Session Programme
Week One
Day 1 鈥� 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 鈥� 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 鈥� 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 鈥� Data Science Concepts
- Correlation vs Causation.
- Classification vs Regression.
- Evaluation of Classifiers and Regressors.
Day 5 鈥� Time Series, NLP, and Visualization
- Time Series and Properties.
- Natural Language Processing (NLP).
- Visualization using Matplotlib, Seaborn, and Plotly.
鈥�
Week Two
Day 6 鈥� Machine Learning
- What is Machine Learning?
- Supervised vs Unsupervised Learning.
- Classification vs Regression.
- Bias and Variance.
- Linear Regression.
- Logistic Regression.
- Neural Networks.
Day 7 鈥� Clustering and Decision Trees
- Agglomerative Clustering.
- Divisive Clustering.
- K-Means and DBSCAN.
- EM Clustering.
- Decision Trees and ID3.
Day 8 鈥� Ensembles, Bagging, and Boosting
- Ensemble Learning.
- Bagging and Boosting.
- Random Forests.
- AdaBoost, XGBoost, LightGBM, and CATBoost.
Day 9 鈥� Deep Learning
- What is Deep Learning.
- Building deep learning models with Keras.
- Recurrent Neural Networks (including LSTMs).
- Convolutional Neural Networks.
Day 10 鈥� Miscellaneous Topics
- Na茂ve Bayes and SVMs.
- Data Privacy and Ethics.
- Data Science on the Cloud.
