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Data Science Projects with Python: Pandas to Machine Learning

Learn by building five end-to-end projects using Pandas, visualisation and scikit-learn

Ghosla Academy · Data Team

This course teaches data science through complete projects rather than isolated topics. Each project follows the same path: understand the question, explore and clean the data, build a model, evaluate it honestly and communicate the result. Datasets include public Indian data such as agriculture statistics alongside classic practice datasets, so you learn both the tools and the judgement data roles require.

What you will be able to do

  • Load, clean and transform data with Pandas
  • Create clear charts with Matplotlib and Seaborn
  • Build regression and classification models with scikit-learn
  • Evaluate models with proper train/test splits and metrics
  • Engineer features and tune hyperparameters
  • Present projects in notebooks and on GitHub

What you build

  • Exploratory analysis of Indian crop production data
  • House price prediction model
  • Customer churn classifier
  • Movie review sentiment analysis

What you need to start

  • Python basics
  • School-level maths (averages, percentages, graphs)

Curriculum · 8 sections, 28 lectures

Setup and workflow4 lectures
  1. The data science project lifecycleFree preview8 min
  2. Jupyter and Python environment setupFree preview12 min
  3. NumPy essentials10 min
  4. Quiz: workflow5 min
Pandas for analysis4 lectures
  1. DataFrames, selection and filtering15 min
  2. Handling missing values and types15 min
  3. groupby, merge and pivot14 min
  4. Quiz: Pandas6 min
Visualisation and EDA3 lectures
  1. Matplotlib and Seaborn basics14 min
  2. Telling a story with charts12 min
  3. Project: Indian crop production EDA22 min
Regression4 lectures
  1. Linear regression intuition14 min
  2. Train/test split and metrics14 min
  3. Feature engineering14 min
  4. Project: house price prediction22 min
Classification4 lectures
  1. Logistic regression and decision trees14 min
  2. Precision, recall and confusion matrix14 min
  3. Handling imbalanced classes12 min
  4. Project: churn classifier22 min
Better models3 lectures
  1. Random forests and gradient boosting14 min
  2. Cross-validation and grid search14 min
  3. Avoiding data leakage10 min
Text data3 lectures
  1. Cleaning text and TF-IDF12 min
  2. Naive Bayes for text classification12 min
  3. Project: review sentiment analysis22 min
Portfolio3 lectures
  1. Writing a project report10 min
  2. Publishing notebooks on GitHub10 min
  3. Data science interview topics8 min

Who this is for

  • Students aiming for data science internships
  • Analysts who want to move into machine learning
  • Engineering students needing a final-year project