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Semester 1: Fundamentals of Data Science
Introduction of Data Science: Data Science Venn diagram, Basic terminology, Data science case studies, Types and levels of data, Types of data analytics including Descriptive, Diagnostic, Predictive, Prescriptive analytics, Five steps of Data science
Introduction of Data Science
The Data Science Venn diagram visually represents the intersection of three core areas: Computer Science, Statistics, and Domain Expertise. Data Science is at the center of these intersections.
This diagram emphasizes that successful data science requires skills and knowledge from all three fields.
Raw facts and figures that can be processed to obtain information.
Processed data that provides meaning and context.
Creating a mathematical representation of a real-world process.
A set of rules or procedures for solving a problem.
Retail Sales Optimization
Using predictive analytics to forecast sales and optimize inventory levels.
Improved stock management and increased sales.
Healthcare Predictive Analysis
Analyzing patient data to predict disease outbreaks.
Timely interventions and improved patient outcomes.
Numerical data that can be measured.
Descriptive data that can be categorized.
Categorical data without a specific order.
Categorical data with a logical order.
Numeric data where differences are meaningful, but there is no true zero.
Numeric data with meaningful differences and a true zero.
Analyzes past data to understand trends.
Examines data to determine causes of events.
Uses statistical models to forecast future events.
Suggests actions based on data analysis.
Problem Definition
Clearly define the problem that needs to be solved.
Data Collection
Gather relevant data from multiple sources.
Data Preparation
Clean and preprocess the data for analysis.
Modeling
Develop models to analyze and derive insights from the data.
Evaluation
Assess the model's performance and make necessary adjustments.
Mathematical Preliminaries: Basic maths, symbols and terminology, linear algebra, Probability definitions, Bayesian vs frequentist, compound events, conditional probability, rules of probability
Mathematical Preliminaries
Basic Maths
Basic mathematics involves essential operations such as addition, subtraction, multiplication, and division. Familiarity with integers, fractions, decimals, and percentages is crucial. Understanding properties like commutative, associative, and distributive laws enhances computation skills.
Symbols and Terminology
Mathematical symbols are used to represent numbers, operations, and relationships. Common symbols include + for addition, - for subtraction, * for multiplication, / for division, = for equality, and ≠ for inequality. Familiarity with these symbols is essential for reading and writing mathematical expressions.
Linear Algebra
Linear algebra focuses on vector spaces and linear mappings. Key concepts include matrices, vectors, determinants, and eigenvalues. Applications include solving systems of equations, transformations, and understanding data structures in machine learning.
Probability Definitions
Probability is the study of uncertainty and quantifies how likely events are to occur. Key terms include sample space, event, probability of an event, and complementary events. Events can be simple (one outcome) or compound (multiple outcomes).
Bayesian vs Frequentist
Bayesian statistics incorporates prior knowledge into probability estimates, using Bayes' theorem. Frequentist statistics focuses on long-run behavior and does not incorporate prior beliefs. Understanding these paradigms is crucial for interpreting data analysis results.
Compound Events
Compound events consist of two or more simple events. They can be expressed using the union (or) and intersection (and) of events. Understanding how to calculate the probability of compound events is fundamental to probability theory.
Conditional Probability
Conditional probability measures the likelihood of an event occurring given the occurrence of another event. It is denoted as P(A|B) and calculated using the formula P(A|B) = P(A and B) / P(B). Understanding this concept is essential for analyzing dependent events.
Rules of Probability
The rules of probability include the addition rule, multiplication rule, and the law of total probability. The addition rule is used to find the probability of the union of events, while the multiplication rule is used for independent events. Mastery of these rules is critical for thorough statistical analysis.
Data Mining and Data Warehousing: Introduction, Design considerations, Data loading process, Data mining techniques, Tools and platforms
Data Mining and Data Warehousing
Introduction
Data mining refers to the process of discovering patterns and knowledge from large amounts of data. Data warehousing is the storage system that supports data analysis and reporting. The integration of data mining and data warehousing leads to informed decision-making in various domains.
Design Considerations
Key design considerations for data warehousing include scalability, performance, data quality, and security. It is important to choose the right schema, like star or snowflake, to support efficient queries and reporting. Data should be organized for optimal access and retrieval.
Data Loading Process
The data loading process involves extracting data from various sources, transforming it to suit the data model, and loading it into the warehouse. ETL (Extract, Transform, Load) tools help automate this process, ensuring data consistency and integrity.
Data Mining Techniques
Common data mining techniques include classification, clustering, regression, and association rule mining. Each technique serves a different purpose, such as predicting outcomes or segmenting data into meaningful groups. The choice of technique depends on the nature of the data and the analysis goals.
Tools and Platforms
Several tools and platforms facilitate data mining and warehousing. Popular tools include Apache Hadoop for big data, SQL-based platforms for structured data analysis, and machine learning libraries like TensorFlow and Scikit-learn for advanced analytics. The choice of tools depends on user requirements and data complexity.
Visualizing Data: Exploratory Data Analysis, Developing visual aesthetics, Chart types, Reading graphs, Interactive visualizations
Visualizing Data
Exploratory Data Analysis
Exploratory Data Analysis (EDA) is a statistical approach used to analyze datasets to summarize their main characteristics, often with visual methods. EDA helps in detecting outliers, understanding data distribution, and formulating hypotheses. Techniques include using summary statistics, visualizations such as histograms, box plots, and scatter plots to reveal patterns and relationships within the data.
Developing Visual Aesthetics
Creating visually appealing data visualizations involves considering color schemes, typography, layout, and overall design principles. Good aesthetics enhance readability and engagement. Important aspects include consistency in style, effective use of color to convey meaning, and clarity to ensure the audience understands the message being communicated.
Chart Types
There are various types of charts used for data visualization, each suitable for different contexts. Common types include bar charts, line charts, pie charts, histograms, and scatter plots. Each chart type serves a purpose: bar charts for comparing quantities, line charts for trends over time, pie charts for showing proportions, and so on. Choosing the right chart type is essential for effective communication.
Reading Graphs
Reading graphs requires understanding the components such as axes, legends, and titles. It is important to interpret the data accurately by considering scale, dimensions, and the context of the information presented. Observing trends, patterns, and anomalies in the graph helps in making informed decisions and conclusions based on the visual representation.
Interactive Visualizations
Interactive visualizations allow users to engage with the data, exploring different facets through actions like filtering, zooming, and clicking. Tools such as Tableau, Power BI, and D3.js enable the creation of these visualizations, enhancing user experience and providing deeper insights. Interactivity empowers users to uncover specific details and analyze dynamic data.
Data Science Recent Trends: Applications, recent trends in data collection and analysis techniques, various visualization techniques, application development methods
Data Science Recent Trends
Applications of Data Science
Data science is applied in various fields such as healthcare for predictive analytics, finance for fraud detection, e-commerce for customer segmentation, and marketing for targeted campaigns. Each application leverages data for improved decision making.
Recent Trends in Data Collection Techniques
New data collection methods include sensor data from IoT devices, social media data scraping, and surveys conducted via mobile applications. The reliance on big data has pushed for more efficient data collection methods, focusing on real-time data.
Recent Trends in Data Analysis Techniques
The use of machine learning algorithms and artificial intelligence in data analysis is on the rise. Tools such as Python and R for statistical analysis, along with automated machine learning platforms, help streamline the analysis process.
Visualization Techniques
Current trends involve the use of interactive dashboards, real-time data visualization tools, and augmented reality for data representation, enabling users to explore data in more dynamic ways.
Application Development Methods
Modern application development for data science incorporates agile methodologies, rapid prototyping, and the use of cloud computing for hosting data applications, allowing for scalable solutions.
