Python for Data Science Course

✔ Installation: Anaconda, Jupyter Notebooks, Spyder.

✔ Syntax, Variables, Operators, and Data Types.

✔ Control Structures: Loops (for, while) and Conditional Statements (if-else).

✔ Functions and Functional Programming.

✔ Lists, Tuples, Sets, Dictionaries

✔ String Handling

✔ NumPy: Array creation, indexing, slicing, and broadcasting.

✔ SciPy: Basic scientific computing.

✔ Loading Data: Reading CSVs, Excel, SQL databases, and JSON.

✔ Pandas DataFrames & Series.

✔ Data Cleaning: Handling missing data, removing duplicates, and data type conversion.

✔ Data Manipulation: Filtering, sorting, merging, joining, and grouping data (groupby).

✔ Descriptive Statistics: Mean, median, mode, variance, and standard deviation.

✔ Data Visualization: Matplotlib and Seaborn for plotting histograms, scatter plots, box plots, and heatmaps.

✔ Introduction to Machine Learning workflows.

✔ Regression: Linear Regression, Polynomial Regression.

✔ Classification: Logistic Regression, K-Nearest Neighbors (KNN), Decision Trees, Random Forests.

✔ Clustering: K-Means Clustering.

✔ Model Evaluation: Confusion matrix, accuracy, precision, recall, and cross-validation.