unseen data by splitting the dataset into training and validation subsets. Here’s the process: Split the Data: Divide the data into k folds. Train and Validate: Train the model on k − 1 folds and validate it on the remaining fold. Repeat this process k times, using a different fold for validation each time. Aggregate Errors: Compute the average validation error across all k k-folds for each hyperparameter combination. Select the Best Hyperparameters: Choose the hyperparameters with the lowest average validation error. 4️⃣ Why Cross-Validation? Without cross-validation, we risk overfitting to the training data or underestimating errors. Cross-validation ensures: Robust Evaluation: Every part of the data is used for training and validation, reducing bias. Hyperparameter Optimization: We can test different combinations of hyperparameters and find the best-performing setup. Tip: Pair hyperparameter tuning with techniques like Grid Search or Random Search to systematically explore the search space.

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🍁 Thankful for Innovation and Learning this Thanksgiving! 🍁 I’m incredibly grateful to Snowflake for the opportunity to attend BUILD 2024 and for this wonderful swag! The event was packed with cutting-edge sessions on Machine Learning, NLP, and Generative AI, showcasing how Snowflake continues to innovate in the AI and data space. 💡 Key Highlights from ML, NLP, and GenAI Sessions: 1️⃣ Fine-Tuning LLMs with Snowflake Cortex Learned how to customize LLMs for specific tasks, reducing computational demands while maximizing accuracy. Fine-tuning techniques are opening new doors for business-specific applications. 2️⃣ Conversational Applications and NLP with Cortex AI Cortex AI is redefining how we interact with data, from operationalizing NLP pipelines to building conversational apps with advanced text-to-SQL enhancements. 3️⃣ End-to-End ML with GPUs in Snowflake Notebooks Witnessed the seamless development and deployment of resource-intensive ML workflows like deep learning, all within Snowflake using Snowpark. 4️⃣ Building Enterprise-Grade AI Apps with Snowflake Native Apps Explored how AI-powered predictive applications integrate with Snowflake's ecosystem to deliver high-impact insights while ensuring data security. 5️⃣ Evaluating LLM Applications at Scale Deep dived into techniques for improving LLM reliability and addressing challenges like hallucinations and safety issues. The practical demo on the RAG triad framework (context relevance, groundedness, and answer relevance) was particularly inspiring. 6️⃣ Building RAG Chatbots Using Cortex AI Hands-on session to build a Retrieval-Augmented Generation (RAG) chatbot combining structured and unstructured data to deliver accurate, real-time answers. 💡 Key Takeaway: Snowflake is making it easier than ever to build, scale, and productionize ML and AI applications, empowering developers to leverage GenAI and advanced NLP tools to unlock the true potential of their data. 🎉 A huge shout-out to Sridhar Ramaswamy, Andrew Ng, and all the brilliant speakers for their inspiring sessions. Your insights are shaping the future of AI-driven innovation! This Thanksgiving, I’m thankful for the opportunity to learn, grow, and be part of a community dedicated to driving data and AI forward. #Thanksgiving #SnowflakeBUILD2024 #MachineLearning #GenerativeAI #NLP #AIInnovation #DataScience

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🚀 Day 96/100 - Data Engineering Journey Today, let's revisit the foundational components of Hadoop Distributed File System (HDFS), a cornerstone technology in the data engineering landscape. Recap on HDFS and Hadoop File System: 1. Hadoop Distributed File System (HDFS): HDFS, a core component of the Apache Hadoop ecosystem, is a distributed file system designed to store and manage large volumes of data across commodity hardware clusters. It follows a master-slave architecture, comprising two main components: the NameNode and DataNode. NameNode: Acts as the master node and maintains metadata about file system namespaces, such as file names, permissions, and block locations. DataNode: Represents the slave nodes responsible for storing and managing the actual data blocks across the cluster's underlying storage devices. HDFS employs a block-based storage model, where large files are divided into fixed-size blocks (typically 128MB or 256MB) distributed across multiple DataNodes for fault tolerance and parallel processing. 2. Key Characteristics of HDFS: Fault Tolerance: HDFS ensures fault tolerance by replicating data blocks across multiple DataNodes, enabling data recovery in the event of node failures.   Scalability: HDFS scales horizontally to accommodate massive datasets by adding more nodes to the cluster, thereby increasing storage capacity and processing power. Data Locality: Leveraging data locality optimization, HDFS aims to process data where it resides, minimizing data movement across the network and enhancing performance. Streaming Data Access: HDFS supports sequential data access patterns, making it well-suited for batch processing workloads, such as MapReduce jobs. 3. Use Cases and Applications: HDFS serves as the foundation for various big data processing frameworks and applications within the Hadoop ecosystem, including Apache Spark, Apache Hive, and Apache HBase. It finds widespread adoption in diverse industries, ranging from financial services and healthcare to retail and telecommunications, for tasks such as log analysis, ETL (Extract, Transform, Load) operations, and machine learning model training. 4. Evolution and Advancements: Over the years, HDFS has undergone significant enhancements and optimizations to address evolving data engineering requirements, such as improved data consistency, performance optimizations, and support for heterogeneous storage tiers (e.g., SSDs and HDDs). Additionally, advancements in the Hadoop ecosystem, such as HDFS Federation and Erasure Coding, have further augmented HDFS's capabilities in terms of scalability, reliability, and storage efficiency. Stay tuned for more insights as we near the culmination of our 100-day data engineering journey! [ An engaging read: https://lnkd.in/g46gsqzc ] #HDFS #HadoopFileSystem #DataStorage #BigData #Recap #100daysofdataengineering #day96

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🔢 **Data Types in SQL: A Quick Laugh** 🤓 In the quirky world of SQL, choosing the right data type is like picking your favorite ice cream flavor: - **INTEGER**: The no-nonsense vanilla—just good old whole numbers, no frills. - **VARCHAR**: The adventurous sprinkles—perfect for variable-length strings. Sometimes you need that extra bit of flavor! - **DATE**: The classic chocolate fudge—dates are essential and add that sweet touch for tracking time. - **FLOAT**: The smooth caramel swirl—ideal for precision with a bit of flexibility. Just like choosing your ice cream, getting your data types right can make your SQL queries both effective and enjoyable! 🍦📝 Here’s a quick MySQL example to keep things cool: ```sql CREATE TABLE CricketStats ( PlayerName VARCHAR(50), Matches INTEGER, Runs FLOAT, DebutDate DATE ); INSERT INTO CricketStats (PlayerName, Matches, Runs, DebutDate) VALUES ('Virat Kohli', 112, 7547.65, '2008-08-18'), ('Steve Smith', 90, 7097.80, '2010-07-13'); ``` Want to try this out yourself? Head over to [DB-Fiddle](https://www.db-fiddle.com/) or [SQL Fiddle](http://sqlfiddle.com/) and give it a spin! #sql_with_preetam #sql #data_analytics

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🔢 **Data Types in SQL: A Quick Laugh** 🤓 In the quirky world of SQL, choosing the right data type is like picking your favorite ice cream flavor: - **INTEGER**: The no-nonsense vanilla—just good old whole numbers, no frills. - **VARCHAR**: The adventurous sprinkles—perfect for variable-length strings. Sometimes you need that extra bit of flavor! - **DATE**: The classic chocolate fudge—dates are essential and add that sweet touch for tracking time. - **FLOAT**: The smooth caramel swirl—ideal for precision with a bit of flexibility. Just like choosing your ice cream, getting your data types right can make your SQL queries both effective and enjoyable! 🍦📝 Here’s a quick MySQL example to keep things cool: ```sql CREATE TABLE CricketStats ( PlayerName VARCHAR(50), Matches INTEGER, Runs FLOAT, DebutDate DATE ); INSERT INTO CricketStats (PlayerName, Matches, Runs, DebutDate) VALUES ('Virat Kohli', 112, 7547.65, '2008-08-18'), ('Steve Smith', 90, 7097.80, '2010-07-13'); ``` Want to try this out yourself? Head over to [DB-Fiddle](https://www.db-fiddle.com/) or [SQL Fiddle](http://sqlfiddle.com/) and give it a spin! #sql_with_preetam #sql #data_analytics #data_types

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👉 Character Strings (Char, Varchar, Nchar, Nvarchar): These data types are used to store text data. Char data types store fixed-length strings, while Varchar data types can store strings of varying lengths. Nchar and Nvarchar are similar to Char and Varchar, but they are designed to store Unicode characters. 👉Numeric (bit, smallint, int, bigint, decimal, float): These data types are used to store numbers. There are different numeric data types to accommodate various ranges and precision requirements. For example, the bit data type can store only 0 or 1, while the bigint data type can store very large integers. 👉Date and Time (Date, Time, Datetime, Timestamp): These data types are used to store dates and times. The Date data type stores only the date, while the Time data type stores only the time. The Datetime and Timestamp data types store both the date and time. Binary (Binary, Varbinary, Varbinary(max)): These data types are used to store binary data, such as images or audio files. Binary data is stored as a sequence of bytes. 👉Miscellaneous (Clob, Blob, JSON, XML): These data types are used to store other types of data. For example, the Clob data type is used to store large amounts of character data, while the Blob data type is used to store large amounts of binary data. The JSON and XML data types are used to store data in JSON and XML format, respectively. #sql #Datascience

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🚀 **Unlocking SQL Secrets: Complex Joins and Subqueries!** 🚀 Hey Data Science Enthusiasts! 📊 Ready to level up your SQL game? Today, we're diving into the magic of Complex Joins and Subqueries using MySQL. 🎩✨ Imagine you run a quirky café with a customer loyalty program. You have two tables: 1. `Customers` - Who’s buying the coffee? ☕ 2. `Orders` - What’s brewing today? ☕ **Complex Joins** are like setting up a grand coffee party and figuring out who bought what. For instance, if you want to know which customers made more than 5 orders, you’d join these tables on the `customer_id` to combine customer info with their orders. Here’s how: ```sql SELECT Customers.name, COUNT(Orders.order_id) as order_count FROM Customers JOIN Orders ON Customers.customer_id = Orders.customer_id GROUP BY Customers.name HAVING COUNT(Orders.order_id) > 5; ``` **Subqueries** let you get fancy. Imagine you want a list of customers who only ordered a specific type of coffee. You’d first find those specific orders and then look up who made them: ```sql SELECT name FROM Customers WHERE customer_id IN ( SELECT customer_id FROM Orders WHERE coffee_type = 'Latte' ); ``` It's like finding the ultimate latte lover in your café! ☕💕 Want to explore more? Check out these sites for practice and examples: - **W3Schools SQL Tutorial**: [W3Schools SQL](https://lnkd.in/gZniqbtz) - **SQL Fiddle for Testing Queries**: [SQL Fiddle](http://sqlfiddle.com/) Happy querying, and may your data always be insightful! 🚀📈 #DataScience #SQL #MySQL #ComplexJoins #Subqueries #LearnSQL #DataAnalysis #sql_with_preetam

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🌟 Day 3 of My 100 Days Job Hunt Challenge! 🌟 Today was an eventful and productive day in my journey to secure a Data Scientist/ML Engineer role. Here are the highlights: 1) 30 Job Applications: Applied to 30 positions that align with my skills and experience. 2) 30 Personal Requests to Recruiters: Reached out to 30 recruiters with personalized connection requests to expand my network and explore opportunities. 3) Learning and Implementation: Focused on deepening my understanding of TensorFlow, Keras, and PyTorch. Implemented a neural network on the handwritten digits dataset, working with Sequential, Dense, and flattened layers. Balancing job applications with continuous learning is challenging, but I'm committed to staying focused and making the most of every opportunity. #JobHunt #Day3 #DataScience #MachineLearning #DeepLearning #TensorFlow #Keras #PyTorch #Networking #100DaysChallenge #CareerGrowth

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Day 80 - 100 Day Data Science Today, I wrapped up my project by building the SVM and KNN models over the 5G quality of service data set and then doing a comparative analysis of the KNN, SVM, and MLR models to find out which one is the most suitable for such kind of exploration. Today's work included: 1. Building SVM and KNN models 2. Predicting results with SVM and KNN models 3. Evaluating the performance of SVM and KNN models 4. Visualizing predicted results 5. Comparing the performance of multiple linear regression, SVM, and 6. KNN models With that, I wrapped up Day 80 and my project! See you tomo!! #datascience #dataanalytics #dataviusalisation #learningjourney #python #machinelearning

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👉 List: Mutability: Can be modified after creation. Order: Elements are stored in a specific order and can be accessed by index. Duplicate elements: Allows duplicate elements. Empty list: Created using square brackets []. Data types: Can store any data type, including other lists, tuples, dictionaries, and sets. 👉 Tuple: Mutability: Cannot be modified after creation. Order: Elements are stored in a specific order and can be accessed by index. Duplicate elements: Allows duplicate elements. Empty tuple: Created using parentheses (). Data types: Can store any data type, including other lists, tuples, dictionaries, and sets. 👉 Dictionary: Mutability: Can be modified after creation. Order: Elements are stored in a specific order (from Python 3.7 onwards). Duplicate keys: Does not allow duplicate keys. Empty dictionary: Created using curly braces {}. Data types: Keys must be immutable data types (like numbers, strings, or tuples), while values can be any data type. 👉 Set: Mutability: Can be modified after creation. Order: Elements are not stored in a specific order. Duplicate elements: Does not allow duplicate elements. Empty set: Created using curly braces with no elements set(). Data types: Elements must be immutable data types (like numbers, strings, or tuples). #Python #Datascience

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