Data Science for Value-Chain Management
How can you leverage data science to optimize operations and boost profitability?
Value Chain Management (VCM) refers to organizing activities that add value to the goods or services to achieve a competitive advantage in the marketplace.
This method helps organizations to effectively respond to market trends and improve efficiency to boost profitability.
We quickly delve into the fundamental components of Value Chain Management.
We will then explore four examples of data science applications to support strategic primary activities.
The value chain framework was originally introduced in Michael Porter's book “Competitive Advantage: Creating and Sustaining Superior Performance”.
This revolutionized how businesses perceive their operations by dissecting any business into a series of interconnected activities that contribute to creating and delivering value to customers.
How can you leverage data science to optimize operations and boost profitability?
Value Chain Management (VCM) refers to organizing activities that add value to the goods or services to achieve a competitive advantage in the marketplace.
This method helps organizations to effectively respond to market trends and improve efficiency to boost profitability.
We quickly delve into the fundamental components of Value Chain Management.
We will then explore four examples of data science applications to support strategic primary activities.
The value chain framework was originally introduced in Michael Porter's book “Competitive Advantage: Creating and Sustaining Superior Performance”.
This revolutionized how businesses perceive their operations by dissecting any business into a series of interconnected activities that contribute to creating and delivering value to customers.
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🌳 What is a Decision Tree? 🌳
Imagine you're trying to figure out what to eat for dinner. 🍕🥗🍔 A decision tree is like a flowchart that helps you make choices based on yes/no questions:
Are you in the mood for something light?
Yes ➡️ Salad 🥗
No ➡️ Are you craving something cheesy?
Yes ➡️ Pizza 🍕
No ➡️ Burger 🍔
That's the essence of how decision trees work in machine learning!
🤖 In Machine Learning Terms:
Nodes: Questions (e.g., Is the price > $50?)
Branches: Possible answers (e.g., Yes/No)
Leaves: Final decisions or predictions (e.g., "Expensive" or "Affordable")
📊 They're used for tasks like:
✅ Classifying emails as spam or not.
✅ Predicting if a customer will buy a product.
✅ Diagnosing diseases in healthcare.
🎯 Why are they Awesome?
Simple to understand (even for non-techies).
Visual and interpretable (you can see the logic behind predictions).
Great for small-to-medium datasets.
⚡️ Limitations:
They can "overfit" (become too specific).
Not the best for very large datasets or complex problems.
🛠 Pro Tip:
To handle overfitting, use Random Forests 🌲🌲 or Gradient Boosted Trees 🚀—advanced versions of decision trees.
What do you think about decision trees? Drop your 🌳 below if you love their simplicity!
Imagine you're trying to figure out what to eat for dinner. 🍕🥗🍔 A decision tree is like a flowchart that helps you make choices based on yes/no questions:
Are you in the mood for something light?
Yes ➡️ Salad 🥗
No ➡️ Are you craving something cheesy?
Yes ➡️ Pizza 🍕
No ➡️ Burger 🍔
That's the essence of how decision trees work in machine learning!
🤖 In Machine Learning Terms:
Nodes: Questions (e.g., Is the price > $50?)
Branches: Possible answers (e.g., Yes/No)
Leaves: Final decisions or predictions (e.g., "Expensive" or "Affordable")
📊 They're used for tasks like:
✅ Classifying emails as spam or not.
✅ Predicting if a customer will buy a product.
✅ Diagnosing diseases in healthcare.
🎯 Why are they Awesome?
Simple to understand (even for non-techies).
Visual and interpretable (you can see the logic behind predictions).
Great for small-to-medium datasets.
⚡️ Limitations:
They can "overfit" (become too specific).
Not the best for very large datasets or complex problems.
🛠 Pro Tip:
To handle overfitting, use Random Forests 🌲🌲 or Gradient Boosted Trees 🚀—advanced versions of decision trees.
What do you think about decision trees? Drop your 🌳 below if you love their simplicity!
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Begin to Use Cloud Computing with Anaconda Cloud Notebook
Begin to use Cloud Computing and Anaconda Cloud Notebook with Python, Data Science and Machine Learning [2024]
Rating ⭐️: 4.9 out 5
Students 👨🎓 : 1,028
Duration ⏰ : 40min on-demand video
Created by 👨🏫: Henrik Johansson
🔗 Course Link
#Data_Science
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Begin to use Cloud Computing and Anaconda Cloud Notebook with Python, Data Science and Machine Learning [2024]
Rating ⭐️: 4.9 out 5
Students 👨🎓 : 1,028
Duration ⏰ : 40min on-demand video
Created by 👨🏫: Henrik Johansson
🔗 Course Link
#Data_Science
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
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Udemy
Free Data Science Tutorial - Begin to Use Cloud Computing with Anaconda Cloud Notebook
Begin to use Cloud Computing and Anaconda Cloud Notebook with Python, Data Science and Machine Learning [2024] - Free Course
👍5👎1
🎉💯2024 Highly demanded Top 100+ IT Training courses FREE Giveaway in Networking, Project Management, Cloud and Cyber security including #CCNA 200-301, #CCNP 350-401 #Comptia, #PMP, #AWS, #Azure #Python, #Excel, #AI, #Google courses...... ⬇️📕
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✅Free Cisco #CCNA 200-301 Course - Gateway to IT Networking
Duration: 30+ hours 🔥 Cisco Tutor
🔗Link: https://bit.ly/3OUwvOW
✅AWS Training Course Ebook & Official Guide
🔗Link: https://bit.ly/3VDGWtY
✅ FREE #PMP Course to Help you be Project Manager
Duration: 30+ hours 🔥 PMI Tutor
🔗Link: https://bit.ly/3BvlSPB
🔗📝Download Free #IT Study Materials: https://bit.ly/3ZPcKyI
🔗📲Contact for 1v1 IT Certs Exam Help: https://wa.link/kjvvun
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Data Science
common data analysis and machine learning tasks using python
Creator: Ujjwal Karn
Stars ⭐️: 5.3k
Forked By: 1.5k
GithubRepo: https://github.com/ujjwalkarn/DataSciencePython
#datascience #python
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*This channel belongs to @bigdataspecialist group
common data analysis and machine learning tasks using python
Creator: Ujjwal Karn
Stars ⭐️: 5.3k
Forked By: 1.5k
GithubRepo: https://github.com/ujjwalkarn/DataSciencePython
#datascience #python
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Join @datascience_bds for more cool repositories.
*This channel belongs to @bigdataspecialist group
GitHub
GitHub - ujjwalkarn/DataSciencePython: common data analysis and machine learning tasks using python
common data analysis and machine learning tasks using python - ujjwalkarn/DataSciencePython
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Python for Deep Learning: Build Neural Networks in Python
Complete Deep Learning Course to Master Data science, Tensorflow, Artificial Intelligence, and Neural Networks
Rating ⭐️: 4.2 out 5
Students 👨🎓 : 145651
Duration ⏰ : 2 hours on-demand video
Created by 👨🏫: Meta Brains, school of AI
🔗 Course Link
⚠️ Its free for first 1000 enrollments only!
#python #deeplearning
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👉Join @bigdataspecialist for more👈
Complete Deep Learning Course to Master Data science, Tensorflow, Artificial Intelligence, and Neural Networks
Rating ⭐️: 4.2 out 5
Students 👨🎓 : 145651
Duration ⏰ : 2 hours on-demand video
Created by 👨🏫: Meta Brains, school of AI
🔗 Course Link
⚠️ Its free for first 1000 enrollments only!
#python #deeplearning
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Udemy
Python for Deep Learning: Build Neural Networks in Python
Complete Deep Learning Course to Master Data science, Tensorflow, Artificial Intelligence, and Neural Networks
👍2
𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬 vs 𝐆𝐫𝐚𝐩𝐡 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬
Selecting the right database depends on your data needs—vector databases excel in similarity searches and embeddings, while graph databases are best for managing complex relationships between entities.
𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬:
- Data Encoding: Vector databases encode data into vectors, which are numerical representations of the data.
- Partitioning and Indexing: Data is partitioned into chunks and encoded into vectors, which are then indexed for efficient retrieval.
- Ideal Use Cases: Perfect for tasks involving embedding representations, such as image recognition, natural language processing, and recommendation systems.
- Nearest Neighbor Searches: They excel in performing nearest neighbor searches, finding the most similar data points to a given query efficiently.
- Efficiency: The indexing of vectors enables fast and accurate information retrieval, making these databases suitable for high-dimensional data.
𝐆𝐫𝐚𝐩𝐡 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬:
- Relational Information Management: Graph databases are designed to handle and query relational information between entities.
- Node and Edge Representation: Entities are represented as nodes, and relationships between them as edges, allowing for intricate data modeling.
- Complex Relationships: They excel in scenarios where understanding and navigating complex relationships between data points is crucial.
- Knowledge Extraction: By indexing the resulting knowledge base, they can efficiently extract sub-knowledge bases, helping users focus on specific entities or relationships.
- Use Cases: Ideal for applications like social networks, fraud detection, and knowledge graphs where relationships and connections are the primary focus.
𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧:
Choosing between a vector and a graph database depends on the nature of your data and the type of queries you need to perform. Vector databases are the go-to choice for tasks requiring similarity searches and embedding representations, while graph databases are indispensable for managing and querying complex relationships.
Source: Ashish Joshi
Selecting the right database depends on your data needs—vector databases excel in similarity searches and embeddings, while graph databases are best for managing complex relationships between entities.
𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬:
- Data Encoding: Vector databases encode data into vectors, which are numerical representations of the data.
- Partitioning and Indexing: Data is partitioned into chunks and encoded into vectors, which are then indexed for efficient retrieval.
- Ideal Use Cases: Perfect for tasks involving embedding representations, such as image recognition, natural language processing, and recommendation systems.
- Nearest Neighbor Searches: They excel in performing nearest neighbor searches, finding the most similar data points to a given query efficiently.
- Efficiency: The indexing of vectors enables fast and accurate information retrieval, making these databases suitable for high-dimensional data.
𝐆𝐫𝐚𝐩𝐡 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬:
- Relational Information Management: Graph databases are designed to handle and query relational information between entities.
- Node and Edge Representation: Entities are represented as nodes, and relationships between them as edges, allowing for intricate data modeling.
- Complex Relationships: They excel in scenarios where understanding and navigating complex relationships between data points is crucial.
- Knowledge Extraction: By indexing the resulting knowledge base, they can efficiently extract sub-knowledge bases, helping users focus on specific entities or relationships.
- Use Cases: Ideal for applications like social networks, fraud detection, and knowledge graphs where relationships and connections are the primary focus.
𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧:
Choosing between a vector and a graph database depends on the nature of your data and the type of queries you need to perform. Vector databases are the go-to choice for tasks requiring similarity searches and embedding representations, while graph databases are indispensable for managing and querying complex relationships.
Source: Ashish Joshi
👍8❤4
Data Science Full Course For Beginners
⏰ 24 hours long
Created by IBM ✅
https://www.youtube.com/watch?v=WlLgysXJ0Ec
#datascience
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👉Join @datascience_bds for more👈
⏰ 24 hours long
Created by IBM ✅
https://www.youtube.com/watch?v=WlLgysXJ0Ec
#datascience
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YouTube
Data Science Full Course - Complete Data Science Course | Data Science Full Course For Beginners IBM
⭐⭐⭐⭐🕑TIME STAMP IS IN THE COMMENTS SECTION📋⭐⭐⭐⭐⭐
What you'll learn
✅Master the most up-to-date practical skills and knowledge that data scientists use in their daily roles
✅Learn the tools, languages, and libraries used by professional data scientists, including…
What you'll learn
✅Master the most up-to-date practical skills and knowledge that data scientists use in their daily roles
✅Learn the tools, languages, and libraries used by professional data scientists, including…
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🔥 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬 𝐒𝐢𝐦𝐩𝐥𝐢𝐟𝐢𝐞𝐝! 🔥
🚀 1. Array – Fixed-size collection of elements, perfect for fast lookups!
📦 2. Queue – First in, first out (FIFO). Think of a line at a grocery store!
🌳 3. Tree – Hierarchical structure, great for databases and file systems!
📊 4. Matrix – 2D representation, widely used in image processing and graphs!
🔗 5. Linked List – A chain of nodes, efficient for insertions & deletions!
🔗 6. Graph – Represents relationships, used in social networks & maps!
📈 7. Heap (Max/Min) – Optimized for priority-based operations!
🗂 8. Stack – Last in, first out (LIFO). Undo/Redo in action!
🔡 9. Trie – Best for search & autocomplete functionalities!
🔑 10. HashMap & HashSet – Fast lookups, perfect for key-value storage!
Understanding these will make you a better problem solver & efficient coder! 💡
🚀 1. Array – Fixed-size collection of elements, perfect for fast lookups!
📦 2. Queue – First in, first out (FIFO). Think of a line at a grocery store!
🌳 3. Tree – Hierarchical structure, great for databases and file systems!
📊 4. Matrix – 2D representation, widely used in image processing and graphs!
🔗 5. Linked List – A chain of nodes, efficient for insertions & deletions!
🔗 6. Graph – Represents relationships, used in social networks & maps!
📈 7. Heap (Max/Min) – Optimized for priority-based operations!
🗂 8. Stack – Last in, first out (LIFO). Undo/Redo in action!
🔡 9. Trie – Best for search & autocomplete functionalities!
🔑 10. HashMap & HashSet – Fast lookups, perfect for key-value storage!
Understanding these will make you a better problem solver & efficient coder! 💡
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𝐔𝐬𝐢𝐧𝐠 𝐁𝐢𝐠-𝐎 𝐢𝐧 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬 𝐚𝐧𝐝 𝐄𝐯𝐞𝐫𝐲𝐝𝐚𝐲 𝐋𝐢𝐟𝐞.
Big-O notation is a mathematical notation that is used to describe the performance or complexity of an algorithm, specifically how long an algorithm takes to run as the input size grows.
Understanding Big-O notation is essential for software engineers, as it allows them to analyze and compare the efficiency of different algorithms and make informed decisions about which one to use in a given situation.
Here are famous Big-O notations with examples.
Big-O notation is a mathematical notation that is used to describe the performance or complexity of an algorithm, specifically how long an algorithm takes to run as the input size grows.
Understanding Big-O notation is essential for software engineers, as it allows them to analyze and compare the efficiency of different algorithms and make informed decisions about which one to use in a given situation.
Here are famous Big-O notations with examples.
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Database.png
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𝐇𝐨𝐰 𝐭𝐨 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞?
Here are some of the top ways to improve database performance:
1. Indexing
Create the right indexes based on query patterns to speed up data retrieval.
2. Materialized Views
Store pre-computed query results for quick access, reducing the need to process complex queries repeatedly.
3. Vertical Scaling
Increase the capacity of the hashtag#database server by adding more CPU, RAM, or storage.
Here are some of the top ways to improve database performance:
1. Indexing
Create the right indexes based on query patterns to speed up data retrieval.
2. Materialized Views
Store pre-computed query results for quick access, reducing the need to process complex queries repeatedly.
3. Vertical Scaling
Increase the capacity of the hashtag#database server by adding more CPU, RAM, or storage.
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