This course provides a comprehensive introduction to the field of data science. It covers essential concepts, tools, and techniques used in data science, including data analysis, visualization, and machine learning. The course is suitable for beginners who are interested in pursuing a career in data science.

Course Content:

1. Introduction to Data Science:

• • Overview of data science and its applications
  • The data science process
  • Setting up the environment (Python, Jupyter Notebooks, libraries)

2.Data Manipulation with Pandas:

• • Introduction to Pandas library
  • DataFrames and Series
  • Importing and exporting data
  • Data cleaning and preprocessing
  • Handling missing data

3.Data Visualization:

• • Introduction to Matplotlib and Seaborn
  • Creating basic plots (line, bar, scatter, histogram)
  • Customizing plots (labels, legends, titles)
  • Advanced visualizations (heatmaps, pair plots)
  • Interactive visualizations with Plotly

4.Exploratory Data Analysis (EDA):

• • Descriptive statistics
  • Identifying patterns and correlations
  • Data summarization techniques
  • Handling outliers

5.Introduction to Machine Learning:

• • Supervised vs. unsupervised learning
  • Key concepts (features, labels, training, testing)
  • Introduction to Scikit-learn library

6.Supervised Learning Algorithms:

• • Linear Regression
  • Logistic Regression
  • Decision Trees and Random Forests
  • Support Vector Machines (SVM)
  • Model evaluation (cross-validation, confusion matrix, accuracy, precision, recall)