Questions and Answers
What is data science/mining?
Data science is the discipline of extraction of knowledge from data, relying on computer science, statistics, and domain knowledge.
Which countries together account for 37% of the projected growth in urban population?
Delhi is projected to be the world’s second largest city by 2050 with a population rise to 36 million.
True
What does IoT stand for?
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What types of analysis are mentioned in the module?
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What is the role of data preprocessing?
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Match the following data types with their characteristics:
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What is the challenge in household poverty level prediction?
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What types of data can be represented in a dataset?
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Supervised learning uses unlabelled data.
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What is spatio-temporal data?
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What is unstructured data?
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Which of the following are examples of unstructured data? (Select all that apply)
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What does graph data capture?
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Most real-world data is clean and reliable.
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What are common data quality issues? (Select all that apply)
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What is noise in data?
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What are the types of missing data?
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What is duplicate data?
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Which of the following are techniques involved in data cleaning? (Select all that apply)
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Scaling is necessary when different numeric features have different scales.
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What are some types of data scaling methods? (Select all that apply)
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What is the goal of data transformation?
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Match each encoding technique with its description.
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How can sampling handle imbalance data?
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Study Notes
Data Science and IoT in Smart Cities
- Data Science involves extracting knowledge from data, utilizing computer science, statistics, and domain knowledge.
- The process includes data structure, descriptive programming, algorithms, visualization, and big data computing.
Importance of Urbanization
- Rapid growth of megacities, with 90% of the increase occurring in developing countries, primarily in Asia and Africa.
- India, China, and Nigeria contribute to 37% of urban population growth.
- Delhi is projected to become the world's second-largest city with a population of 36 million by 2050.
The Concept of Smart Cities
- Smart cities feature ubiquitous connected devices, such as connected vehicles, enhancing urban environments.
- IoT infrastructure layers include application, transport, network, and physical layers facilitating data collection and processing.
Characteristics of IoT Data
- IoT produces "big data" which necessitates data science for effective analysis and utilization.
- Challenges in data mining include handling raw data, noise, incompleteness, heterogeneity, and high volume.
Types of Data Analysis
- Descriptive, diagnostic, predictive, and prescriptive analysis provide different levels of insight into data.
- Exploratory analysis uncovers patterns and trends in data.
Smart City Applications
- Example problems include predicting household poverty levels, disaster recovery support, and bushfire monitoring using various datasets.
- Effective data management improves resource allocation and risk assessment in urban settings.
Data Quality and Types
- Data is characterized by instances (observations) and attributes (features), which can be labeled outcomes.
- Key data types include categorical (nominal, ordinal) and numerical (discrete, continuous) data.
Varieties of Data
- Common data forms include tabular, transaction, temporal, spatial, spatio-temporal, and unstructured data.
- Spatial data is vital for geographical analysis, while spatio-temporal data tracks movements over time.
Ensuring Data Quality
- Data quality considers completeness, accuracy, and consistency, which are often compromised in real-world scenarios.
- Data mining focuses on detecting and rectifying quality issues in datasets for reliable analysis.
Course Structure and Learning Outcomes
- Course content spans from data types and quality to machine learning applications in smart cities.
- Key learning outcomes include proficiency in statistical tools, data mining algorithms, and real-world problem-solving using programming.
Instructor Credentials
- Punit Rathore has a PhD from the University of Melbourne and postdoctoral experience at MIT's Senseable City Lab.
- Expertise includes machine learning, spatio-temporal data mining, and IoT applications in urban intelligence.
Practical Tools and Assessment
- Familiarity with Python or R is recommended for course success; assessment includes a final quiz.
- Students will practice using Jupyter Notebook for data analysis and coding tasks related to the course content.### Typical Data Quality Issues
- Noise: Random errors or distortions present in measurements, often irrelevant and can arise from various sources like accelerometer data or GPS inaccuracies.
- Outliers: Data points that significantly differ from the majority; can be classified as local (affecting small subsets) or global (impacting the entire dataset).
- Missing Values: Occur when one or more attribute values are not present; reasons include non-response, sensor failures, or inapplicable attributes.
- Duplicates: Instances of identical or nearly identical objects in a dataset caused by sensor errors, merging multiple data sources, or human error.
Missing Data Types
- Missing Completely at Random: No pattern to the missing data, maintaining unbiased analysis although may lose statistical power.
- Missing at Random: Specific factors may influence missingness, yet there is no direct correlation with the missing value.
- Missing Not at Random: Missingness is systematically related to the unobserved value, often requiring careful modeling or resolution.
Data Pre-processing Importance
- Essential as raw data may breach many assumptions made by machine learning (ML) models, influencing accuracy and efficacy.
- Pre-processing can account for a significant portion of the workload in ML, potentially up to 90%.
Major Data Pre-processing Techniques
- Data Cleaning: Involves managing noise, outliers, duplicates, and missing values through strategies like imputation, binning, regression for smoothing, and clustering.
- Data Transformation: Encompasses scaling, encoding, feature engineering, and sampling for better model integration.
Data Cleaning Methods
- Imputation: Estimating missing data values using techniques like mean, k-NN, or constant values.
- Binning: Sorting data into bins to manage noise and outliers, can utilize methods like equal-width or equal-depth binning.
- Regression: Fitting curves to data points to replace noisy or missing values.
- Human Inspection: Combining automated systems with expert evaluations for identifying anomalies.
Data Scaling Techniques
- Standard Scaling: Normalizes data means to zero and standard deviations to one, assuming normal distribution.
- Min-Max Scaling: Scales values to a specified range (e.g., 0 to 1); sensitive to outliers.
- Robust Scaling: Centers data using median and scales based on interquartile range, reducing the impact of outliers.
Data Encoding
- Converts categorical data into numerical formats for model applicability; methods include:
- Ordinal Encoding: Assigns integer values based on the order of categories.
- One-hot Encoding: Creates binary columns for each category, increasing feature dimensions.
Guidelines for Data Transformation
- Transform only input features, not output targets.
- Follow fit-predict paradigm during transformation to prevent data leakage or distortion of training and testing data.
Data Sampling Techniques
- Random Sampling: Each data point has an equal chance of selection.
- Stratified Sampling: Ensures representative groups by maintaining class distributions.
- Under-sampling & Oversampling: Techniques used to address imbalanced datasets through equalizing the representation of classes.
Data Pre-processing Summary
- A critical step in ML that influences model performance.
- Scaling is especially relevant for distance-based algorithms.
- Missing data imputation is preferred over data removal for maintaining integrity.
- Imbalanced datasets require additional strategies for developing reliable models.
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Description
This quiz covers the concepts of data science and data mining within the context of smart cities and IoT data analytics. It includes topics like data types, pre-processing, and the essential questions to guide data extraction. Enhance your understanding of how knowledge is derived from data in modern applications.