Smart City and IoT Data Analytics Module 1

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?

India

China

Nigeria

All of the above (correct)

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?

Internet of Things Signup and view all the answers

What types of analysis are mentioned in the module?

All of the above Signup and view all the answers

What is the role of data preprocessing?

Data preprocessing is essential to prepare and clean data for analysis. Signup and view all the answers

Match the following data types with their characteristics:

Categorical = Distinct categories such as gender Numerical = Quantitative values that can be continuous or discrete Ordinal = An ordering exists among categories Nominal = No natural order between categories Signup and view all the answers

What is the challenge in household poverty level prediction?

Difficult to ensure if the right people are given enough aid. Signup and view all the answers

What types of data can be represented in a dataset?

Data can be numbers, names, or other labels. Signup and view all the answers

Supervised learning uses unlabelled data.

False Signup and view all the answers

What is spatio-temporal data?

It involves time-ordered movements of users or vehicles. Signup and view all the answers

What is unstructured data?

Data that are not organized in a clearly defined framework. Signup and view all the answers

Which of the following are examples of unstructured data? (Select all that apply)

Emails Signup and view all the answers

What does graph data capture?

The relationship among data objects. Signup and view all the answers

Most real-world data is clean and reliable.

False Signup and view all the answers

What are common data quality issues? (Select all that apply)

Outliers Signup and view all the answers

What is noise in data?

Random component of a measurement error, meaningless information. Signup and view all the answers

What are the types of missing data?

Missing completely at random, missing at random, and missing not at random. Signup and view all the answers

What is duplicate data?

Objects in a dataset that are duplicates or almost duplicates. Signup and view all the answers

Which of the following are techniques involved in data cleaning? (Select all that apply)

Clustering Signup and view all the answers

Scaling is necessary when different numeric features have different scales.

True Signup and view all the answers

What are some types of data scaling methods? (Select all that apply)

Robust scaling Signup and view all the answers

What is the goal of data transformation?

To prepare data for modeling by adjusting formats and scales. Signup and view all the answers

Match each encoding technique with its description.

Ordinal Encoding = Assigns an integer to categories based on order One-hot Encoding = Creates binary columns for each category Signup and view all the answers

How can sampling handle imbalance data?

By creating representative samples that balance the class distribution. Signup and view all the answers

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.