Denis Panjuta’s Post

Careers In Al Data Scientist - Core Skills: Python, R, Machine Learning, Data Visualization, Big Data technologies - Responsibilities: Develop models, mine and analyze data, translate business issues into data projects, communicate findings Data Analyst - Core Skills: SQL, Python, Data Wrangling, Statistical Analysis, Data Visualization tools - Responsibilities: Interpret data, create reports, identify trends, support decision-making Al Engineer - Core Skills: Programming, Al Frameworks, Robotics, NLP, Computer Vision - Responsibilities: Design Al models, develop systems, collaborate on improvements, integrate Al in business Machine Learning Engineer - Core Skills: Python, Scala, Machine Learning Algorithms, Deep Learning Frameworks, Big Data tools - Responsibilities: Design ML applications, conduct experiments, implement algorithms, collaborate on deployment Data Engineer - Core Skills: Database Management, ETL Tools, Big Data technologies, Data Warehousing - Responsibilities: Build data architecture, develop processes, assemble datasets, ensure business alignment If you found this helpful don't forget to save this for later and comment your thoughts. Join my newsletter or send me a DM "Newsletter" for more posts like this. Follow TechGear on Linkedin: credit to: Denis Panjuta

A clear outline of core skills and requirements for a role in AI.

Data Integrations Engineer https://ift.tt/ExAuIFj via ai-jobs.net - Your prime source for jobs and talents in AI/ML, Data Science and Big Data. 🧠🐱💻🤖🚗🚀 January 10, 2023 at 10:49AM . . . #dataengineer #datascience #python #datascientist #data #dataengineering #dataanalytics #bigdata #cat #machinelearning #dataanalysis #pythonprogramming #artificialintelligence #hadoop #devops #deeplearning #bigdataanalytics #datasciencetraining #cloudcomputing #datavisualization #computerscience #coding #dataanalyst #programminglife #bigdatatechnologies #ai #sql #pythonlearning #nosql #rprogramming

Want to become a better Data Scientist? 🧪 1. 📊 Enhance Data Visualization Skills: - Learn advanced plotting libraries (e.g., Matplotlib, Seaborn). - Practice creating clear and compelling visualizations. - Understand the art of storytelling through data graphics. 2. 💻 Strengthen Coding Proficiency: - Master programming languages like Python and R. - Explore version control systems (e.g., Git) for collaborative coding. - Participate in coding challenges to refine your skills. 3. 🤖 Dive into Deep Learning: - Gain expertise in deep neural networks and architectures. - Work on projects involving deep learning applications. - Stay updated on the latest advancements in the field. 4. 📉 Master Time Series Analysis: - Learn techniques for handling time-dependent data. - Understand seasonality, trends, and forecasting methods. - Apply time series analysis to real-world datasets. 5. 🧠 Develop Advanced Statistical Modeling: - Explore Bayesian statistics for robust modeling. - Learn about hierarchical modeling and mixed-effects models. - Apply statistical techniques to solve complex problems. 6. 🔄 Optimize Feature Engineering: - Understand the impact of feature selection on model performance. - Experiment with different feature engineering techniques. - Continuously iterate to improve the quality of input features. 7. 🌐 Deepen Domain Knowledge: - Immerse yourself in the industry you're working in. - Understand the specific challenges and nuances of the domain. - Tailor your analyses to align with domain-specific goals. 8. 🛠️ Excel in Data Preprocessing: - Develop skills in data cleaning and handling missing values. - Utilize feature scaling and normalization techniques. - Ensure data quality for accurate and reliable analysis. 9. 🤝 Sharpen Collaboration Skills: - Engage in cross-functional collaboration with domain experts. - Foster effective communication within interdisciplinary teams. - Understand the business needs and align data solutions accordingly. 10. 🚀 Stay Updated on Cloud Platforms: - Familiarize yourself with cloud services like AWS, Azure, or GCP. - Explore cloud-based data storage, processing, and machine learning tools. - Leverage the scalability and flexibility offered by cloud platforms.

Enterprise Analytics and Modeling - Data Science - Senior Associate - REF11736Z https://ift.tt/NAVOQ1v via ai-jobs.net - Your prime source for jobs and talents in AI/ML, Data Science and Big Data. 🧠🐱💻🤖🚗🚀 January 06, 2023 at 08:49PM . . . #datascience #machinelearning #python #artificialintelligence #ai #data #dataanalytics #bigdata #programming #coding #datascientist #technology #deeplearning #computerscience #datavisualization #analytics #pythonprogramming #tech #iot #dataanalysis #java #developer #programmer #business #ml #database #software #javascript #statistics #innovation

What’s the difference between a Machine Learning engineer and a Data scientist? Does one get paid more than other? Recently I got questions similar to this so I thought I’ll write a more detailed post for everyone. A lot of the times these terms are used interchangeably and might be confusing But there’s distinction which you can understand and see on the basis of roles, responsibilities and skills Responsibilities: • Data Scientist: Focuses on developing machine learning or deep learning solutions for various business problems. They may or may not create novel algorithms, depending on the context. • Machine Learning Engineer: Takes the models prepared by Data Scientists and deploys them to production. They handle model optimization and build MLOps infrastructure for deployment, monitoring, and management. 2. Skills: Both roles require programming skills, but Data Scientists emphasize more on data-related skills, while Machine Learning Engineers emphasize software engineering and deployment-related skills. 3. Tech Stack: • Data Scientist: Typically uses Python or R for programming, along with data analytics and visualization libraries like Scikit-learn, Pandas, and Matplotlib. • Machine Learning Engineer: Uses Python, but might also use languages like C++ or Scala for production-related tasks. Familiar with ML frameworks like TensorFlow, PyTorch, and serving tools like TFServing and ONNX. 4. Salary : on average an ML engineer gets paid more than a data scientist. But on an absolute scale both earn quite well Machine learning engineer or data scientist, these terms are used interchangeably and depending upon which type of company you’re working at you’ll hear one more than another. But knowing above can be crucial while deciding what kind of role suits you more #MachineLearning #DataScience #DataScientists #softwareengineering

🥊 Machine Learning Engineer vs. Data Scientist: The Ultimate Showdown! 🎬 In the red corner, we have the Machine Learning Engineer – the master of algorithms, the champion of code! 💻👨💻 In the blue corner, we have the Data Scientist – the sultan of statistics, the ruler of insights! 📊👩🔬 Who will come out on top in this battle of the brains? Let's break it down! 🧠 Machine Learning Engineer: - Builds and trains complex models using advanced programming skills 💪 - Focuses on creating efficient, scalable ML systems 🚀 - Implements algorithms and tunes hyperparameters for optimal performance 🎛️ - Collaborates with software engineers to integrate ML into products 🤝 Data Scientist: - Dives deep into data to uncover hidden patterns and insights 🔍 - Uses statistical methods to analyze and interpret data 📈 - Communicates findings to stakeholders and makes data-driven recommendations 📣 - Works closely with business teams to solve real-world problems 🌍 While both roles involve working with data and ML, the Machine Learning Engineer is like the architect 🏗️, designing and constructing the ML systems, while the Data Scientist is like the explorer 🧭, navigating the data landscape to discover valuable insights! In reality, these roles often overlap and collaborate closely 🤝, working together to harness the power of data and ML to drive innovation and solve complex challenges! 🌟 #MachineLearningEngineer #DataScientist #AIShowdown #DataScience

Data Engineer https://ift.tt/tTlf6vm via ai-jobs.net - Your prime source for jobs and talents in AI/ML, Data Science and Big Data. 🧠🐱💻🤖🚗🚀 January 23, 2023 at 02:05PM . . . #dataengineer #datascience #python #datascientist #data #dataengineering #dataanalytics #bigdata #cat #machinelearning #dataanalysis #pythonprogramming #artificialintelligence #hadoop #devops #deeplearning #bigdataanalytics #datasciencetraining #cloudcomputing #datavisualization #computerscience #coding #dataanalyst #programminglife #bigdatatechnologies #ai #sql #pythonlearning #nosql #rprogramming

Director, Data Engineering and Machine Learning https://ift.tt/jWsu2P6 via ai-jobs.net - Your prime source for jobs and talents in AI/ML, Data Science and Big Data. 🧠🐱💻🤖🚗🚀 January 30, 2023 at 09:34PM . . . #machinelearning #artificialintelligence #ai #datascience #python #technology #programming #deeplearning #coding #bigdata #computerscience #tech #data #iot #software #dataanalytics #pythonprogramming #developer #datascientist #javascript #programmer #java #innovation #ml #coder #robotics #webdevelopment #analytics #html #linux

Senior Big Data Engineer - MX https://ift.tt/d41LX2Z via ai-jobs.net - Your prime source for jobs and talents in AI/ML, Data Science and Big Data. 🧠🐱💻🤖🚗🚀 December 30, 2022 at 04:05AM . . . #dataengineer #datascience #python #datascientist #data #dataengineering #dataanalytics #bigdata #cat #machinelearning #dataanalysis #pythonprogramming #artificialintelligence #hadoop #devops #deeplearning #bigdataanalytics #datasciencetraining #cloudcomputing #datavisualization #computerscience #coding #dataanalyst #programminglife #bigdatatechnologies #ai #sql #pythonlearning #nosql #rprogramming