- Introduction to Data Science and Python
- Data Preparation and Cleaning
- Exploratory Data Analysis (EDA)
- Data Visualization with Python
- Supervised Machine Learning
What is Data Science? An overview of the field of data science, including the importance of data in decision making and the role of data scientists in organizations.
Introduction to Python: A brief overview of the Python programming language, including its syntax, data structures, and common libraries.
Getting Started with Python for Data Science: An introduction to using Python for data science, including how to install and configure Python, how to use Jupyter notebooks, and how to load and manipulate data using Python.
Python Libraries for Data Science: An overview of the most popular Python libraries for data science, including NumPy, Pandas, and Matplotlib.
Basic Data Types and Structures in Python: An introduction to the basic data types and structures in Python, including lists, dictionaries, and arrays.
Data Loading and Cleaning in Python: Techniques for loading and cleaning data in Python, including reading data from files and databases, handling missing values, and transforming data.
Importance of Data Preparation: An overview of why data preparation is an important step in the data science process, and why it can often take up to 80% of the time spent on a project.
Handling Missing Values: Techniques for identifying and handling missing values, including mean imputation, median imputation, and interpolation.
Handling Outliers: Techniques for identifying and handling outliers, including Z-score method, IQR method, and Tukey method.
Data Transformation: Techniques for transforming data to meet the requirements of specific algorithms, including normalization, standardization, and encoding.
Dealing with Duplicates: Techniques for identifying and dealing with duplicate records in a dataset, including methods for finding and removing duplicates.
Data Quality Assessment: An overview of how to assess the quality of data, including methods for checking the validity, accuracy, completeness, and consistency of data.
Introduction to EDA: An overview of the purpose and objectives of Exploratory Data Analysis (EDA), including its role in the data science process and the benefits of conducting EDA.
Univariate Analysis: Techniques for analyzing individual variables, including measures of central tendency (mean, median, mode), measures of dispersion (range, variance, standard deviation), and graphical techniques (histograms, box plots, density plots).
Bivariate Analysis: Techniques for analyzing the relationship between two variables, including scatter plots, correlation coefficients, and regression analysis.
Multivariate Analysis: Techniques for analyzing the relationships among multiple variables, including dimensionality reduction, principal component analysis (PCA), and clustering.
Data Visualization: An overview of the importance of data visualization in EDA, including best practices for creating effective visualizations and the use of popular visualization libraries such as Matplotlib and Seaborn.
Case Studies: An exploration of real-world EDA case studies, where students work through the process of conducting EDA on real-world datasets and learn how to apply the techniques they have learned to solve real-world problems.
Introduction to Data Visualization: An overview of the importance of data visualization in the data science process, including its role in Exploratory Data Analysis (EDA) and the benefits of using visualization to communicate insights.
Matplotlib: An introduction to the Matplotlib library, including how to create basic visualizations such as line plots, scatter plots, bar plots, and histograms.
Seaborn: An introduction to the Seaborn library, including how to create advanced visualizations such as box plots, violin plots, and heatmaps.
Plotting with Pandas: An overview of how to use the Pandas library to create visualizations, including how to create line plots, bar plots, histograms, and scatter plots.
Interactive Visualizations: An introduction to interactive visualizations, including how to create interactive plots and dashboards using libraries such as Plotly and Bokeh.
Case Studies: An exploration of real-world data visualization case studies, where students work through the process of creating visualizations for real-world datasets and learn how to apply the techniques they have learned to solve real-world problems.
5. Supervised Machine Learning
Introduction to Supervised Machine Learning: An overview of the purpose and objectives of Supervised Machine Learning, including its role in the data science process, and the differences between supervised and unsupervised learning.
Linear Regression: An introduction to linear regression, including the concept of linear models, the ordinary least squares (OLS) method, and how to evaluate the performance of a linear regression model.
Logistic Regression: An introduction to logistic regression, including how to build logistic regression models, how to evaluate model performance, and how to interpret model coefficients.
Decision Trees and Random Forests: An introduction to decision trees, including how to build decision trees and random forests, how to evaluate model performance, and how to interpret model predictions.
Support Vector Machines (SVM): An introduction to Support Vector Machines (SVM), including how to build SVM models, how to evaluate model performance, and how to interpret model predictions.
K-Nearest Neighbors (KNN): An introduction to K-Nearest Neighbors (KNN), including how to build KNN models, how to evaluate model performance, and how to interpret model predictions.
Model Evaluation and Selection: An overview of model evaluation and selection, including techniques for evaluating the performance of different models and how to select the best model for a particular problem.
Case Studies: An exploration of real-world supervised machine learning case studies, where students work through the process of building and evaluating supervised machine learning models for real-world datasets and learn how to apply the techniques they have learned to solve real-world problems.
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