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Jobs | Internships | Placement | Interviews:
Swiggy is hiring Business Analyst 🚀

Min. Experience : 1 Year
Location : Bangalore

Apply link : https://careers.swiggy.com/#/careers?src=linkedin&p=eyJwYWdlVHlwZSI6ImpkIiwiY3ZTb3VyY2UiOiJsaW5rZWRpbiIsInJlcUlkIjoxODMwMiwicmVxdWVzdGVyIjp7ImlkIjoibGlua2VkaW4iLCJjb2RlIjpudWxsLCJuYW1lIjoiIn19&reqid=18302

All the best 👍👍


Micron Technology is hiring!
Position: HR Services Representative
Qualifications: Bachelor's degree/ Diploma
Experience: 1 - 2 (Years)
Location: Hyderabad, India

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📌Expro is hiring for Career Opportunities: Engineer 1
Experience: 0 - 2 year's
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𝐒𝐢𝐞𝐦𝐞𝐧𝐬 𝐢𝐬 𝐡𝐢𝐫𝐢𝐧𝐠 𝐅𝐫𝐞𝐬𝐡𝐞𝐫𝐬!
Position: Trainee
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Company name: DTCC
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Location: Hyderabad/Chennai

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Adobe is hiring!
Position: Software Development Engineer
Qualifications: Bachelor’s/ Master’s Degree
Salary: 13 LPA (Expected)
Experience: Freshers/ Experienced
Location: Bangalore, India

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Armada is hiring UI Engineer

For 2021, 2022, 2023, 2024, 2025 gards
Location: Thiruvananthapuram

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American Express is hiring Business Analyst 🚀

Qualification : Bachelor's degree
Experience : Freshers / Experienced
Location : Gurugram

Apply link : https://aexp.eightfold.ai/careers/job/28007370?hl=en&utm_source=linkedin&domain=aexp.com


All the best 👍👍

📌 Total jobs posted today - (15)

➖️➖️➖️➖️➖️➖️➖️➖️➖️➖️➖️➖️➖️

𝐂𝐨𝐠𝐧𝐢𝐳𝐚𝐧𝐭 𝐎𝐟𝐟 𝐂𝐚𝐦𝐩𝐮𝐬 𝐃𝐫𝐢𝐯𝐞!
Position: Business Analyst
Qualification: Bachelor’s/ Master’s/ MBA
Salary: 5 - 9 LPA (Expected)
Experience: Freshers/ Experienced
Location: Remote/ Office

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Philips is hiring!
Position: Software Engineer I - Medical imaging
Qualifications: Bachelor’s/ Master’s Degree
Salary: 8 - 12 LPA (Expected)
Experience: Entry Level
Location: Bangalore, India

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Company: Zupee
Role: Android Dev
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Company: Endur.fi
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Gartner is hiring Software Engineer (React JS & Python)
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Morgan Stanley is hiring Associate Software Engineer (Full stack)
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Real Page is hiring Database Engineer
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Altiar is hiring Quality Assurance Engineer

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Type of problem while solving DSA problem in Array

There are many types of problems that can be solved using arrays and different techniques in Data Structures and Algorithms. Here are some common problem types and techniques that you might encounter:

𝟏. 𝐒𝐥𝐢𝐝𝐢𝐧𝐠 𝐰𝐢𝐧𝐝𝐨𝐰 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are given an array and a window size, and you have to find a subarray of that size that satisfies certain conditions. You can use a sliding window technique to efficiently search through the array by maintaining a current window of fixed size and updating it as you move forward.

𝟐. 𝐓𝐰𝐨 𝐩𝐨𝐢𝐧𝐭𝐞𝐫 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you use two pointers to traverse the array from both ends and find a certain pattern or condition. For example, you can use two pointers to find a pair of elements that sum up to a target value, or to reverse an array.

𝟑. 𝐒𝐨𝐫𝐭𝐢𝐧𝐠 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are asked to sort an array in a certain way, such as in ascending or descending order, or according to certain criteria such as frequency or value. You can use sorting algorithms such as merge sort or quick sort to efficiently sort the array.


𝟒. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are asked to find a specific element in the array or to search for a certain pattern. You can use searching algorithms such as binary search or linear search to efficiently search through the array.

𝟓. 𝐒𝐮𝐛𝐚𝐫𝐫𝐚𝐲 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are asked to find a contiguous subarray that satisfies certain conditions. You can use techniques such as prefix sum or Kadane's algorithm to efficiently find the subarray with the maximum sum.


𝟔. 𝐂𝐨𝐮𝐧𝐭𝐢𝐧𝐠 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are asked to count the occurrences of certain elements or to count the number of subarrays or subsequences that satisfy certain conditions. You can use techniques such as hashing or dynamic programming to efficiently count the occurrences or number of subarrays.

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🚀 Advanced Data Science Frameworks for Cutting-Edge AI & Analytics 📊🤖

Data science is evolving with high-performance frameworks that enhance scalability, automation, and deep learning integration. Here are some of the most advanced frameworks shaping the future of data science:

🔹 High-Performance ML & AI Frameworks:

✅ H2O.ai – Open-source AutoML for scalable data science and deep learning
✅ Ray & Ray Tune – Distributed computing for hyperparameter tuning & reinforcement learning
✅ Dask-ML – Scalable machine learning with parallel computing on large datasets
✅ MLflow – End-to-end machine learning lifecycle management
✅ Kubeflow – AI & ML orchestration on Kubernetes for cloud-native deployment

🔹 Deep Learning & Advanced AI Frameworks:

✅ JAX – High-performance numerical computing for AI research
✅ TensorFlow Extended (TFX) – Production-ready ML pipeline automation
✅ FastAI – High-level deep learning framework built on PyTorch
✅ Horovod – Distributed deep learning for scaling AI models across GPUs & TPUs
✅ ONNX (Open Neural Network Exchange) – Model interoperability for deep learning across different frameworks

🔹 Data Processing & Big Data Frameworks:

✅ Apache Spark MLlib – Scalable machine learning for big data analytics
✅ Apache Flink – Real-time stream processing for large-scale AI applications
✅ Rapids.AI – GPU-accelerated data processing and ML
✅ Polars – Next-gen DataFrame library for high-speed data manipulation
✅ Delta Lake – Scalable data lakehouse architecture for analytics & AI

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✅ Optuna – Automated hyperparameter tuning with Bayesian optimization
✅ Auto-Sklearn & TPOT – Automated machine learning (AutoML) for optimized model selection
✅ Neptune.ai – Experiment tracking and model monitoring for ML workflows
✅ Featuretools – Automated feature engineering for predictive modeling
✅ Evidently AI – Real-time ML model monitoring and fairness analysis

🚀 The future of data science is automated, scalable, and AI-driven!
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