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Data Analytics Projects: 9 Project Ideas for Your Portfolio

Edureka

As a data analytics professional, building a strong portfolio of projects is essential to showcase your skills and expertise to potential employers. This article will discuss nine data analytics project ideas for your portfolio. This type of analysis is particularly relevant in industries such as manufacturing and logistics.

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Top 22 Data Science Applications That You Should Know

Knowledge Hut

Data processing can be done using statistical techniques, algorithms, scientific approaches, various technologies, etc. Manufacturing Process Optimization The distinction between the physical and digital worlds has become more ambiguous due to data science applications in industrial industries.

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50 ML Projects To Strengthen Your Portfolio and Get You Hired

ProjectPro

Projects help you create a strong foundation of various machine learning algorithms and strengthen your resume. Each project explores new machine learning algorithms, datasets, and business problems. In this ML project, you will learn to implement the random forest regressor and Xgboost algorithms to train the model.

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Meta’s Evenstar is transitioning to OCP to accelerate open RAN adoption

Engineering at Meta

Evenstar’s firmware, which has also been contributed to OCP and open sourced, handles complex signal processing algorithms such as digital pre-distortion (DPD) and crest factor reduction (CFR). All modules have been designed, manufactured, and tested in the lab on an individual basis for viability.

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Top 16 Tips For a Software Developer

Knowledge Hut

Build A Portfolio Of Your Software Development Work As you practice programming languages, making a portfolio of your work is one of the foremost software developer tips for beginners. The portfolio should have a short biography, contact information, relevant skills, a link to a resume, awards, and professional and personal projects.

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Top 6 Data Science Projects in Python

Knowledge Hut

Whether you are a student or a working professional, the journey to be an industry-ready Data Scientist would never be fulfilled unless there is a good portfolio of projects considerably demonstrated in the resume. Additionally having features like sensor type, brand, manufacturer, buying date, etc., to detect anomalies.

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10 Practical Generative AI Examples to be More Productive

Edureka

Recognising Patterns: The algorithm then recognises patterns and relationships between various data sets based on all the retrieved training data. Getting Trained on Data: To perform any task, first, the generative AI models need to be trained on massive datasets of existing content.