Week 1 DS - Introduction to Data Science (PDF)

Week 1 (DS) Video 1 Exploration of Data Science Course Overview Course Goals: Practical dive into the complexities of digital insights. Guide for both beginners and those with some experience in data science. Key Concepts: Collaborative Effort: Data science involv...

Week 1 (DS) Video 1 Exploration of Data Science Course Overview Course Goals: Practical dive into the complexities of digital insights. Guide for both beginners and those with some experience in data science. Key Concepts: Collaborative Effort: Data science involves various tasks working together to uncover knowledge. Fundamental Topics: 1. Basics of Data Analysis: o Understanding multimodal data. o Distinguishing between structured and unstructured data. 2. Data Collection: o Learn various concepts related to data collection. o Basic understanding of SQL. 3. Data Cleaning and Exploration: o Importance of data cleaning for meaningful analysis. o Exploratory Data Analysis (EDA) essentials: ▪ Addressing missing values. ▪ Identifying outliers. ▪ Executing data transformations to reveal patterns. 4. Data Visualization: o Techniques for visualizing data and telling stories within datasets. 5. Data Management: o Explore relational database management systems (RDBMS). o Basics of SQL for effective database interaction. 6. Model Building: o Core of exploration includes: ▪ Training and evaluating models. ▪ Practical techniques in machine learning. ▪ Topics include linear regression and a base classifier. 7. Real-World Applications: o Discuss tangible impacts of data science:  ▪ Predicting consumer behavior. ▪ Image recognition. ▪ Showcase versatility of data in various domains. Invitation to Explore: Encouragement to explore data science at your own pace. Emphasis on acquiring knowledge and curiosity to find practical insights in data's vast landscape. Thank you for your attention, and welcome to the course DA 106: Introduction to Data Science! Video 2 Course Overview: Data Science and Introduction Welcome Message: Excitement about the journey into the world of data science. Beginning of an exploration into a field that is shaping the future. Learning Objectives for the Week: 1. Introduction to Data Science: o Explore core concepts and principles of data science. o Understand its significance in today's technological landscape. 2. Relationship Between Data Science and Artificial Intelligence: o Examine the intersection of data science and AI. o Uncover synergies and dependencies between these two transformative fields. 3. Comprehending the Process of Data Collection and Generation: o Dive into methodologies and techniques for collecting and generating data. o Understand the importance of high-quality, reliable data in the data science workflow. 4. Learning About Various Data Categorizations: o Explore how data can be categorized and organized. o Understand different types of data and their implications in analysis and decision-making. Looking Forward: Anticipation for upcoming classes discussing these important aspects of data science. Video 3 Exploration of Data Science: Integral Components Overview: Data science harmonizes various domains to extract valuable insights from data. Essential components of data science will be broken down. Essential Components of Data Science: 1. Domain Expertise: o Deep understanding of a specific field or industry relevant to data analysis. o Example: In healthcare, a data scientist analyzes medical records to improve patient care. o Importance: Accurate data interpretation is challenging without domain knowledge. 2. Programming Skills: o Proficiency in languages like Python, R, or SQL is crucial. o Enables efficient data collection, cleaning, manipulation, and analysis. o Example: Python libraries (pandas, NumPy, scikit-learn) help work with complex datasets and build predictive models. 3. Knowledge of Mathematics and Statistics: o Understanding mathematical concepts and statistical techniques is foundational. o Key concepts: Linear algebra, calculus, probability, hypothesis testing. o Example: Regression analysis predicts future outcomes based on historical data trends. Multidisciplinary Nature of Data Science: Data science covers a wide range of fields, including: o Mathematics and statistics o Computer science o Domain applications Various subcategories exist within each area. Collaborative Problem-Solving: Data science often requires teamwork, with diverse expertise contributing to problem-solving. The problem-solving cycle includes: o Observation o Questioning o Hypothesizing o Experimenting o Analyzing o Concluding insights Use of Machine Learning: Practitioners use machine learning algorithms to process diverse data types (numbers, text, images, video, audio).  Machine learning systems mimic human intelligence to: o Learn patterns from data. o Make predictions. o Classify information. o Perform tasks requiring human cognitive abilities. Conclusion: Exciting opportunities arise from collaboration in data science. Looking forward to the next class. Video 4 Data Science and Machine Learning: Understanding the Landscape Data Growth: The volume of data is rapidly escalating from sources like social media, sensors, online transactions, etc. This digital data accumulation presents both opportunities and challenges for meaningful insights. Data science and machine learning offer solutions to handle this exponential data growth. Machine Learning Overview: Definition: Machine learning enables computers to automatically identify patterns in data and make predictions or decisions. Key Feature: It learns from data without relying solely on predetermined mathematical models. o Previously, mathematical models were predefined based on researchers' experience. o Now, data itself serves as a knowledge base for understanding relationships between inputs and outputs. Relationship Between AI, ML, and Data Science: Hierarchy: o AI is a subset of computer science. o ML is a subset of AI. o Data science intersects with computer science, AI, and ML. Role of Data Science: o Not limited to solving problems with mathematical models. o Effective in visualizing data to identify patterns. o Often requires developing models where machine learning or AI algorithms are beneficial. Machine Learning Subsets: Deep Learning: o A subset of machine learning. o Uses artificial neural networks, including specialized networks known as deep neural networks.  o Deep learning contributes to advanced successes in solving complex problems. Role of Analysts and Business Users: Analysts and business users translate insights from data into tangible value. Machine learning provides raw material for informed decision-making, innovation, and competitive advantages. Focus on solving useful problems and addressing business pain points is crucial for analysis to be valuable. Complexity of Data Science Workflow: The process involves multiple steps: o Data collection o Pre-processing o Model selection o Validation o Interpretation o Deployment Each step involves various components requiring expertise. Importance of a Holistic Understanding: Data scientists should understand all components of the workflow to grasp outcomes and important considerations in problem-solving. Upcoming classes will cover example applications. Video 5 Data Science: A Powerful Tool for Businesses and Beyond Importance of Data Science in Business: Data science acts as a secret weapon for businesses to: o Understand customer needs and preferences. o Make informed decisions. o Gain a competitive edge and grow faster. Analyzing data reveals hidden insights, akin to finding hidden treasures. Data science serves as a guiding map towards success by leveraging available information. Applications Beyond Business: Data science is also impactful in other areas such as: o Agriculture: Helps farmers grow food more efficiently. o Education: Makes learning more engaging and personalized. o Disaster Response: Assists emergency teams in responding more quickly. o Transportation: Enhances the efficiency and intelligence of travel. Data Science in Fraud Detection: Acts as a superhero in preventing credit card fraud: o Analyzes data to determine if a credit card payment is safe. o Detects fraudulent activities by identifying patterns. o The more data it processes, the better it becomes at fraud detection. Supervised Machine Learning Setup: o Requires features (e.g., transaction amount, frequency, location, etc.) and labels (e.g., fraud or no fraud). o Machine learning algorithms learn from historical data to identify fraudulent transactions. Customer Segmentation: Data science helps businesses predict customer preferences through: o Customer Segmentation: Identifying shopping patterns by grouping similar buyers. o Enables targeted advertising and personalized offers. o Utilizes data generated from customer interactions with websites or stores. Acts like a personal shopper, enhancing the shopping experience. Unsupervised Machine Learning: o Used for customer segmentation without predefined labels. o Clustering techniques analyze customer behaviors based on features (e.g., products purchased, spending rate, demographics). Upcoming Topics: More machine learning problems will be discussed in future modules. Next class will cover different job roles available after mastering data science. Video 6 Understanding the Role of a Data Scientist Definition of a Data Scientist: A data scientist is described as: "Better at statistics than any programmer and better at programming than any statistician." o Source: Josh Wills, Head of Data Engineering at Slack. This quote highlights the multidisciplinary nature of a data scientist's role and the unique skill set required for success in data science. Different Roles in Data Science: 1. Data Analyst: o Interprets data to uncover insights and trends. o Uses tools to visualize and present findings.  o Aids in decision-making processes. 2. Data Scientist: o Explores complex datasets. o Applies statistical models and machine learning algorithms. o Extracts actionable insights and builds predictive models. 3. Data Engineer: o Designs and maintains infrastructure for data generation. o Ensures data is accessible and ready for analysis. Interconnected Roles: Each role plays a distinct yet interconnected part in the data science ecosystem. Unique skills from each role harness the power of data for various purposes. Data Scientist Workflow: Data scientists dedicate time to several key tasks, including: i. Data Cleaning and Preparation: ▪ Significant time spent on cleaning and organizing data. ▪ Ensures data quality and suitability for analysis. ii. Exploratory Data Analysis (EDA): ▪ Conducts EDA to understand patterns and trends within the data before modeling. iii. Building Models and Algorithms: ▪ Invests time in developing and refining statistical models and machine learning algorithms. iv. Interpreting Results and Communication: ▪ Interprets model outcomes and communicates findings to stakeholders. ▪ Aids in informed decision-making. Comprehensive Nature of Data Science: Understanding the distribution of time across these tasks emphasizes the comprehensive nature of a data scientist's workflow. Balances technical analysis with effective communication and interpretation skills. Data science involves tasks beyond mathematical modeling, including problem formulation and insights communication. Conclusion: The class will conclude with a quick recap and tips for your journey in data science in the next session. Video 7 Simplified Data Science Workflow Overview of the Workflow: Problem Formulation: o Identify the real problem and pain points to be solved using data science. Data Collection and Preprocessing: o Collect relevant data and perform preprocessing to extract useful features. Data Analysis and Modeling: o Analyze the data to find patterns, derive insights, and make predictions. Presentation of Insights: o Present the insights and predictions obtained from the analysis. Required Skills: Domain Expertise: Understanding the specific field related to the problem. Programming Skills: Proficiency in programming languages (e.g., Python, R). Mathematical Foundations: Strong grasp of mathematical concepts relevant to data science. Tool Proficiency: Familiarity with machine learning and statistical tools, as well as R/Python libraries. Team Collaboration: Data science often involves teamwork, where different roles contribute unique expertise. Team members should have deep knowledge in at least one area while being aware of other phases of the workflow. Red Flags in Data Science: 1. Rushing into Modeling: o Avoid taking shortcuts by not thoroughly understanding the data before modeling. o Emphasize the importance of spending time on data exploration. 2. Blindly Using Machine Learning Tools: o Understand the limitations of machine learning algorithms to choose suitable ones. 3. Ignoring Ethical Implications: o Consider ethical aspects to prevent serious issues in data practices. o New learners should prioritize responsible data practices. Mindset for Success in Data Science: Data-Driven Scientific Mindset: o Focus on uncovering meaningful insights that solve problems and reveal nonobvious findings. o Spend ample time exploring and understanding the data deeply. Importance of Visualization: o Learn proper visualization techniques to effectively communicate findings. Conclusion:  The next class will cover the process of data generation and how data is produced in our environment. Video 8 Data Generation and Collection Sources of Data: Data Capture: Information is captured from various physical or digital activities, such as: o Sales records o Customer feedback o Social media interactions o Temperature readings o Body movements o Other activities contributing to a vast pool of information available online. Methods of Data Collection: 1. Digital Data Collection: o Often comes from sensors that automatically capture information. o Examples of sensors include: ▪ Temperature sensors ▪ Motion detectors ▪ Cameras (for video capture). 2. Manual Data Collection: o Involves annotating or entering data from physical documents into computers. 3. Web Scraping: o Uses scripts to extract data from websites, likened to a "digital vacuum cleaner" that gathers information from the Internet. Data Formats: Collected data comes in various formats, including: o.raw or.mp4 for videos o.wav for speech o.csv for tabular data The data is often raw and messy, requiring cleaning before analysis. Importance of Data Cleaning: Data cleaning is a crucial step in the data science workflow. Ensures that data is tidy and suitable for analysis. Clean data is necessary before feeding it into machine learning models for training. Next Class: The next session will cover the categorization of data. Video 9 Types of Data: Structured vs. Unstructured Definitions: Structured Data: o Well-organized and represented in tables with rows and columns. o Easily searchable and analyzable. o Examples: Numbers, dates, strings, scientific observations. Unstructured Data: o Lacks organization and exists as free-form content without a predefined structure. o Examples: Textual data, server logs, social media posts, images, audio, video, word processing files, emails, spreadsheets. Key Differences: Display: o Structured data can be displayed in rows and columns, typically in relational databases. o Unstructured data cannot be organized in this manner. Storage: o Structured data requires less storage and can be compressed. o Unstructured data requires more storage, especially for large files like images and videos, which may need compression. Management: o Structured data is easier to manage and protect using legacy solutions. o Unstructured data presents challenges due to the variety of data types and requires newer database solutions. Data Distribution: Structured data comprises about 20% of enterprise data. Unstructured data accounts for 80% to 90% of the world's data, representing a significant challenge for data scientists. Data Pre-Processing: Importance: o Data pre-processing is crucial for converting unstructured data into a structured format, enabling meaningful analysis. o It involves applying transformations to extract valuable insights from unstructured data. Techniques:  o Data pre-processing is described as the "secret sauce" for data scientists. o It includes a set of techniques to transform unstructured data into structured formats. Example of Feature Extraction: When dealing with a sentence, direct use in a machine learning model is difficult. Potential Features to Extract: i. Word/phrase count ii. Existence of certain special characters iii. Relative length or tag of text iv. Topics identified Structured Representation: o Extracted features can be tabulated in a structured format (rows and columns) for easier management and analysis. Video 10 Types of Data: Quantitative vs. Qualitative Quantitative Data: Definition: Data that can be quantified, described, and manipulated using numbers. Characteristics: o Involves measurements, counts, or any data allowing basic mathematical operations (e.g., addition, averaging). o Provides a numerical lens to understand and analyze data. Qualitative Data: Definition: Data that cannot be neatly expressed with numbers or subjected to basic mathematical operations. Characteristics: o Resides in natural categories and language. o Relies on words, descriptions, and context to convey meaning. o Adds depth and nuance to understanding. Example: Coffee Shop Data Features: o Name of the coffee shop: Qualitative o Revenue (in thousands of dollars): Quantitative o Zip code: Qualitative (cannot perform mathematical operations with zip codes) o Average monthly customers: Quantitative o Country of coffee origin: Qualitative Analyzing Quantitative Data:  Key Questions: i. Averages: ▪ What is the mean or average value? Provides a central point for trends. ii. Trends Over Time: ▪ How does quantity evolve over time? Identifying increases or decreases can reveal patterns and assist in predictions. iii. Thresholds: ▪ Are there points where numbers raise red flags? Identifying thresholds helps in risk management and proactive decision-making. Analyzing Qualitative Data: Key Questions: i. Frequency: ▪ Which values appear most and least? Identifying dominant patterns and outliers. ii. Uniqueness: ▪ How many distinct values are present? Counting unique values reveals diversity within the dataset. iii. Specific Values: ▪ What are the unique values? Listing them helps understand the scope and variety of the qualitative data. Overview of Data Science Essence of Data Science: Definition: A blend of art and science that combines statistics, programming, and domain expertise. Purpose: Transforms raw data into meaningful insights. Data Science vs. Machine Learning: Data Science: Encompasses the entire process of extracting insights from data. Machine Learning: Focuses specifically on building models for making predictions. Real World Impact of Data Science: Applications: o Predicting customer behavior. o Optimizing healthcare operations. o Powering recommendation engines in daily life. Roles in Data Science Ecosystem: Recognizes the various roles involved, including versatile data scientists and the contributions of other positions. Collaboration is essential to unlock the full potential of data. Data Generation: Sources: Includes experiments, simulations, and continuous digital data flow. Importance of understanding data sources in harnessing data power. Categories of Data: 1. Structured Data: o Neatly organized in tables. 2. Unstructured Data: o Messier but equally valuable; includes text, images, and videos. Conclusion: This exploration is just the beginning of the vast and evolving world of data science. Future discussions will cover more topics related to data science in upcoming videos and modules.