Data Science - Revision history

BPeat at 01:50, 27 April 2024

2024-04-27T01:50:43Z

BPeat at 03:00, 6 March 2024

2024-03-06T03:00:51Z

BPeat: /* ... and Data */

2023-09-18T16:10:13Z

‎... and Data

BPeat at 16:08, 18 September 2023

2023-09-18T16:08:18Z

BPeat: /* People, Process, Product... and Data */

2023-09-18T16:05:46Z

‎People, Process, Product... and Data

BPeat: /* Ground Truth */

2023-09-18T15:44:25Z

‎Ground Truth

BPeat at 13:46, 13 September 2023

2023-09-13T13:46:09Z

BPeat at 11:41, 17 August 2023

2023-08-17T11:41:32Z

BPeat at 01:06, 13 July 2023

2023-07-13T01:06:47Z

BPeat at 01:16, 11 July 2023

2023-07-11T01:16:49Z

@@ Line 28: / Line 28: @@
 * [[Backpropagation]] ... [[Feed Forward Neural Network (FF or FFNN)|FFNN]] ... [[Forward-Forward]] ... [[Activation Functions]] ...[[Softmax]] ... [[Loss]] ... [[Boosting]] ... [[Gradient Descent Optimization & Challenges|Gradient Descent]] ... [[Algorithm Administration#Hyperparameter|Hyperparameter]] ... [[Manifold Hypothesis]] ... [[Principal Component Analysis (PCA)|PCA]]
 * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]]
-* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless, Generators, Drag n' Drop]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
+* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless]] ... [[Hugging Face]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
 * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]]
 * [https://en.wikipedia.org/wiki/Data_science Data Science | Wikipedia]

@@ Line 29: / Line 29: @@
 * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]]
 * [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless, Generators, Drag n' Drop]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
-* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]]
 * [https://en.wikipedia.org/wiki/Data_science Data Science | Wikipedia]
 * [https://towardsdatascience.com/introduction-to-statistics-e9d72d818745 Data science concepts you need to know! Part 1 | Michael Barber - Towards Data Science]

@@ Line 55: / Line 55: @@
 * <b>Product</b>: the goods or services that a business offers to its customers. Managing products effectively involves developing high-quality products that meet customer needs and preferences, and continuously innovating to stay ahead of the competition.
-== ... and Data ==
 As businesses increasingly rely on data and AI technologies to drive growth and innovation, Marcus Lemonis' strategy can be enhanced by incorporating a stronger focus on data-driven decision-making and AI integration. Here's how his Three P's framework can be adapted to this new landscape:

@@ Line 42: / Line 42: @@
 <youtube>yqmiViOXlk8</youtube>
 <youtube>WrJWXxcxguc</youtube>
 = What is Data Science =
@@ Line 221: / Line 243: @@
 <img src="https://cdn-images-1.medium.com/max/800/1*z5VIYRsdFI-b8WPVyFPeWQ.png" width="1000">

← Older revision		Revision as of 16:05, 18 September 2023
Line 226:		Line 226:
	[https://www.google.com/search?q=ai+People+Proces+Product+Data ...Google search]		[https://www.google.com/search?q=ai+People+Proces+Product+Data ...Google search]

−	~~<img src="~~https://~~cdn~~-~~images~~-1.~~medium~~.~~com~~/~~max~~/~~800~~/~~1*z5VIYRsdFI~~-~~b8WPVyFPeWQ~~.~~png" width~~=~~"1000"~~>	+	* [https://marcuslemonis.com/business/3ps-of-business Marcus Lemonis Business Learning Center]
		+
		+	Marcus Lemonis businessman, television personality, and philanthropist, popularized the concept of the "Three P's of Business Success": People, Process, and Product. According to Lemonis, these three elements are the cornerstone of everything inside a business, and managing them effectively is critical to growing and succeeding in business. By focusing on these three key areas, businesses can improve their chances of success and growth. Marcus Lemonis uses these principles in his reality television show "The Profit," where he invests his own cash in struggling businesses and helps them turn around and succeed. Here's a breakdown of each of the Three P's:
		+
		+	* <b>People</b>: the employees, customers, and other stakeholders involved in the business. Managing people effectively involves hiring the right people, training them well, and creating a positive work environment that fosters productivity and innovation.
		+	* <b>Process</b>: the systems and procedures that a business uses to create and deliver its products or services. Managing processes effectively involves streamlining operations, eliminating waste, and continuously improving efficiency and quality.
		+	* <b>Product</b>: the goods or services that a business offers to its customers. Managing products effectively involves developing high-quality products that meet customer needs and preferences, and continuously innovating to stay ahead of the competition.
		+
		+	== ... and Data ==
		+
		+	As businesses increasingly rely on data and AI technologies to drive growth and innovation, Marcus Lemonis' strategy can be enhanced by incorporating a stronger focus on data-driven decision-making and AI integration. Here's how his Three P's framework can be adapted to this new landscape:
		+
		+	* <b>People</b>: In addition to understanding and managing employees and customers, businesses should also focus on leveraging <i>data</i> to gain insights into customer behavior, preferences, and trends. This can help in personalizing marketing efforts, improving customer experiences, and driving customer loyalty
		+	* <b>Process</b>: <i>Data</i> can play a crucial role in optimizing business processes. By collecting and analyzing <i>data</i>, businesses can identify bottlenecks, inefficiencies, and areas for improvement. AI technologies can be used to automate repetitive tasks, streamline operations, and enhance overall efficiency
		+	* <b>Product</b>: <i>Data</i> and AI can be used to inform product development and innovation. By analyzing market trends, customer feedback, and competitor insights, businesses can identify new product opportunities, optimize existing offerings, and stay ahead of the competition
		+
		+	<youtube>u2q-QF8TDGA</youtube>

← Older revision		Revision as of 15:44, 18 September 2023
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	[https://towardsdatascience.com/the-what-where-and-how-of-data-science-6dda1af98671 The What, Where and How of Data Science \| Iliya Valchanov]		[https://towardsdatascience.com/the-what-where-and-how-of-data-science-6dda1af98671 The What, Where and How of Data Science \| Iliya Valchanov]

−	<img src="https://cdn-images-1.medium.com/max/800/1*z5VIYRsdFI-b8WPVyFPeWQ.png" width="~~1200~~">	+	<img src="https://cdn-images-1.medium.com/max/800/1*z5VIYRsdFI-b8WPVyFPeWQ.png" width="1000">
		+
		+	= <span id="People, Process, Product... and Data"></span>People, Process, Product... and Data =
		+	[https://www.youtube.com/results?search_query=ai+People+Proces+Product+Data YouTube search...]
		+	[https://www.google.com/search?q=ai+People+Proces+Product+Data ...Google search]
		+
		+	<img src="https://cdn-images-1.medium.com/max/800/1*z5VIYRsdFI-b8WPVyFPeWQ.png" width="1000">

@@ Line 97: / Line 97: @@
 <youtube>D0B1JZMCMLo</youtube>
 <b>Data Science in 30 Minutes: Predicting Content Demand with Machine Learning
-</b><br>Netflix is well-known for its data-driven recommendations that seek to customize the user experience for every subscriber. But data science at Netflix extends far beyond that - from optimizing streaming and content caching to informing decisions about the TV shows and films available on the service. The talk covered work done by Becky and the Content Data Science team at Netflix, which seeks to evaluate where Netflix should spend their next content dollar using machine learning and [[Predictive Analytics|predictive models]].  The Data Incubator is a data science education company based in NYC, DC, and SF with both corporate training as well as recruiting services. For data science corporate training, we offer customized, in-house corporate training solutions in data and analytics. For data science hiring, we run a free 8 week fellowship training PhDs to become data scientists. The fellowship selects 2% of its 2000+ quarterly applicants and is free for Fellows. Hiring companies (including EBay, Capital One, Pfizer) pay a recruiting fee only if they successfully hire. You can read about us on Harvard Business Review, VentureBeat, or The Next Web, or read about our alumni at LinkedIn, Palantir or the NYTimes.  About the speakers:
+</b><br>Netflix is well-known for its data-driven recommendations that seek to customize the user experience for every subscriber. But data science at Netflix extends far beyond that - from optimizing streaming and content caching to informing decisions about the TV shows and films available on the service. The talk covered work done by Becky and the Content Data Science team at Netflix, which seeks to evaluate where Netflix should spend their next content dollar using machine learning and [[Predictive Analytics|predictive models]].  The Data Incubator is a data science education company based in NYC, DC, and SF with both corporate training as well as recruiting services. For data science corporate training, we offer customized, in-house corporate training solutions in data and analytics. For data science hiring, we run a free 8 week fellowship training PhDs to become data scientists. The fellowship selects 2% of its 2000+ quarterly applicants and is free for Fellows. Hiring companies (including EBay, Capital One, Pfizer) pay a recruiting fee only if they successfully hire. You can read about us on Harvard Business Review, VentureBeat, or The Next Web, or read about our alumni at LinkedIn, [[Palantir]] or the NYTimes.  About the speakers: Dr. Becky Tucker is a Senior Data Scientist at Netflix, a streaming media and entertainment company based in Los Gatos, CA. She holds a PhD in Physics from Caltech. At Netflix, Becky works on models that predict the demand for TV shows and movies. Michael Li founded The Data Incubator, a New York-based training program that turns talented PhDs from academia into workplace-ready data scientists and quants. The program is free to Fellows, employers engage with the Incubator as hiring partners.
 |}
 |}<!-- B -->

@@ Line 25: / Line 25: @@
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 * [[Risk, Compliance and Regulation]] ... [[Ethics]] ... [[Privacy]] ... [[Law]] ... [[AI Governance]] ... [[AI Verification and Validation]]
-* [[Excel]] ... [[LangChain#Documents|Documents]] ... [[Database]] ... [[Graph]] ... [[LlamaIndex]]
+* [[Excel]] ... [[LangChain#Documents|Documents]] ... [[Database|Database; Vector & Relational]] ... [[Graph]] ... [[LlamaIndex]]
 * [[Backpropagation]] ... [[Feed Forward Neural Network (FF or FFNN)|FFNN]] ... [[Forward-Forward]] ... [[Activation Functions]] ...[[Softmax]] ... [[Loss]] ... [[Boosting]] ... [[Gradient Descent Optimization & Challenges|Gradient Descent]] ... [[Algorithm Administration#Hyperparameter|Hyperparameter]] ... [[Manifold Hypothesis]] ... [[Principal Component Analysis (PCA)|PCA]]
 * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]]

@@ Line 22: / Line 22: @@
 * [[Data Science]] ... [[Data Governance|Governance]] ... [[Data Preprocessing|Preprocessing]] ... [[Feature Exploration/Learning|Exploration]] ... [[Data Interoperability|Interoperability]] ... [[Algorithm Administration#Master Data Management (MDM)|Master Data Management (MDM)]] ... [[Bias and Variances]] ... [[Benchmarks]] ... [[Datasets]]
 * [[Data Quality]] ...[[AI Verification and Validation|validity]], [[Evaluation - Measures#Accuracy|accuracy]], [[Data Quality#Data Cleaning|cleaning]], [[Data Quality#Data Completeness|completeness]], [[Data Quality#Data Consistency|consistency]], [[Data Quality#Data Encoding|encoding]], [[Data Quality#Zero Padding|padding]], [[Data Quality#Data Augmentation, Data Labeling, and Auto-Tagging|augmentation, labeling, auto-tagging]], [[Data Quality#Batch Norm(alization) & Standardization| normalization, standardization]], and [[Data Quality#Imbalanced Data|imbalanced data]]
-** [[Evaluation - Measures]]
+* [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]]
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 * [[Risk, Compliance and Regulation]] ... [[Ethics]] ... [[Privacy]] ... [[Law]] ... [[AI Governance]] ... [[AI Verification and Validation]]