GNEST305 Introduction to Artificial Intelligence and Data Science KTU BTech S3 2024 Scheme

  Introduction to Data Science Data science is the interdisciplinary field that combines statistics, computer science, and domain expertise to extract meaningful insights and knowledge from data. With the explosion of digital information, organizations now generate enormous volumes of data from social media, sensors, transactions, healthcare systems, and more. Traditional tools are no longer sufficient to handle this scale and complexity. Data science addresses this challenge by integrating methods from machine learning, big data technologies, and visualization techniques to transform raw data into actionable insights. The  data science is not just about handling large datasets (“big data”), but also about asking the right questions , applying analytical methods , and using Python-based tools effectively to create practical solutions. Benefits of Data Science Better Decision-Making By applying statistical models and machine learning, data science provides evidenc...

  Use of Statistics in Data Science Statistics provides the mathematical tools and principles needed to understand, analyze, and interpret data. In data science, it plays a vital role in every stage of the workflow: Data Understanding and Exploration Statistics helps describe and summarize data using measures of central tendency (mean, median, mode) and dispersion (variance, standard deviation). This step is crucial for detecting trends, anomalies, or irregularities in datasets. Data Cleaning and Quality Checking Statistical methods identify outliers and missing values, ensuring that the data used for analysis or machine learning is reliable. Hypothesis Testing Statistics allows data scientists to test assumptions (e.g., “Does a new marketing campaign increase sales?”) using p-values, t-tests, chi-square tests , etc. This makes conclusions more scientifically valid instead of relying on guesswork. Probability and Uncertainty Handling Many d...

  The Data Science Process (Six Steps) Data science projects follow a structured process. The book highlights six main steps , which help ensure that insights are reliable, reproducible, and useful for organizations. 1. Setting the Research Goal What it means: Clearly define the purpose of the project. How it’s done: Prepare a project charter that specifies: What you’re going to research. Why it benefits the organization. What data and resources are needed. Timeline and deliverables. Example: A company may want to know: “Can we predict customer churn to improve retention?” 2. Retrieving Data What it means: Collect the data required for the project. Sources: Databases, spreadsheets, APIs, third-party vendors, or logs. Checks needed: Does the data exist? Is the quality sufficient? Do we have access rights? Example: Gathering customer purchase records from a database or downloading open data from a government portal. 3. D...

  Use of Machine Learning in Data Science Machine Learning is one of the most powerful tools in data science . While statistics helps us describe and test data, ML enables computers to automatically learn patterns from data and make decisions or predictions. Here’s how ML is used in data science: 1. Making Predictions ML models can use past data to predict future outcomes. Example: Predicting sales for the next month, predicting exam scores from study hours. 2. Classifying Data ML can separate data into categories. Example: Email → spam or not spam, medical diagnosis → disease present or not. 3. Finding Patterns and Groups ML can discover hidden structures in data. Example: Grouping customers with similar buying behavior (customer segmentation). 4. Recommendation Systems ML personalizes experiences by suggesting items. Example: Netflix recommending movies, Amazon suggesting products. 5. Detecting Anomalies ML identifies unusual patterns in data. Ex...

  Step 1: Defining Research Goals and Creating a Project Charter The very first step of any data science project is understanding the problem clearly and aligning it with the organization’s needs . This step answers three key questions: What → What exactly does the company want you to do? Why → Why is this project valuable? Does it align with a bigger strategy or is it a one-off project? How → How will the project be carried out? What resources, data, and methods will you use? 1. Spend Time Understanding the Goals and Context The research goal should be clear, precise, and agreed upon by all stakeholders. Ask questions until you fully understand the expectations. Avoid misunderstandings — one of the biggest mistakes in data science is solving the wrong problem. Example: Business asks: “Why are customers leaving our service?” If misunderstood, you might analyze sales trends instead of customer churn . 👉 Tip for students: This step is les...

 “Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed.” —Arthur Samuel, 1959 When machine learning is seen as a process, the following definition is insightful: “Machine learning is the process by which a computer can work more accurately as it collects and learns from the data it is given.” —Mike Roberts Applications for machine learning in data science Regression and classification are of primary importance to a data scientist. To achieve these goals, one of the main tools a data scientist uses is machine learning. The uses for regression and automatic classification are wide ranging, such as the following: ■ Finding oil fields, gold mines, or archeological sites based on existing sites (classification and regression) ■ Finding place names or persons in text (classification) ■ Identifying people based on pictures or voice recordings (classification) ■ Recognizing birds based on their whistle (classification) ■ Identif...