Data Analytics is a wild career. One minute you're doing fancy product experimentation, statistics, and ML... and the next minute you're spending hours copying and pasting into an Excel doc while people tell you to hurry up.
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SQL Interview Question for #DataScience:
A company has provided sales data containing information about customer purchases, as shown in the table below.
Your task is to:
Calculate Total Revenue
Calculate Total Sales by Product
Find Top Customers by Revenue
Solve it using SQL
A company has provided sales data containing information about customer purchases, as shown in the table below.
Your task is to:
Calculate Total Revenue
Calculate Total Sales by Product
Find Top Customers by Revenue
Solve it using SQL
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Hi Guys,
Here are some of the telegram channels which may help you in data analytics journey 👇👇
SQL: https://t.iss.one/sqlanalyst
Power BI & Tableau: https://t.iss.one/PowerBI_analyst
Excel: https://t.iss.one/excel_analyst
Python: https://t.iss.one/dsabooks
Jobs: https://t.iss.one/jobs_SQL
Data Science: https://t.iss.one/datasciencefree
Artificial intelligence: https://t.iss.one/machinelearning_deeplearning
Data Engineering: https://t.iss.one/sql_engineer
Hope it helps :)
Here are some of the telegram channels which may help you in data analytics journey 👇👇
SQL: https://t.iss.one/sqlanalyst
Power BI & Tableau: https://t.iss.one/PowerBI_analyst
Excel: https://t.iss.one/excel_analyst
Python: https://t.iss.one/dsabooks
Jobs: https://t.iss.one/jobs_SQL
Data Science: https://t.iss.one/datasciencefree
Artificial intelligence: https://t.iss.one/machinelearning_deeplearning
Data Engineering: https://t.iss.one/sql_engineer
Hope it helps :)
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Future Trends in Artificial Intelligence 👇👇
1. AI in healthcare: With the increasing demand for personalized medicine and precision healthcare, AI is expected to play a crucial role in analyzing large amounts of medical data to diagnose diseases, develop treatment plans, and predict patient outcomes.
2. AI in finance: AI-powered solutions are expected to revolutionize the financial industry by improving fraud detection, risk assessment, and customer service. Robo-advisors and algorithmic trading are also likely to become more prevalent.
3. AI in autonomous vehicles: The development of self-driving cars and other autonomous vehicles will rely heavily on AI technologies such as computer vision, natural language processing, and machine learning to navigate and make decisions in real-time.
4. AI in manufacturing: The use of AI and robotics in manufacturing processes is expected to increase efficiency, reduce errors, and enable the automation of complex tasks.
5. AI in customer service: Chatbots and virtual assistants powered by AI are anticipated to become more sophisticated, providing personalized and efficient customer support across various industries.
6. AI in agriculture: AI technologies can be used to optimize crop yields, monitor plant health, and automate farming processes, contributing to sustainable and efficient agricultural practices.
7. AI in cybersecurity: As cyber threats continue to evolve, AI-powered solutions will be crucial for detecting and responding to security breaches in real-time, as well as predicting and preventing future attacks.
Like for more ❤️
Artificial Intelligence
1. AI in healthcare: With the increasing demand for personalized medicine and precision healthcare, AI is expected to play a crucial role in analyzing large amounts of medical data to diagnose diseases, develop treatment plans, and predict patient outcomes.
2. AI in finance: AI-powered solutions are expected to revolutionize the financial industry by improving fraud detection, risk assessment, and customer service. Robo-advisors and algorithmic trading are also likely to become more prevalent.
3. AI in autonomous vehicles: The development of self-driving cars and other autonomous vehicles will rely heavily on AI technologies such as computer vision, natural language processing, and machine learning to navigate and make decisions in real-time.
4. AI in manufacturing: The use of AI and robotics in manufacturing processes is expected to increase efficiency, reduce errors, and enable the automation of complex tasks.
5. AI in customer service: Chatbots and virtual assistants powered by AI are anticipated to become more sophisticated, providing personalized and efficient customer support across various industries.
6. AI in agriculture: AI technologies can be used to optimize crop yields, monitor plant health, and automate farming processes, contributing to sustainable and efficient agricultural practices.
7. AI in cybersecurity: As cyber threats continue to evolve, AI-powered solutions will be crucial for detecting and responding to security breaches in real-time, as well as predicting and preventing future attacks.
Like for more ❤️
Artificial Intelligence
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What's your favorite approach to learning new technologies?
1. Online courses
2. Tutorials and documentation
3. Books
4. Hands-on projects
5. Community forums and meetups
If any other, add in comments 👇👇
1. Online courses
2. Tutorials and documentation
3. Books
4. Hands-on projects
5. Community forums and meetups
If any other, add in comments 👇👇
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Here’s a detailed breakdown of critical roles and their associated responsibilities:
🔘 Data Engineer: Tailored for Data Enthusiasts
1. Data Ingestion: Acquire proficiency in data handling techniques.
2. Data Validation: Master the art of data quality assurance.
3. Data Cleansing: Learn advanced data cleaning methodologies.
4. Data Standardisation: Grasp the principles of data formatting.
5. Data Curation: Efficiently organise and manage datasets.
🔘 Data Scientist: Suited for Analytical Minds
6. Feature Extraction: Hone your skills in identifying data patterns.
7. Feature Selection: Master techniques for efficient feature selection.
8. Model Exploration: Dive into the realm of model selection methodologies.
🔘 Data Scientist & ML Engineer: Designed for Coding Enthusiasts
9. Coding Proficiency: Develop robust programming skills.
10. Model Training: Understand the intricacies of model training.
11. Model Validation: Explore various model validation techniques.
12. Model Evaluation: Master the art of evaluating model performance.
13. Model Refinement: Refine and improve candidate models.
14. Model Selection: Learn to choose the most suitable model for a given task.
🔘 ML Engineer: Tailored for Deployment Enthusiasts
15. Model Packaging: Acquire knowledge of essential packaging techniques.
16. Model Registration: Master the process of model tracking and registration.
17. Model Containerisation: Understand the principles of containerisation.
18. Model Deployment: Explore strategies for effective model deployment.
These roles encompass diverse facets of Data and ML, catering to various interests and skill sets. Delve into these domains, identify your passions, and customise your learning journey accordingly.
🔘 Data Engineer: Tailored for Data Enthusiasts
1. Data Ingestion: Acquire proficiency in data handling techniques.
2. Data Validation: Master the art of data quality assurance.
3. Data Cleansing: Learn advanced data cleaning methodologies.
4. Data Standardisation: Grasp the principles of data formatting.
5. Data Curation: Efficiently organise and manage datasets.
🔘 Data Scientist: Suited for Analytical Minds
6. Feature Extraction: Hone your skills in identifying data patterns.
7. Feature Selection: Master techniques for efficient feature selection.
8. Model Exploration: Dive into the realm of model selection methodologies.
🔘 Data Scientist & ML Engineer: Designed for Coding Enthusiasts
9. Coding Proficiency: Develop robust programming skills.
10. Model Training: Understand the intricacies of model training.
11. Model Validation: Explore various model validation techniques.
12. Model Evaluation: Master the art of evaluating model performance.
13. Model Refinement: Refine and improve candidate models.
14. Model Selection: Learn to choose the most suitable model for a given task.
🔘 ML Engineer: Tailored for Deployment Enthusiasts
15. Model Packaging: Acquire knowledge of essential packaging techniques.
16. Model Registration: Master the process of model tracking and registration.
17. Model Containerisation: Understand the principles of containerisation.
18. Model Deployment: Explore strategies for effective model deployment.
These roles encompass diverse facets of Data and ML, catering to various interests and skill sets. Delve into these domains, identify your passions, and customise your learning journey accordingly.
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What kind of problems neural nets can solve?
Neural nets are good at solving non-linear problems. Some good examples are problems that are relatively easy for humans (because of experience, intuition, understanding, etc), but difficult for traditional regression models: speech recognition, handwriting recognition, image identification, etc.
Neural nets are good at solving non-linear problems. Some good examples are problems that are relatively easy for humans (because of experience, intuition, understanding, etc), but difficult for traditional regression models: speech recognition, handwriting recognition, image identification, etc.
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Future Trends in Artificial Intelligence 👇👇
1. AI in healthcare: With the increasing demand for personalized medicine and precision healthcare, AI is expected to play a crucial role in analyzing large amounts of medical data to diagnose diseases, develop treatment plans, and predict patient outcomes.
2. AI in finance: AI-powered solutions are expected to revolutionize the financial industry by improving fraud detection, risk assessment, and customer service. Robo-advisors and algorithmic trading are also likely to become more prevalent.
3. AI in autonomous vehicles: The development of self-driving cars and other autonomous vehicles will rely heavily on AI technologies such as computer vision, natural language processing, and machine learning to navigate and make decisions in real-time.
4. AI in manufacturing: The use of AI and robotics in manufacturing processes is expected to increase efficiency, reduce errors, and enable the automation of complex tasks.
5. AI in customer service: Chatbots and virtual assistants powered by AI are anticipated to become more sophisticated, providing personalized and efficient customer support across various industries.
6. AI in agriculture: AI technologies can be used to optimize crop yields, monitor plant health, and automate farming processes, contributing to sustainable and efficient agricultural practices.
7. AI in cybersecurity: As cyber threats continue to evolve, AI-powered solutions will be crucial for detecting and responding to security breaches in real-time, as well as predicting and preventing future attacks.
Like for more ❤️
Artificial Intelligence
1. AI in healthcare: With the increasing demand for personalized medicine and precision healthcare, AI is expected to play a crucial role in analyzing large amounts of medical data to diagnose diseases, develop treatment plans, and predict patient outcomes.
2. AI in finance: AI-powered solutions are expected to revolutionize the financial industry by improving fraud detection, risk assessment, and customer service. Robo-advisors and algorithmic trading are also likely to become more prevalent.
3. AI in autonomous vehicles: The development of self-driving cars and other autonomous vehicles will rely heavily on AI technologies such as computer vision, natural language processing, and machine learning to navigate and make decisions in real-time.
4. AI in manufacturing: The use of AI and robotics in manufacturing processes is expected to increase efficiency, reduce errors, and enable the automation of complex tasks.
5. AI in customer service: Chatbots and virtual assistants powered by AI are anticipated to become more sophisticated, providing personalized and efficient customer support across various industries.
6. AI in agriculture: AI technologies can be used to optimize crop yields, monitor plant health, and automate farming processes, contributing to sustainable and efficient agricultural practices.
7. AI in cybersecurity: As cyber threats continue to evolve, AI-powered solutions will be crucial for detecting and responding to security breaches in real-time, as well as predicting and preventing future attacks.
Like for more ❤️
Artificial Intelligence
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Hi guys,
So, today I am working on something interesting.
I am creating an youtube channel on songs.
What's special: These songs are AI Generated.
I'll ask you guys for feedback as well
Stay tuned 😄❤️
So, today I am working on something interesting.
I am creating an youtube channel on songs.
What's special: These songs are AI Generated.
I'll ask you guys for feedback as well
Stay tuned 😄❤️
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First song on youtube
👇👇
https://youtu.be/GrtZsXaZcCI?si=HPGd-cKx1Xm-lLa0
Share your views in the comments 😄
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https://youtu.be/GrtZsXaZcCI?si=HPGd-cKx1Xm-lLa0
Share your views in the comments 😄
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Data Science Projects
First song on youtube 👇👇 https://youtu.be/GrtZsXaZcCI?si=HPGd-cKx1Xm-lLa0 Share your views in the comments 😄
Which song language do you guys prefer?
Anonymous Poll
51%
English songs
33%
Hindi songs
3%
French
4%
Spanish
8%
Any other ( add in comments)
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A new song coming up specially dedicated to all data aspirants, hopefully you guys can relate with it 😄❤️
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Here is the next song - data dreams
👇👇
https://youtu.be/CxC0T9mpFL0?si=UxNq_ZGUlW4ZsrTD
Dedicated to all data lovers ❤️
Please like and share your comments on this 😁❤️
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https://youtu.be/CxC0T9mpFL0?si=UxNq_ZGUlW4ZsrTD
Dedicated to all data lovers ❤️
Please like and share your comments on this 😁❤️
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Tech stack for Machine Learning in 2024:
- ml workflow orchestrator: Kubeflow
- experiment tracking: MLflow
- data ingestion: Airbyte
- job orchestrator: Apache Airflow
- batch pipeline: Apache Spark
- message queue for real-time streaming: Apache Kafka
- feature engineering: Scikit-learn
- model selection and training: Pytorch
- hyperparameter tuning: Ray Tune
- model evaluation: Weights & Biases
- model monitoring: Grafana
- CI/CD: Github actions
- model versioning: neptune
- model serving: BentoML
- web app framework: Flask
- front-end: React
- feature store: Qwak
- Graph database: Neo4j
- Vector database: ChromaDB
- NoSQL database: MongoDB
- In-memory data store: Redis
...
What is your current ML tech stack?
- ml workflow orchestrator: Kubeflow
- experiment tracking: MLflow
- data ingestion: Airbyte
- job orchestrator: Apache Airflow
- batch pipeline: Apache Spark
- message queue for real-time streaming: Apache Kafka
- feature engineering: Scikit-learn
- model selection and training: Pytorch
- hyperparameter tuning: Ray Tune
- model evaluation: Weights & Biases
- model monitoring: Grafana
- CI/CD: Github actions
- model versioning: neptune
- model serving: BentoML
- web app framework: Flask
- front-end: React
- feature store: Qwak
- Graph database: Neo4j
- Vector database: ChromaDB
- NoSQL database: MongoDB
- In-memory data store: Redis
...
What is your current ML tech stack?
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