1. Introduction

Unprocessed, unorganized data collected from sources; needs processing to become useful

• Data Mining: Discovering patterns in large datasets.

• Business Intelligence (BI): Focuses on historical and real-time data for descriptive analytics; uses tools like Excel, SQL.

• Data Science vs. BI: Data science focuses on predictive/prescriptive analytics, algorithms, programming, and machine learning.

• Data → Information → Knowledge:

  • Data: Raw facts without context.

  • Information: Organized, contextualized data.

  • Knowledge: Insights and understanding from information.

• Goal: Create systems performing tasks needing human-like intelligence.

• Types:

  • ANI: Narrow AI (e.g., NLP, facial recognition).

  • AGI: General AI (not yet achieved; handles any task).

  • ASI: Superintelligence (future potential; surpasses human intelligence

• Definition: Systems learn from data to make predictions.

• Types:

  • Supervised (classification, regression).

  • Unsupervised (clustering, pattern detection).

  • Reinforcement learning (goal-oriented learning via rewards/penalties).

• Data Splits: Training set (model building) vs. Testing set (performance testing)

1. Classification Metrics:

   • Type I Error: False positives.

   • Type II Error: False negatives.

   • Sensitivity: True positive rate.

   • Specificity: True negative rate.

2. Regression Metrics: Absolute error, mean square error, relative error.

• Industry: Process automation & optimization (e.g., Shell.ai).

• Business: Customer insights, sales predictions, HR analytics (e.g., Google People Analytics).

• Text Data: Sentiment analysis, chatbots (e.g., Airbnb AI assistant).

• Image Data: Object detection, scene classification (e.g., smart city transport planning).

• Medical Data: Disease detection, drug development, remote monitoring (e.g., Pfizer predictive analytics).

• Goal: Manage movement of data from source to access.

• Data Collection:

  • Identify sources, relevant attributes, and involved people.

  • Methods: observation, interviews, surveys, existing records.

  • Ensure accuracy & reliability.

• Data Storage:

  • Requirements: accessibility, transparency, security.

  • Consider type, form, frequency of use, and cost.

  • Options: distributed storage, cloud (watch for security risks).

• Data Access:

  • Structured data → queries, XML.

  • Unstructured data → NoSQL technologies.

• Goal: Refine and prepare data for use.

• Data Preservation:

  • Remove noise (errors), fix missing values.

  • Depends on domain knowledge.

  • Storage lifespan example: flash drives (80–90 yrs), HDDs (3–6 yrs).

• Data Description:

  • Use schemes & metadata for meaning.

  • Enables understanding and better use.

• Data Publication:

  • Make data available for others.

  • Clean, format, describe for usability & value.

• Data Security:

  • Identify threats: physical (infrastructure damage), human error, malicious activity.

  • Measures: monitoring, firewalls, intrusion detection, encryption, physical safety.

• Goal: Extract insights & predict future events for decision-making.

• Statistical Analysis:

  • Methods: matrix-valued analysis, prediction models, PCA, clustering, sampling.

• Modeling & Simulation:

  • Test real-world scenarios with large datasets.

  • Not 100% accurate, but useful.

  • Examples: Monte Carlo, Markov Chain Monte Carlo.

• Visual Techniques:

  • Present results for better understanding.

  • Examples: box plots, histograms, word clouds, charts.

• Trustworthiness is key for robust, high-quality data.

• Organizational / Trademarked Data:

  • Data from company activities: transactions, customers, employees, products.

  • Examples: Google, Facebook collect massive user data.

  • Reluctance to share due to competition risk & customer privacy concerns.

• Government Data:

  • Many governments provide open data (e.g., demographics, economics).

  • Example: USA’s Data.gov → 300,000+ datasets, privacy protected.

• Academic Data:

  • Large datasets from research in medicine, economics, history.

  • Often publicly available via journals & databases (e.g., Google Scholar).

• Social Media Data:

  • Text, videos showing user interactions.

  • APIs give structured access (e.g., Twitter API for tweet attributes).

  • Useful for sentiment analysis & pattern detection.

1. Quantitative Data (measurable)

   • Discrete: Fixed numbers (e.g., number of students, age).

   • Continuous: Range values

     • Interval → can be negative (e.g., °C/°F temperature).

     • Ratio → >0 only (e.g., height, weight).

2. Qualitative Data (descriptive)

   • Nominal: No order (e.g., eye color).

   • Ordinal: Ordered categories (e.g., salary grade).

   • Binary: Two categories (e.g., on/off).

1. Structured: Organized, tabular (rows & columns).

2. Unstructured: Unknown form (text, images, audio, video).

3. Semi-Structured: Partial organization (e.g., metadata, tags, e-mails: to and cc are structured, Body is Not)

4. Streaming Data: Continuous, real-time flow from multiple sources (e.g., social media feeds, sensors).

1. Volume: Large scale (terabytes → zettabytes). Example: plane sensors generate 10 GB/sec.

2. Variety: Multiple types (structured, unstructured).

3. Velocity: Speed of creation & processing (e.g., 500 hrs video uploaded to YouTube/min).

4. Veracity / Validity: Trustworthiness & relevance (remove noise, avoid outdated data).

5. Value: Business benefits (new services, improved customer experience & operations).

Definition: Extract useful information from raw data (often unstructured, incomplete, inconsistent).

Goal: Identify patterns, trends, correlations → enable analysis, decision-making, actionable insights.

• Data collection

• Data preprocessing

• Data analysis / model building

• Insight implementation

• Data storage (parallel to all stages).

• describe the center of a dataset’s distribution

• mean: arithmetic average of the variable’s values

• mode: most repeated element in a dataset

• median: value located exactly at the middle point of a variable’s sorted values

• range: difference between highest and lowest value, heavily influenced by outliers

• standard deviation: square difference between a unique element and the mean

• variance: square of the standard deviation measure

• a particular event (N) cannot occur at the same time as another event (M)

• events can occur simultaneously

• events can be correlated with each other —> dependent on each other

• conditional probability: probability of event A, given that event B has already occured

• discrete random variables: can only take distinct values, e. g. students in a class, number of family members

• continuous random variables: can take all values in an intervall, e. g. height of students

• probability of a continuous random variable to take a given value

• not possible to calculate the probability for each value —> calculate the probability of a value within an interval by estimating the area under the curve within that interval

• likelihood of a discrete random variable to take a given value

• possible to find a porbability for each of the variables

• bell-curve shape

• mean: center of the bell

• standard deviation: wideness of the bell shape

• whether or not an event occurs —> two possible outcomes

• probability of getting a given outcome a precise number of times

• frequency with which an independent event occurs within a specific interval

• not the probability of an event, but how often it occurs in a specific period of time

• the knowledge available about the given parameters is updated with the new information gathered from the data observed

• we want to know how event A is conditioned by event B but we only know how event B is conditioned by event A

Collection → Preprocessing → Analysis/Modeling → Insights Implementation → Storage.

Extract knowledge from data to make predictions and guide decision-making

• Better computing power,

• more available data,

• improved storage capabilities