Introduction-to-Data-Science_Abiot_.pptx

Data

1. Introduction to Data Science Data science is an interdisciplinary field that combines statistical analysis, machine learning, and domain expertise to extract insights and knowledge from data. It is a powerful tool for solving complex problems and driving business decisions. AB by Abiot Banti

2. Data Collection and Preprocessing Data Collection Gathering data from various sources, such as databases, sensors, and web APIs, is a crucial first step in the data science process. Data Preprocessing Cleaning, transforming, and preparing the raw data for analysis is essential to ensure data quality and reliability. Feature Engineering Creating new features from the raw data can help improve the performance of machine learning models.

3. Exploratory Data Analysis 1 Data Visualization Using various charts, graphs, and plots to understand the patterns, trends, and relationships within the data. 2 Statistical Analysis Applying statistical techniques to identify the distribution, central tendency, and variability of the data. 3 Anomaly Detection Identifying outliers and unusual data points that may require further investigation or special handling. 4 Hypothesis Testing Formulating and testing hypotheses to gain insights into the data and uncover potential relationships.

4. Statistical Modeling 1 Linear Regression Modeling the relationship between a dependent variable and one or more independent variables using a linear equation. 2 Logistic Regression Predicting the probability of a binary outcome based on one or more predictor variables. 3 Time Series Analysis Analyzing and forecasting data that is collected over time, such as stock prices or sales figures.

5. Machine Learning Algorithms Supervised Learning Algorithms that learn from labeled data to make predictions or classify new data, such as linear regression and decision trees. Unsupervised Learning Algorithms that discover patterns and insights from unlabeled data, such as clustering and dimensionality reduction. Deep Learning A powerful subset of machine learning that uses neural networks to learn complex patterns in data, such as image recognition and natural language processing. Reinforcement Learning Algorithms that learn by interacting with an environment and receiving feedback, such as game-playing agents and robotic control systems.

6. Model Evaluation and Validation Testing Evaluating the performance of the model on a held-out test set to ensure it generalizes well to new data. Validation Tuning the model's hyperparameters and checking for overfitting or underfitting using a validation set. Iteration Iterating on the model design and feature engineering to improve its performance and accuracy. Deployment Deploying the final model to production and monitoring its performance in real-world applications.

7. Data Visualization Techniques Scatter Plots Visualizing the relationship between two numerical variables, revealing patterns and trends. Line Charts Displaying trends and changes in a variable over time, useful for time series data. Bar Charts Comparing and contrasting categorical data, such as sales or revenue by product or region. Pie Charts Illustrating the proportional composition of a whole, such as market share or budget allocation.

8. Conclusion and Key Takeaways Diverse Applications Data science can be applied to a wide range of industries and domains, from healthcare and finance to e-commerce and transportation. Multidisciplinary Approach Effective data science requires a combination of statistical, computational, and domain-specific knowledge. Continuous Learning As technology and data sources evolve, data scientists must continuously update their skills and knowledge to stay relevant.