Methodical recommendations for laboratory work on the topic "Using Python within Jupyter Notebook and Google Colab environments"

Abstract

Data Science is a multidisciplinary field that extracts insights and knowledge from data using techniques from statistics, computer science, and domain expertise. It involves collecting, cleaning, analyzing, and visualizing data to make informed decisions or create predictive models. With applications ranging from healthcare to finance and marketing, Data Science has become a cornerstone of innovation in modern industries. Python has emerged as the essential programming language for Data Science due to its simplicity, versatility, and rich ecosystem of libraries. Libraries like Pandas and NumPy enable efficient data manipulation and numerical computations, while Matplotlib and Seaborn are widely used for data visualization. Moreover, Python's machine learning libraries, such as Scikit-learn, TensorFlow, and PyTorch, provide powerful tools for building predictive models. Its readable syntax and supportive community make Python an accessible choice for beginners and experts alike, ensuring it remains the go-to language in this field. Jupyter Notebook plays a crucial role in Data Science workflows. It is an interactive environment that allows data scientists to write and execute Python code in a cell-based format, combining code, visualizations, and explanatory text in a single document. This format makes Jupyter Notebook ideal for exploratory data analysis, as it enables immediate feedback and iterative experimentation. Additionally, it facilitates collaboration and communication by allowing users to share notebooks that clearly present their methods, results, and insights. Objective: gain skills in installing, configuring, and mastering the basics of working with Jupyter Notebook.