Python for Data Science, 10 weeks guidelines for the hands-on projects in each week:

Week 1: Introduction to Python and Data Manipulation

• Hands-on Project: Write a Python program that takes user input and performs basic operations like arithmetic calculations, conditional statements, and loops. Practice using Python lists to manipulate and analyze data.

Week 2: Data Analysis with Pandas

• Hands-on Project: Choose a dataset of your choice (e.g., CSV file, Excel sheet) and load it into a Pandas DataFrame. Perform data cleaning tasks such as handling missing values, removing duplicates, and transforming data types. Perform basic exploratory data analysis (EDA) tasks like calculating summary statistics, visualizing data distributions, and identifying correlations.

Week 3: Data Visualization with Matplotlib and Seaborn

• Hands-on Project: Select a dataset and create visualizations using Matplotlib and Seaborn. Choose appropriate plot types (e.g., line plot, bar chart, scatter plot) to showcase different aspects of the data. Customize the visualizations by adding labels, titles, and legends to enhance clarity and interpretability.

Week 4: Statistical Analysis with SciPy and Statsmodels

• Hands-on Project: Choose a dataset and perform statistical analysis using SciPy and Statsmodels. Apply hypothesis testing to compare groups or test hypotheses, calculate confidence intervals for population parameters, and perform regression analysis to explore relationships between variables. Provide interpretations of the statistical results.

Week 5: Machine Learning Fundamentals with Scikit-learn

• Hands-on Project: Select a dataset suitable for a supervised learning task (e.g., classification or regression). Split the dataset into training and testing sets. Apply a Scikit-learn machine learning algorithm (e.g., logistic regression, decision tree, random forest) to train a model on the training set. Evaluate the model's performance on the testing set and report relevant metrics (e.g., accuracy, precision, recall, or mean squared error).

Week 6: Unsupervised Learning and Clustering

• Hands-on Project: Choose a dataset suitable for unsupervised learning or clustering. Apply a clustering algorithm (e.g., K-means, hierarchical clustering) to group similar data points together. Visualize the clusters and interpret the results. Explore different clustering techniques and evaluate their effectiveness using appropriate metrics.

Week 7: Natural Language Processing (NLP) Basics

• Hands-on Project: Select a text dataset (e.g., a collection of articles or social media posts) and perform basic NLP tasks using NLTK. Tokenize the text, remove stopwords, perform stemming or lemmatization, and calculate word frequencies. Apply sentiment analysis techniques to classify the sentiment of the text (positive, negative, neutral).

Week 8: Deep Learning Fundamentals with TensorFlow

• Hands-on Project: Choose a classification problem (e.g., image classification) and build a simple neural network model using TensorFlow. Preprocess the data, design the architecture of the neural network, train the model, and evaluate its performance on a validation set. Experiment with different hyperparameters or network architectures to improve the model's accuracy.

Week 9: Time Series Analysis

• Hands-on Project: Select a time series dataset (e.g., stock prices, weather data) and perform time series analysis using Statsmodels. Explore different time series decomposition techniques, build forecasting models (e.g., ARIMA, LSTM), and evaluate the model's performance using appropriate metrics. Visualize the forecasted values and compare them to the actual data.

Week 10: Final Project

• Hands-on Project: Choose a data science project that aligns with your interests. Identify a problem to solve, gather relevant data, and apply the skills you've learned throughout the course. Perform data preprocessing, exploratory analysis, feature engineering, model training, and evaluation. Communicate your findings and insights effectively through visualizations and a final report or presentation.

For each project, it's important to document your process, explain the choices you made, and provide clear interpretations of your results. Remember to share your code, visualizations, and any additional insights you gained from the project.

Feel free to explore additional datasets, modify the projects based on your interests, or collaborate with other students to enhance your learning experience. Enjoy the hands-on projects and have fun learning data science with Python!