/Data Science - Beginner

Course Curriculum

Section 1 · Module 1: Introduction to Data Science

○ What is Data Science? ○ Importance of Data Science in different industries ○ Overview of Data Science workflow

Section 2 · Module 2: Python for Data Science

○ Installing Python and setting up Jupyter Notebook ○ Python basics: variables, data types, loops, and functions ▶ Libraries: NumPy, Pandas, Matplotlib, and Seaborn

Section 3 · Module 3: Data Wrangling & Cleaning

○ Handling missing data and duplicates ○ Data manipulation using Pandas ○ Working with different file formats (CSV, JSON, Excel)

Section 4 · Module 4: Data Visualization

○ Creating plots with Matplotlib & Seaborn ○ Understanding histograms, scatter plots, bar plots, and heatmaps ○ Interactive visualizations with Plotly

Section 5 · Module 5: Introduction to Statistics & Probability

○ Descriptive statistics (mean, median, mode, variance, standard deviation) ○ Probability distributions and their applications ○ Hypothesis testing and confidence intervals

Section 6 · Module 6: Introduction to Machine Learning

○ Overview of supervised vs. unsupervised learning ○ Introduction to Scikit-learn ○ Implementing a simple linear regression model

Module 2: Python for Data Science

Libraries: NumPy, Pandas, Matplotlib, and Seaborn

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