Technology

Science and Nature

Data Science, AI, and Blockchain

By Ekaaksh Deshpande

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Length5h 26m

About this audiobook

"Data Science, AI, and Blockchain: Integrated Approaches" emerges as a beacon for undergraduate students navigating the intricate landscapes of these transformative technologies. Our primary objective is to empower students with a comprehensive understanding of the synergy between Data Science, Artificial Intelligence (AI), and Blockchain, recognizing them as pivotal forces propelling innovation across diverse industries. We begin with Data Science, centered on extracting knowledge and insights from vast datasets, navigating through fundamental principles, methodologies, and tools. Real-world applications illustrate the significance of data-driven decision-making. Seamlessly moving into Artificial Intelligence, the book demystifies the algorithms underpinning intelligent systems. By weaving together theoretical concepts with practical examples, students gain insights into machine learning, natural language processing, and computer vision. Ethical considerations accompany the exploration, urging students to contemplate societal impacts. The exploration culminates in Blockchain, a revolutionary technology disrupting traditional notions of trust and transparency. Students understand how Blockchain secures transactions, empowers smart contracts, and transforms industries. Practical insights into building decentralized applications (DApps) are provided. Interactive elements, case studies, and exercises engage students actively. By fostering a multidisciplinary approach, we aim to equip undergraduates with the knowledge and skills needed to thrive in a world where the convergence of Data Science, AI, and Blockchain is reshaping the future.

Artificial Intelligence Futuristic Exploration

Audiobook details

GenreTechnology, Science and Nature

Length5 hrs 26 mins

Narrated byListen with 1,000+ voices

FormateBook with Audio

Publish dateFeb 20, 2025

LanguageEnglish

1CHAPTER 1 Introduction to Data Science

4912.2 Early platform examples: Ethereum, Hyperledger

21.1 Defining Data Science

5012.3 Smart Contract Programming

31.2 Evolution of Data Science

5112.4 Scalability Challenges

41.3 Data Science Process and Techniques

52CHAPTER 13 Blockchain Challenges and Limitations

51.4 Applications of Data Science

5313.1 Energy Consumption Concerns

Show all chapters

6CHAPTER 2 Data Collection and Management

5413.2 Low Transaction Throughput

72.1 Data Sources and Formats

5513.3 Private Key Management

82.2 Data Cleaning and Preprocessing

5613.4 Regulation and Compliance

9CHAPTER 3 Exploratory Data Analysis

5713.5 Privacy and Security Issues

10Exploratory Data Analysis

5813.6 Legal and Compliance Risks

11Data Visualization

59CHAPTER 14 Careers in Data Science

12Exploratory Data Analysis

6014.1 Data Scientist

13Identifying Patterns and Outliers

6114.2 Data Engineer

14CHAPTER 4 Statistical Inference and Modeling

6214.3 Business Analyst

154.1 Probability Distributions

6314.4 Data Journalist

164.2 Statistical Hypothesis Testing

6414.5 Research Scientist

174.3 Regression Analysis

65CHAPTER 15 Careers in AI

184.4 Machine Learning Models

6615.1 AI Researcher

19CHAPTER 5 Big Data Analytics

6715.2 Machine Learning Engineer

205.1 Characteristics of Big Data

6815.3 Computer Vision Engineer

215.2 Distributed Systems and Big Data Frameworks

69CHAPTER 16 Careers in Blockchain

225.3 Real-time and Stream Analytics

7016.1 Cryptocurrency Developer

235.4 Cloud Computing and Storage

7116.2 Blockchain Engineer

24CHAPTER 6 Data Mining and Machine Learning

7216.3 Smart Contracts Developer

256.1 Supervised vs Unsupervised Learning

7316.4 Blockchain Business Strategist

266.2 Classification, Regression, Clustering

7416.5 Blockchain Legal Expert

276.3 Bias-Variance Tradeoff

7516.6 Blockchain Project Manager

286.4 Model Evaluation Metrics

76CHAPTER 17 Building a Data Science Portfolio

29CHAPTER 7 Artificial Intelligence and Neural Networks

7717.1 Personal, Academic, and Work Projects

307.1 Introduction to Artificial Intelligence

7817.2 Open Source Contributions on GitHub

317.2 Neural Network Architectures and Training

7917.3 Writing Technical Blogs and Tutorials

32CHAPTER 8 Visualization and Communication

8017.5 Presentations at Meetings and Conferences

338.1 Data Visualization Principles and Tools

81CHAPTER 18 Lifelong Learning in Technology

348.2 Interactive Visualizations and Dashboards

8218.1 Learning Emerging Tools and Techniques

358.3 Data Storytelling and Reports

8318.2 Attending Conferences and Workshops

36CHAPTER 9 Introduction to Blockchain

8418.3 Participating in Meetups and Online Forums

379.1 Decentralization Using Blockchain

8518.4 Follow Thought Leaders on Social Media

389.2 Cryptography and Consensus Mechanisms

86CHAPTER 19 Ethics in Data Science, AI, and Blockchain

399.3 Smart Contracts and DApps

8719.1 Algorithmic Bias

40CHAPTER 10 Cryptocurrencies and Financial Services: 10.1 History of Bitcoin and Early Cryptocurrencies

8819.2 Data Privacy and Re-identification Risks

41CHAPTER 11 Blockchain Use Cases

8919.3 Transparency and Explainability

4211.1 Digital identity management

9019.4 Regulatory Compliance

4311.2 Healthcare records management

9119.5 Professional Codes of Conduct

4411.3 Real estate and land registry

92CHAPTER 20 Future Outlook for Data Science, AI, and Blockchain

4511.4 Voting and governance

9320.1 Emerging Trends and Innovations

4611.5 More industry examples

9420.2 Applications to Business, Government and Science

47CHAPTER 12 Blockchain Platforms and Architecture

95Glossary

4812.1 Public vs private blockchains

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