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Module 1: Introduction to Python Programming
Lesson Plan Learning Objectives Real World Examples Activities Discussion Questions Ways to Expand Learning
Lesson 1 – Understand the fundamentals of Python programming language. – Explain how Python is utilized in diverse industries, along with information science and internet development. – Install Python on your computer. – How do you think Python has contributed to the increase of facts science? – Research and talk about the extraordinary Python libraries used in information technological know-how.
Lesson 2 – Learn approximately variables, information types, and operators. – Demonstrate how to declare variables and perform primary operations. – Write an application to calculate the location of a rectangle given its duration and width. – What are the advantages of using variables in programming? – Design a software that converts temperature from Celsius to Fahrenheit and vice versa.
Lesson 3 – Understand manage systems and glide manipulate. – Illustrate the use of conditional statements (if-else) and loops (for, while) in Python programming. – Create an application that checks if a variety is on top or not. – How can manipulating structures enhance the performance of a program? – Develop a program that generates the Fibonacci sequence up to a given variety.
Module 2: Python Functions and Modules
Lesson Plan Learning Objectives Real World Examples Activities Discussion Questions Ways to Expand Learning
Lesson 1 – Understand the concept of features and their significance in programming. – Explore what capabilities may be used to modularize code and make it reusable. – Create a software that calculates the factorial of a given wide variety of the usage of a feature. – What are a few advantages of the usage of functions in programming? – Design a software that uses features to calculate the location and circumference of a circle

Module 2: Python Functions and Modules
Lesson
Plan Learning Objectives Real World Examples Activities Discussion Questions Ways to Expand Learning
Lesson 1 – Understand the idea of functions and their importance in programming. – Explore how functions can be used to modularize code and make it reusable. – Create a software that calculates the factorial of a given quantity using a function. – What are some advantages of using capabilities in programming? – Design a program that makes use of features to calculate the place and circumference of a circle.
Lesson 2 – Learn approximately function parameters and return values. – Demonstrate how capabilities can receive inputs (parameters) and convey outputs (go back values). – Write a application that calculates the sum of numbers in a listing using a function. – How can the use of parameters and return values make features greater versatile? – Develop a application that uses capabilities to validate user input (e.G., checking if a string is a palindrome).

Lesson 3 – Explore the concept of modules and their role in code design. – Discusses popular Python modules such as statistics, datetime, and random. – Create a program that uses random modules to generate random passwords. – How can the use of modules enhance code structure and reusable functionality? – Configure a system that uses modules to retrieve current weather information from the API and display it to the user.
Module 3: File Management and Data Processing
Lesson Plan Learning Objectives Real-World Example Assignment Discussion questions Strategies for Extending Learning
Lesson 1 – Learn how to read and write files in Python. – Shows how to save files and retrieve data. – Write a program that reads a text file and counts the number of words in it. – Under what circumstances would file control be useful in real world applications? – Creates a system that processes CSV files containing student data and generates reports with specific information (e.g., grade point average, highest score).

Lesson 2 – Understand the basics of data manipulation using built-in Python data structures (lists, tuples, dictionaries). – Describe how you can use data structures to organize and manipulate data. – Find the maximum number in the list using loops and data structures. – How can data structures improve data processing efficiency and effectiveness? – Establish a system of keywords used to store product information (e.g., name, price, quantity).
Lesson 3 – Explore the capabilities of external libraries for data manipulation, such as numPy and pandas. – Discusses real-world applications of data manipulation libraries such as data analysis and visualization in scientific research or business analysis. – Write a program that performs basic operations on arrays using the numPy library. – How can external libraries like numPy and Panda simplify complex data manipulation? – Configure a program that uses the pandas library to read a CSV file, extract and sort data based on specific criteria, and create graphs (e.g., bar charts, scatter plots) to analyze the data.

Module 4: Web Scraping and Data Visualization
Lesson Plan Lesson Objectives Real World Example Activities Discussion Questions Strategies for Extending Learning
Lesson 1 – Learn how to tug information from a website the usage of Python libraries like Beautiful Soup. – Explore how web scraping may be used to accumulate and examine records. – Write
software that extracts information (e.g., headlines, fees) from an information website using Beautiful Soup. -What ethical issues have to be taken into consideration whilst internet scraping? – A planning gadget that draws information from a weather website and presentations temperatures and forecasts for a given area.
Lesson 2 – Understand the fundamentals of statistics visualization using Matplotlib and other Python libraries. – Discuss the significance of data visualization in presenting insights and insights from records. – Design line plot to reveal stock fee trends. – How can statistical visualizations decorate information of complicated recording units? – Develop a software that makes use of Matplotlib to generate bar charts evaluating income performance of various products.

Lesson 3 – Explore superior facts visualization strategies the use of libraries like Seaborn and Plotly. – Demonstrate how superior visualizations can provide interactive and tasty methods to explore records. – Write an application that creates an interactive scatter plot the use of Plot to visualize the relationship among variables. – What are a few benefits of using interactive visualizations in comparison to static ones? – Design a program that combines more than one visualization technique (e.g., bar chart, heatmap) to investigate and present data.
Module five: Introduction to Data Analysis and Machine Learning
Lesson Plan Learning Objectives Real World Examples Activities Discussion Questions Ways to Expand Learning
Lesson 1 – Understand the basics of facts analysis and its significance in making informed selections. – Explore real-international eventualities wherein statistical analysis is used to pressure enterprise strategies or clinical discoveries. – Write a software that performs fundamental statistical evaluation on a given dataset, such as calculating mean, median, and standard deviation. – How can record analysis make a contribution to evidence-primarily based selection making? – Design a program that analyzes a dataset and generates visualizations (e.g., histograms, field plots) to become aware of outliers and trends.

Lesson 2 – Learn about the basics of system studying and its applications. – Discuss how machine gaining knowledge of algorithms is utilized in various domain names, consisting of photograph reputation, advice structures, and fraud detection. – Create a software that uses a system study algorithm (e.g., choice tree, logistic regression) to predict the target variable primarily based on entry features. – What are a few moral considerations whilst developing and deploying devices to get to know fashions? – Develop a software that applies a gadget learning set of rules (e.g., K-means clustering, random wooded area) to perform purchaser segmentation based totally on a given dataset.
Lesson 3 – Explore famous Python libraries for information analysis and gadget studying, together with pandas and scikit-learn. – Illustrate how these libraries offer effective gear for information manipulation, preprocessing, version education, and evaluation. – Write an application that makes use of pandas and scikit-learn to preprocess a dataset (e.g., coping with missing values, encoding express variables) and teach a machine gaining knowledge of the model for category or regression. – How can the usage of specialised libraries simplify the manner of information analysis and machine getting to know? – Design an application that combines multiple machine learning strategies (e.g., characteristic selection, hyperparameter tuning) to construct a strong predictive version for a selected challenge (e.g., patron churn prediction).

Module 6: Final Project – Real-World Application: Stock Market Analysis
In this very last challenge, students will follow their information of Python programming, statistical manipulation, internet scraping, fact visualization, and fundamental device learning to investigate and expect inventory marketplace trends. The venture plan includes subsequent components:

Data Collection:
Write a web scraping application the use of Beautiful Soup to extract ancient stock rate statistics from a economic internet site.
Store the scraped facts in a CSV file for further evaluation.
Data Preparation and Analysis:
Use pandas to preprocess the dataset, cope with lacking values, and carry out necessary variations.
Calculate statistical measures (imply, general deviation, and many others.) and become aware of any outliers or developments inside the information.
Data Visualization:
Utilize Matplotlib and/or Plotly to create visualizations inclusive of line plots, candlestick charts, or scatter plots to visualise stock charge moves and styles.
Predictive Modeling:
Apply machine mastering techniques from scikit-study, which include linear regression or help vector machines, to construct a predictive model based on historic inventory price data.
Split the dataset into training and trying out sets and evaluate the model’s overall performance.

Project Presentation:
Prepare a presentation summarizing the findings and insights won from the stock marketplace evaluation.
Include visualizations and discuss the accuracy of the predictive version.
Discussion Questions:

How can the evaluation of ancient stock fee statistics help buyers make knowledgeable decisions?
What are some limitations or demanding situations while predicting inventory marketplace traits using system learning?
What ethical concerns ought to be taken into consideration when the usage of predictive models in monetary markets?
Ways to Expand Learning:

Explore opportunity systems by studying algorithms and evaluating their overall performance in predicting stock marketplace traits.
Incorporate additional financial signs and outside elements (e.g., information sentiment analysis) into the predictive version for improved accuracy.
Research and analyze the impact of main economic activities or marketplace news on stock prices.

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