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Understanding Language Models: The Power of ChatGPT Explained | 360DigiTMG 👇 SUBSCRIBE TO 360DigiTMG’s YOUTUBE CHANNEL NOW 👇https://www.youtube.com/c/360DigiTMGWe have specificall...

Interview with Sukumar | Placed at HCL | Senior Consultant in Data Science | 360DigiTMG 👇 SUBSCRIBE TO 360DigiTMG’s YOUTUBE CHANNEL NOW 👇https://www.youtube.com/c/3...

Interview with Nikhil Miryala | Collinson | Data Scientist | 360DigiTMG 👇 SUBSCRIBE TO 360DigiTMG’s YOUTUBE CHANNEL NOW 👇https://www.youtube.com/c/360DigiTMGW...

https://youtube.com/shorts/Bc5wa0i_fbg?si=yBRXtsl_nQmLP04o

Interview with Vyshali | Placed at TECH MAHINDRA | ASSOCIATE ML ENGINEER | 360DigiTMG 👇 SUBSCRIBE TO 360DigiTMG’s YOUTUBE CHANNEL NOW 👇https://www.youtube.com/c/36...

SUCCESFUL JOB PLACEMENT - interview with Meghal Agarwal

Interview with Meghal Agarwal | Konverge AI | Data Engineer | 360DigiTMG 👇 SUBSCRIBE TO 360DigiTMG’s YOUTUBE CHANNEL NOW 👇https://www.youtube.com/c/360DigiTM...

Exclusively live know 370DigiTMG Youtube Channel

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Interview with Sarita | International Research Article Publication | 360DigiTMG 👇 SUBSCRIBE TO 360DigiTMG’s YOUTUBE CHANNEL NOW 👇https://www.youtube.com/c/360DigiTMGWe have specifically created a Facebook Group for all our Data Science...

GitHub and GitHub actions for machine learning

How Data science helps banking domain professionals

How Data science Banking domain professionals

How CHATGPT4 PROCESSES INFORMATION

Want to learn how to build Data Pipeline and Machine Learning Model Deployment using Azure Kubernetes Services (AKS) and Azure Machine Learning services❓

With great pride, we announce that 360DigiTMG is now a proud partner of Naan Mudhalvan, a Government of Tamil Nadu initiative to skill/upskill students and working professionals on all industry-required skills. As part of this Initiative, 360DigiTMG signed an MoU to train Participants in Emerging Trends & Data related courses. Me exchanging the MoU in the presence of CM of Tamil Nadu Mr. Stalin.

Watch https://lnkd.in/g85rg_Ew

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Job Oppurtunities in Data Science - Domain Related

Data scientist for healthcare: A data scientist for healthcare is responsible for analyzing medical data to improve patient outcomes, optimize hospital operations, and develop new medical treatments.

Data scientist for marketing: A data scientist for marketing is responsible for analyzing customer data to identify consumer trends, develop targeted marketing campaigns, and improve customer engagement.

Data scientist for finance: A data scientist for finance is responsible for analyzing financial data to identify investment opportunities, manage risk, and develop financial models.

Data scientist for cybersecurity: A data scientist for cybersecurity is responsible for analyzing data to identify threats and vulnerabilities, develop predictive models, and create strategies to protect an organization's data and infrastructure.

Data journalist: A data journalist is responsible for collecting, analyzing, and visualizing data to uncover news stories and trends in various fields such as politics, economics, or social issues.

Data scientist for supply chain: A data scientist for supply chain is responsible for optimizing supply chain processes using data analysis to improve efficiency, reduce costs, and enhance customer experience.

Data scientist for energy: A data scientist for energy is responsible for analyzing energy usage data to improve energy efficiency, identify opportunities for energy savings, and develop renewable energy solutions.

Data scientist for transportation: A data scientist for transportation is responsible for analyzing transportation data to optimize transportation networks, reduce congestion, and improve safety.

Data scientist for sports: A data scientist for sports is responsible for analyzing sports data to improve athlete performance, develop game strategies, and create new ways of engaging fans.

Data scientist for environmental science: A data scientist for environmental science is responsible for analyzing environmental data to identify patterns and trends, develop predictive models, and inform environmental policy decisions.

Data scientist for education: A data scientist for education is responsible for analyzing student data to identify learning trends, develop personalized learning strategies, and evaluate the effectiveness of educational programs.

Data scientist for gaming: A data scientist for gaming is responsible for analyzing user data to understand player behavior, optimize game design, and improve player engagement and retention.

Data scientist for e-commerce: A data scientist for e-commerce is responsible for analyzing customer data to understand shopping behavior, optimize product recommendations, and improve the overall customer experience.

Data scientist for social media: A data scientist for social media is responsible for analyzing social media data to understand user behavior, optimize advertising campaigns, and improve social media engagement.

Data scientist for artificial intelligence: A data scientist for artificial intelligence is responsible for designing and developing AI algorithms and models, such as natural language processing or computer vision, to solve complex business problems.

Data scientist for image recognition: A data scientist for image recognition is responsible for developing and optimizing computer vision algorithms to analyze images and video data for applications such as autonomous vehicles or security systems.

Data scientist for finance and risk management: A data scientist for finance and risk management is responsible for analyzing financial data to develop risk models and inform investment decisions.

Data scientist for human resources: A data scientist for human resources is responsible for analyzing employee data to improve hiring processes, develop retention strategies, and inform workforce planning decisions.

Job Oppurtunities in Data Science

Data science is a rapidly growing field, and there is a high demand for skilled data scientists across a range of industries. Here are some examples of job opportunities in data science:

Data scientist: A data scientist is responsible for analyzing and interpreting complex data using statistical and machine learning techniques to derive insights and inform business decisions.

Data analyst: A data analyst is responsible for collecting, processing, and performing statistical analyses on data to identify patterns and trends and create reports for stakeholders.

Business intelligence analyst: A business intelligence analyst is responsible for using data to help organizations make informed business decisions by designing and maintaining data models, creating reports and dashboards, and conducting data analysis.

Data engineer: A data engineer is responsible for building and maintaining data pipelines and databases to ensure that data can be effectively collected, stored, and analyzed.

Machine learning engineer: A machine learning engineer is responsible for designing and implementing machine learning models to solve business problems, working closely with data scientists and data engineers to develop and deploy models.

Data visualization specialist: A data visualization specialist is responsible for designing and creating visual representations of data to help stakeholders understand complex information and make informed decisions.

Data governance specialist: A data governance specialist is responsible for ensuring that data is accurate, complete, and secure, and that data policies and procedures are followed.

Natural language processing (NLP) specialist: An NLP specialist is responsible for developing and optimizing algorithms that allow computers to understand and process human language, enabling applications such as chatbots or voice assistants.

Big data engineer: A big data engineer is responsible for designing and implementing large-scale data processing systems using technologies such as Hadoop or Spark.

who can study Data Science ?

Data science is a field that is open to anyone who has an interest in working with data and the necessary skills to analyze it. While a background in mathematics, statistics, or computer science can be helpful, it is not strictly necessary to have a degree in these fields to become a data scientist.

Here are some examples of individuals who may be interested in studying data science:

Recent college graduates: Data science is a growing field, and many recent college graduates are choosing to pursue careers in this area.

Working professionals: Individuals who are looking to transition to a career in data science can take advantage of online courses, boot camps, or other training programs to learn the necessary skills.

Business professionals: Business professionals who want to improve their decision-making by using data can benefit from learning data science skills.

Researchers: Researchers in fields such as healthcare, social sciences, and environmental sciences can benefit from data science skills to help them analyze large datasets and identify patterns and trends.

Data analysts: Individuals working in related fields such as business intelligence, data analysis, and data engineering can expand their skill set by learning data science techniques.

In summary, anyone with an interest in working with data and the necessary skills can study data science, regardless of their background or career goals.

What is the difference between Data Science and Ai ?

Data science and artificial intelligence (AI) are related fields that share some similarities but are not the same thing.

Data science involves using statistical and computational techniques to extract insights and knowledge from data. It encompasses the entire process of data analysis, from collecting and cleaning data to developing predictive models and communicating insights.

AI, on the other hand, is a branch of computer science that focuses on developing systems that can perform tasks that typically require human intelligence, such as recognizing speech or images, playing games, and making decisions. AI often involves using machine learning algorithms that can learn from data to improve their performance on a given task.

While data science can be a key component of AI systems, AI involves more than just data analysis. AI systems often require a combination of data science, computer science, and domain-specific expertise to develop and deploy effectively. Additionally, data science can be used for a wide range of applications beyond AI, such as business analytics, social science research, and healthcare analytics, whereas AI typically refers to systems that can perform intelligent tasks autonomously.

Common Questions which people have on data science has been answered , so that it will be helpfull for the Aspirants

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