Python is a widely adopted programming language in data science due to its simplicity, extensive libraries, and active community support. It enables data scientists to perform a wide range of tasks, including data analysis, visualization, machine learning, deep learning, image processing, computer vision, and natural language processing (NLP)

Libraries :

• Pandas : For structured data operations, data manipulation, and analysis
• NumPy : For numerical computing, especially with multi-dimensional arrays
• Matplotlib : For creating static, interactive, and animated visualizations.
• Scikit-learn : For various machine learning algorithms
• TensorFlow/PyTorch : For deep learning applications

Applications :

• Data Cleaning and Preprocessing : Handling missing values, transforming data types, and preparing data for analysis
• Exploratory Data Analysis (EDA) : Discovering patterns and insights within datasets.
• Data Visualization : Creating charts and graphs to represent data effectively.
• Part-of-speech tagging : Identifying the grammatical role of each word in a sentence (e.g., noun, verb, adjective)
• Machine Learning Model Development : Building and training predictive models
• Deployment : Integrating data science models into production systems.

Course Details