Week One
Day 1 – Introduction to Data Science
- Overview.
- Introduction to Data Science.
- Applications of Data Science.
- Types of Data.
- The Five Steps of Data Science.
- Descriptive, Predictive, and Prescriptive Analytics.
- Big Data.
- Installing Anaconda
Day 2 – Python Programming
- Python for Data Science.
- List comprehensions, Lambda expressions, etc.
- Pandas, Numpy, Matplotlib, Seaborn, etc.
- Data Handling.
- Attendees will be assumed to have a background in programming.
Day 3 – Statistics and Probability
- Concepts of Statistics
- Basis of Experimentation, normalization, and random sampling.
- Hypothesis testing, confidence intervals, interpretation of p-values.
- Introduction to Probability Theory.
Day 4 – Data Science Concepts
- Correlation vs Causation.
- Classification vs Regression.
- Evaluation of Classifiers and Regressors.
Day 5 – Time Series, NLP, and Visualization
- Time Series and Properties.
- Natural Language Processing (NLP).
- Visualization using Matplotlib, Seaborn, and Plotly.
Week Two
Day 6 – Machine Learning
- What is Machine Learning?
- Supervised vs Unsupervised Learning.
- Classification vs Regression.
- Bias and Variance.
- Linear Regression.
- Logistic Regression.
Day 7 – Ensembles, Bagging, and Boosting
- Ensemble Learning.
- Bagging and Boosting.
- Random Forests.
- AdaBoost, XGBoost, LightGBM, and CATBoost.
Day 8 – Clustering and Decision Trees
- Agglomerative Clustering.
- Divisive Clustering.
- K-Means and DBSCAN.
- EM Clustering.
- Decision Trees and ID3.
Day 9 – Deep Learning
- What is Deep Learning.
- Neural Networks.
- Building deep learning models with Keras.
- Recurrent Neural Networks (including LSTMs).
- Convolutional Neural Networks.
Day 10 – An Industry Perspective
- Data Science applications.
- Data Science technologies.
- Working as a Data Scientist.
