What do you want to become?
Anonymous Poll
59%
Data Scientist
26%
Data Analyst
3%
Data Engineer
6%
Software Engineer
6%
Own business
Codvo hiring AI Senior Technical Lead
Apply link: https://www.linkedin.com/jobs/view/4202850511
Easy Apply through LinkedIn ๐
Apply link: https://www.linkedin.com/jobs/view/4202850511
Easy Apply through LinkedIn ๐
Linkedin
Codvo.ai hiring AI Senior Technical Lead (Remote) in Hyderabad, Telangana, India | LinkedIn
Posted 3:13:01 PM. Company Overview: At Codvo, software and people transformations go hand-in-hand. We are a globalโฆSee this and similar jobs on LinkedIn.
๐1
6โฃk completed, thanks everyone for the love & support โค๏ธ
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Data Scientist Job at Impetus: https://www.linkedin.com/jobs/view/4203764847
Linkedin
Impetus hiring Data Scientist in Gurugram, Haryana, India | LinkedIn
Posted 7:10:26 AM. About Impetus
Impetus Technologies is a digital engineering company focused on delivering expertโฆSee this and similar jobs on LinkedIn.
Impetus Technologies is a digital engineering company focused on delivering expertโฆSee this and similar jobs on LinkedIn.
Forwarded from Data Analyst Jobs
Actimise hiring Data Scientist
Location: Pune, India
Apply link: https://job-boards.eu.greenhouse.io/nice/jobs/4567868101?gh_jid=4567868101&gh_src=ff5c4b52teu
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All the best ๐๐
Location: Pune, India
Apply link: https://job-boards.eu.greenhouse.io/nice/jobs/4567868101?gh_jid=4567868101&gh_src=ff5c4b52teu
๐WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J
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ยฉHow fresher can get a job as a data scientist?ยฉ
1. Education: Obtain a degree in a relevant field such as computer science, statistics, mathematics, or data science. Consider pursuing additional certifications or specialized courses in data science to enhance your skills.
2. Build a strong foundation: Develop a strong understanding of key concepts in data science such as statistics, machine learning, programming languages (such as Python or R), and data visualization.
3. Hands-on experience: Gain practical experience by working on projects, participating in hackathons, or internships. Building a portfolio of projects showcasing your data science skills can be beneficial when applying for jobs.
4. Networking: Attend industry events, conferences, and meetups to network with professionals in the field. Networking can help you learn about job opportunities and make valuable connections.
5. Apply for entry-level positions: Look for entry-level positions such as data analyst, research assistant, or junior data scientist roles to gain experience and start building your career in data science.
6. Prepare for interviews: Practice common data science interview questions, showcase your problem-solving skills, and be prepared to discuss your projects and experiences related to data science.
7. Continuous learning: Data science is a rapidly evolving field, so it's important to stay updated on the latest trends, tools, and techniques. Consider taking online courses, attending workshops, or joining professional organizations to continue learning and growing in the field.
1. Education: Obtain a degree in a relevant field such as computer science, statistics, mathematics, or data science. Consider pursuing additional certifications or specialized courses in data science to enhance your skills.
2. Build a strong foundation: Develop a strong understanding of key concepts in data science such as statistics, machine learning, programming languages (such as Python or R), and data visualization.
3. Hands-on experience: Gain practical experience by working on projects, participating in hackathons, or internships. Building a portfolio of projects showcasing your data science skills can be beneficial when applying for jobs.
4. Networking: Attend industry events, conferences, and meetups to network with professionals in the field. Networking can help you learn about job opportunities and make valuable connections.
5. Apply for entry-level positions: Look for entry-level positions such as data analyst, research assistant, or junior data scientist roles to gain experience and start building your career in data science.
6. Prepare for interviews: Practice common data science interview questions, showcase your problem-solving skills, and be prepared to discuss your projects and experiences related to data science.
7. Continuous learning: Data science is a rapidly evolving field, so it's important to stay updated on the latest trends, tools, and techniques. Consider taking online courses, attending workshops, or joining professional organizations to continue learning and growing in the field.
๐5
Forwarded from AI Jobs | Artificial Intelligence
ML Engineer - Jaipur
Product Organisation - Jaipur, Rajasthan (Hybrid)
https://peak.bamboohr.com/careers/370
Product Organisation - Jaipur, Rajasthan (Hybrid)
https://peak.bamboohr.com/careers/370
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Most Demanding Data Analytics Skills!
โณ Dive into the essential skills and tools that are shaping the future of data analytics. From SQL and Python to Tableau and PowerBI, discover which technologies are crucial for advancing your data analysis capabilities.
โณ Explore the importance of machine learning techniques like linear regression, logistic regression, SVM, decision trees, random forests, K-means, and K-nearest neighbors, and how they can enhance your analytical prowess.
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โณ Get a comprehensive overview of the skills and technologies that can propel your career forward and make you a standout in the competitive world of data analytics.
โณ Dive into the essential skills and tools that are shaping the future of data analytics. From SQL and Python to Tableau and PowerBI, discover which technologies are crucial for advancing your data analysis capabilities.
โณ Explore the importance of machine learning techniques like linear regression, logistic regression, SVM, decision trees, random forests, K-means, and K-nearest neighbors, and how they can enhance your analytical prowess.
โณ Understand why soft skills such as communication, collaboration, critical thinking, and creativity are just as important as technical skills in the data analytics field.
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Blue Matter is hiring Analytics Associate ๐
Min. Experience : 6 Months
Location : Gurugram
Apply link : https://in.talent.com/redirect?id=9afa158e5ca9&source=linkedin-premium&utm_source=partner&pos=5917&utm_medium=linkedin-premium&puid=3de7gadf3aeagadf3defgddbgdd8fadb3deb3aedgadf3de73ee33deg3ee33ced3ceffcdc3ee33defgdd93dec3decgdde3ded3dee3de93ded3de8ged39d&cg=talent
Min. Experience : 6 Months
Location : Gurugram
Apply link : https://in.talent.com/redirect?id=9afa158e5ca9&source=linkedin-premium&utm_source=partner&pos=5917&utm_medium=linkedin-premium&puid=3de7gadf3aeagadf3defgddbgdd8fadb3deb3aedgadf3de73ee33deg3ee33ced3ceffcdc3ee33defgdd93dec3decgdde3ded3dee3de93ded3de8ged39d&cg=talent
Forwarded from AI Jobs | Artificial Intelligence
Role: Data Scientist - GenAI
Experience: 2 - 4 Years
NP: Immediate - 60 days
Location: 6th Floor, Resulticks Edge Solution, Greems Road,
Thousand Lights, Chennai - 600006
Date : 12th April ( 10:00 AM - 12:30 PM )
Primary Skills:
GenAI, LLM, Python, Pandas, Flask, API, Cloud ( GCP, AWZ or Azure) &
Data Visualization
Contact person: Nivetha N - 9176250701 (n.nivetha@resulticks.com)
Experience: 2 - 4 Years
NP: Immediate - 60 days
Location: 6th Floor, Resulticks Edge Solution, Greems Road,
Thousand Lights, Chennai - 600006
Date : 12th April ( 10:00 AM - 12:30 PM )
Primary Skills:
GenAI, LLM, Python, Pandas, Flask, API, Cloud ( GCP, AWZ or Azure) &
Data Visualization
Contact person: Nivetha N - 9176250701 (n.nivetha@resulticks.com)
๐3
Forwarded from Python for Data Analysts
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https://jobs.careers.microsoft.com/global/en/job/1818159/Data-Scientist
Microsoft Hiring for Data Scientist Roles in US for all levels and locations!
Microsoft Hiring for Data Scientist Roles in US for all levels and locations!
Forwarded from AI Jobs | Artificial Intelligence
Zycus is hiring AI Engineer
For 2021, 2022, 2023, 2024 gards
Location: Multiple
https://zycus.sensehq.com/careers/jobs/56968
For 2021, 2022, 2023, 2024 gards
Location: Multiple
https://zycus.sensehq.com/careers/jobs/56968
Neural Networks and Deep Learning
Neural networks and deep learning are integral parts of artificial intelligence (AI) and machine learning (ML). Here's an overview:
1.Neural Networks: Neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized in layers: input layer, hidden layers, and output layer.
Each neuron receives input, processes it through an activation function, and passes the output to the next layer. Neurons in subsequent layers perform more complex computations based on previous layers' outputs.
Neural networks learn by adjusting weights and biases associated with connections between neurons through a process called training. This is typically done using optimization techniques like gradient descent and backpropagation.
2.Deep Learning : Deep learning is a subset of ML that uses neural networks with multiple layers (hence the term "deep"), allowing them to learn hierarchical representations of data.
These networks can automatically discover patterns, features, and representations in raw data, making them powerful for tasks like image recognition, natural language processing (NLP), speech recognition, and more.
Deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformer models have demonstrated exceptional performance in various domains.
3.Applications Computer Vision: Object detection, image classification, facial recognition, etc., leveraging CNNs.
Natural Language Processing (NLP) Language translation, sentiment analysis, chatbots, etc., utilizing RNNs, LSTMs, and Transformers.
Speech Recognition: Speech-to-text systems using deep neural networks.
4.Challenges and Advancements: Training deep neural networks often requires large amounts of data and computational resources. Techniques like transfer learning, regularization, and optimization algorithms aim to address these challenges.
LAdvancements in hardware (GPUs, TPUs), algorithms (improved architectures like GANs - Generative Adversarial Networks), and techniques (attention mechanisms) have significantly contributed to the success of deep learning.
5. Frameworks and Libraries: There are various open-source libraries and frameworks (TensorFlow, PyTorch, Keras, etc.) that provide tools and APIs for building, training, and deploying neural networks and deep learning models.
Join for more: https://t.me/machinelearning_deeplearning
Neural networks and deep learning are integral parts of artificial intelligence (AI) and machine learning (ML). Here's an overview:
1.Neural Networks: Neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized in layers: input layer, hidden layers, and output layer.
Each neuron receives input, processes it through an activation function, and passes the output to the next layer. Neurons in subsequent layers perform more complex computations based on previous layers' outputs.
Neural networks learn by adjusting weights and biases associated with connections between neurons through a process called training. This is typically done using optimization techniques like gradient descent and backpropagation.
2.Deep Learning : Deep learning is a subset of ML that uses neural networks with multiple layers (hence the term "deep"), allowing them to learn hierarchical representations of data.
These networks can automatically discover patterns, features, and representations in raw data, making them powerful for tasks like image recognition, natural language processing (NLP), speech recognition, and more.
Deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformer models have demonstrated exceptional performance in various domains.
3.Applications Computer Vision: Object detection, image classification, facial recognition, etc., leveraging CNNs.
Natural Language Processing (NLP) Language translation, sentiment analysis, chatbots, etc., utilizing RNNs, LSTMs, and Transformers.
Speech Recognition: Speech-to-text systems using deep neural networks.
4.Challenges and Advancements: Training deep neural networks often requires large amounts of data and computational resources. Techniques like transfer learning, regularization, and optimization algorithms aim to address these challenges.
LAdvancements in hardware (GPUs, TPUs), algorithms (improved architectures like GANs - Generative Adversarial Networks), and techniques (attention mechanisms) have significantly contributed to the success of deep learning.
5. Frameworks and Libraries: There are various open-source libraries and frameworks (TensorFlow, PyTorch, Keras, etc.) that provide tools and APIs for building, training, and deploying neural networks and deep learning models.
Join for more: https://t.me/machinelearning_deeplearning
Telegram
Artificial Intelligence
๐ฐ Machine Learning & Artificial Intelligence Free Resources
๐ฐ Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more
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Forwarded from Python for Data Analysts
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Airtel is hiring Growth Analyst ๐
Experience : 1+ Year
Location : Gurugram
Apply link : Check out this job at airtel: https://www.linkedin.com/jobs/view/4206005779
Experience : 1+ Year
Location : Gurugram
Apply link : Check out this job at airtel: https://www.linkedin.com/jobs/view/4206005779
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Want to build your tech career without breaking the bank?๐ฐ
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Company Name: Zycus
Job Role: AI Engineer
Location: Mumbai, Pune, Bengaluru
Apply Link: https://zycus.sensehq.com/careers/jobs/56968
Limited Time Opportunities.
Share with your friends, colleagues, and college groups!
Thank You โค๏ธ
Job Role: AI Engineer
Location: Mumbai, Pune, Bengaluru
Apply Link: https://zycus.sensehq.com/careers/jobs/56968
Limited Time Opportunities.
Share with your friends, colleagues, and college groups!
Thank You โค๏ธ