Introduction of Data science .pptx
- 1. DATA SCIENCE Dr. Suneel Pappala
- 2. Data science Data science is an interdisciplinary field that combines statistical techniques, programming, and domain expertise to extract insights and knowledge from structured and unstructured data. It involves a combination of methods from mathematics, statistics, computer science, and machine learning to analyze and interpret complex data. The goal of data science is to uncover patterns, make predictions, and inform decision-making based on data-driven insights.
- 3. Key components of data science include 1. Data Collection: Gathering relevant data from various sources. 2. Data Cleaning: Preparing and cleaning the data to ensure accuracy and consistency. 3. Data Analysis: Applying statistical models and algorithms to understand the data and find trends or patterns. 4. Machine Learning: Using algorithms that allow computers to learn from data and make predictions or decisions without explicit programming. 5. Data Visualization: Presenting the results in an understandable and actionable format, often through charts, graphs, or dashboards. 6. Decision-Making: Using insights from the analysis to drive business strategy, optimize processes, or solve specific problems.
- 4. Data Collection Definition: Gathering raw data from various sources, which could include databases, web scraping, IoT devices, APIs, and more. Example: Collecting customer transaction data from an e-commerce platform or gathering social media data via APIs. Customer Transaction Data (E-commerce Platform): o Example: Suppose you're working with an online retail store like Amazon or Flipkart. The platform collects data about every customer transaction, which includes: Product Details: Item purchased, price, quantity, and category. Customer Information: User ID, age, location, and browsing history. Transaction Metadata: Purchase date, time, payment method, and delivery option. o This transactional data can be used for understanding customer behavior, predicting future purchases, or designing personalized marketing campaigns.
- 5. Data Cleaning (Preprocessing) Definition: Removing or fixing errors, handling missing data, and preparing data for analysis. Example: Converting data types, handling missing values, filtering out outliers, and removing duplicates in a dataset. Converting Data Types Problem: Sometimes data is imported in the wrong format. For example, a numeric column (like price) might be stored as text. Example: Suppose you have a column Price that is in string format but should be numeric. Handling Missing Values Problem: Datasets often have missing values that need to be addressed before analysis. Example: You can either fill missing values (imputation) or drop rows/columns with missing data.
- 6. Data Cleaning (Preprocessing) Filtering Out Outliers Problem: Outliers can distort your analysis or model performance, so they need to be handled. Example: Suppose you have a column Price where an outlier exists, like a product mistakenly priced at $1,000,000. Removing Duplicates Problem: Duplicate entries in the data can lead to inaccurate results, especially in aggregations or model training. Example: If you have a dataset where some rows are repeated, you can remove them.
- 7. Data Cleaning (Preprocessing) Key Points Addressed: Price column converted to numeric. Missing values in the Purchase Date and Quantity columns handled. Outliers in the Price column removed. Duplicate rows removed.
- 8. Data Exploration (Exploratory Data Analysis - EDA) . Definition: Investigating the dataset to uncover initial patterns, trends, and insights using statistical methods and visualization. Example: Plotting histograms, scatter plots, and correlation matrices to understand relationships between variables. Plotting a Histogram Purpose: A histogram shows the distribution of a single variable. It's useful for understanding the frequency of values and the shape of the data distribution (e.g., normal, skewed). Example: Plotting the distribution of product prices in an e-commerce dataset. Scatter Plot Purpose: A scatter plot is used to visualize the relationship between two variables. It shows how one variable changes in relation to another. Example: Plotting the relationship between product price and quantity sold.
- 9. Correlation Matrix Purpose: A correlation matrix shows the correlation coefficients between multiple variables. It helps identify linear relationships between variables, ranging from -1 (negative correlation) to 1 (positive correlation). Example: Finding the correlation between price, quantity, and customer ratings.
- 10. Summary of Visualization Techniques 1. Histogram: Shows the distribution of a single variable (e.g., prices, quantities). o Insight: Reveals the shape and frequency of data (e.g., normal or skewed distribution). 2. Scatter Plot: Displays the relationship between two continuous variables (e.g., price vs. quantity sold). o Insight: Shows patterns, trends, or relationships (e.g., whether a higher price leads to fewer items sold). 3. Correlation Matrix: Quantifies relationships between multiple variables using correlation coefficients. o Insight: Identifies strong or weak correlations between variables (e.g., positive correlation between price and rating).
- 11. Data Transformation/Feature Engineering Definition: Transforming raw data into useful formats by creating new features or adjusting existing ones to make data more suitable for modeling. Example: Normalizing data, converting categorical variables into numerical values (encoding), or creating new features like 'age group' from raw 'date of birth.' Here are some common feature engineering techniques such as normalizing data, encoding categorical variables, and creating new features like age groups from raw data. These techniques are crucial for preparing your data for machine learning models.
- 12. Data Transformation/Feature Engineering 1. Normalizing Data Purpose: Normalization scales the values of continuous variables to a similar range, typically between 0 and 1. This helps machine learning models converge faster and ensures that no one feature dominates due to its scale. Example: Normalizing the Price column to bring all prices between 0 and 1. 2. Converting Categorical Variables into Numerical Values (Encoding) Purpose: Machine learning models require numerical inputs, so categorical features (e.g., product categories or user locations) need to be converted to numeric form. Example: Encoding a Category column (e.g., Electronics, Apparel, Books) using one-hot encoding or label encoding.
- 13. Data Transformation/Feature Engineering 3. Creating New Features (Feature Engineering) Purpose: You can create new features that make the data more useful for analysis or predictive modeling. For example, converting raw Date of Birth data into age groups can make the model understand the user demographics better. Example: Create an Age Group from a Date of Birth column.
- 14. Data Transformation/Feature Engineering Summary of Techniques: 1. Normalization: Rescales numeric values to a similar range (e.g., 0 to 1) to prevent larger numbers from dominating smaller ones in models. 2. Encoding Categorical Variables: o One-Hot Encoding: Creates binary columns for each category (useful for unordered categories). o Label Encoding: Converts categories into numeric labels (useful for ordinal data). 3. Creating New Features: Converts raw data into more meaningful categories (e.g., Age Group from Date of Birth).
Editor's Notes
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