Data Science and AI Overview

Definition: A subset of AI focused on developing algorithms that allow computers to learn from data.
Types:
- Supervised Learning: Uses labeled datasets to make predictions (e.g., regression, classification).
- Unsupervised Learning: Analyzes unlabeled data to identify patterns (e.g., clustering, association).
- Reinforcement Learning: Trains algorithms through rewards and penalties based on actions taken in an environment.
Applications: Fraud detection, recommendation systems, predictive analytics.

Purpose: Transforms complex data sets into visual representations to facilitate understanding and insights.
Tools:
- Tableau: Interactive dashboards and data exploration.
- Matplotlib/Seaborn: Libraries in Python for static, animated, and interactive visualizations.
- D3.js: JavaScript library for producing dynamic, data-driven documents.
Techniques:
- Charts: Bar charts, line graphs, scatter plots.
- Geospatial: Maps showing spatial data distributions.
- Dashboards: Real-time data monitoring.

Definition: Measures and policies designed to protect computer networks from unauthorized access and attacks.
Key Concepts:
- Firewalls: Hardware/software that filters incoming and outgoing traffic.
- Intrusion Detection Systems (IDS): Monitors network traffic for suspicious activity.
- Virtual Private Networks (VPN): Encrypts internet connections to secure data transmission.
Best Practices:
- Regular software updates and patches.
- Strong password policies and multifactor authentication.
- Network segmentation to limit access and reduce risk.

Definition: Authorized practice of probing systems for vulnerabilities to improve security.
Types:
- White Hat: Ethical hackers who work with organizations to enhance security.
- Black Hat: Malicious hackers who exploit vulnerabilities for personal gain.
- Gray Hat: Operate in a morally ambiguous area, sometimes hacking without permission but without malicious intent.
Tools: Nmap (network scanning), Metasploit (vulnerability exploitation), Wireshark (network protocol analysis).
Importance: Helps identify and fix security flaws before they can be exploited by malicious actors.

Definition: A branch of AI that focuses on the interaction between computers and human language.
Applications:
- Sentiment Analysis: Determining the sentiment behind a text.
- Chatbots: Automated responses using language understanding.
- Language Translation: Converting text from one language to another.
Techniques:
- Tokenization: Breaking down text into words or phrases.
- Named Entity Recognition (NER): Identifying proper nouns in text.
- Text Classification: Categorizing text into predefined groups.
Challenges: Ambiguity, context understanding, and dealing with large volumes of unstructured data.

A subset of AI that creates algorithms enabling computers to learn from data.
Supervised Learning: Involves labeled datasets for making predictions, including methods like regression and classification.
Unsupervised Learning: Deals with unlabeled data to uncover hidden patterns through clustering and association techniques.
Reinforcement Learning: Algorithms are trained via rewards and penalties based on actions taken in specific environments.
Common applications include fraud detection, recommendation systems, and predictive analytics.

Aims to convert complex datasets into visual formats for better understanding and insight generation.
Tools:
- Tableau: Enables creation of interactive dashboards for data exploration.
- Matplotlib/Seaborn: Python libraries for various forms of data visualization, including static and animated graphics.
- D3.js: A JavaScript library for creating dynamic, data-driven documents.
Techniques:
- Use of various charts like bar charts, line graphs, and scatter plots for data representation.
- Geospatial visualizations provide maps illustrating data distribution across specific areas.
- Dashboards facilitate real-time monitoring of data metrics.

Encompasses measures and policies aimed at protecting networked systems from unauthorized access and cyber attacks.
Key Concepts:
- Firewalls: Serve as barriers that filter incoming and outgoing network traffic.
- Intrusion Detection Systems (IDS): Continuously monitor network activity to detect suspicious behavior.
- Virtual Private Networks (VPN): Encrypt internet connections to secure data from interception.
Best Practices:
- Implement regular software updates and security patches.
- Utilize strong password practices combined with multifactor authentication.
- Employ network segmentation to restrict access and minimize potential threats.

Refers to authorized attempts to detect vulnerabilities in systems to enhance security.
Types:
- White Hat: Ethical hackers who collaborate with organizations to bolster security measures.
- Black Hat: Malicious hackers who exploit system weaknesses for their benefit.
- Gray Hat: Operate in a morally ambiguous space, probing systems without permission but without harmful intentions.
Tools: Include Nmap for network scanning, Metasploit for exploiting vulnerabilities, and Wireshark for analyzing network protocols.
Vital for identifying and rectifying security weaknesses before exploitation by malicious entities.

A branch of AI regarding how computers understand and interact with human languages.
Applications:
- Sentiment Analysis: Evaluates the sentiment expressed in texts.
- Chatbots: Provide automated language-based responses.
- Language Translation: Translates text between different languages.
Techniques:
- Tokenization: Splits text into individual words or phrases.
- Named Entity Recognition (NER): Detects and classifies proper nouns in text.
- Text Classification: Sorts text into designated categories.
Challenges: Includes addressing ambiguity, context comprehension, and managing large volumes of unstructured data.