Schedule
Schedules can be found in their respective week folders.
Our course Slack channel: dsi-sg-11
Instructional Assistants:
Instructional Assistants:
Instructor Manager: Melanie Wu
Student Experience Coordinator: Aurelia Tan
There might be minor changes to the course schedule due to industry guest speakers, career coach, alumni panel etc.
Week 1 () - Getting Started: Python for Data Science
Week 2 - Exploratory Data Analysis
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Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
Morning |
2.01 Pandas: Intro 1(Basics) |
2.02 Pandas: Intro 2 |
2.04 Principles of Data Visualization |
2.07 Inference/Confidence Interval |
2.05 Advanced transformation using Pandas |
Afternoon |
Lab/Project Time |
2.03 Pandas Concatenation |
2.06 Exploratory Data Analysis (EDA) |
2.08 Inference/Hypothesis Testing |
Outcomes Programming |
Labs |
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2_01 Titanic EDA Lab |
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Deadlines |
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Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
Morning |
Project 1 Presentations |
3.01 Linear Regression |
3.03 Bias-Variance Tradeoff |
3.05 Feature Engineering |
3.06 Regularization |
Afternoon |
Project 1 Presentations |
3.02 Regression Evaluation Metrics |
3.04 Train/Test Split + Cross Validation |
Lab/Project Time |
Lab/Project Time |
Labs |
1-on-1 |
3_01 Linear Regression Lab |
Outcomes Programming |
3_02 Regularization and Validation Lab |
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Deadlines |
Project 1 |
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Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
Morning |
3.07 Model Workflow |
4.01 Intro to Classification + Logistic Regression |
4.03 Classification Metrics I |
4.05 Hyperparameter Tuning and Pipelines |
4.06 API Integration & Consumption |
Afternoon |
Lab/Project Time |
4.02 k-Nearest Neighbours |
4.04 Classification Metrics II |
Outcomes Programming |
Lab/Project Time |
Labs |
Outcomes Programming |
4_01 Classification Model Comparison Lab |
4_02 Classification Model Evaluation Lab |
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Deadlines |
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Week 5 - Web Scraping, APIs and NLP
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Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
Morning |
Project 2 Presentations |
5.01 Intro to HTML |
5.03 API & Flask |
5.05 NLP I |
5.07 Naive Bayes |
Afternoon |
Explore APIs |
5.02 Web Scraping using BeautifulSoup |
5.04 Introduction to AWS |
5.06 NLP II |
5.08 Regex |
Labs |
5_01 Scraping Lab |
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5_02 NLP Lab |
Outcomes Programming |
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Deadlines |
Project 2 |
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Week 6 - Advanced Supervised Learning
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Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
Morning |
5.09 Object-Oriented Programming |
6.01 CART |
6.03 Random Forests and Extra Trees |
6.05 SVMs |
6.07 Gradient Descent |
Afternoon |
Lab/Project Time |
6.02 Bootstrapping and Bagging |
6.04 Boosting |
6.06 GLMs |
Project 3 Review & Prep |
Labs |
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6.01 Supervised Model Comparison Lab |
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Outcomes Programming |
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Deadlines |
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Capstone Check-in 1 |
Week 7 - Unsupervised Learning
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Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
Morning |
Project 3 Presentations |
8.01 Intro to Clustering: K-Means |
8.03 Clustering Walkthrough |
8.05 Recommender Systems I |
8.06 Recommender Systems II |
Afternoon |
1-on-1 |
8.02 DBSCAN Clustering |
8.04 PCA |
Outcomes Programming |
8.07 Missing Data Imputation |
Labs |
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8_01 Clustering Lab |
8_02 PCA Lab |
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Deadlines |
Project 3 |
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Capstone Check-in 2 |
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Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
Morning |
7.01 Intro to Correlated Data |
7.03 AR/MA/ARMA |
7.05 Spatial Data Analysis |
7.07 Benford's Law |
Project 4 Presentations |
Afternoon |
7.02 Intro to Time Series/Autocorrelation |
7.04 Advanced Time Series Analysis |
7.06 Network Analysis |
Outcomes Programming |
Lab/Project Time |
Labs |
7_01 Correlated Data Lab |
7_02 Time Series Lab |
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Deadlines |
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Capstone Check-in 3 |
Project 4 |
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Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
Morning |
10.01 Introduction to Neural Networks |
10.03 Deep Learning Regularization |
10.04 Convolutional Neural Networks |
10.05 Recurrent Neural Networks |
10.06 Introduction to TensorFlow |
Afternoon |
10.02 Introduction to Keras |
Lab/Project Time |
1-on-1 |
Outcomes Programming |
1-on-1 |
Labs |
10_01 Conceptual Neural Networks Lab |
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10_02 Applied Neural Networks Lab |
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Deadlines |
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Capstone Check-in 4 |
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Week 10 - Big Data & Data Engineering
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Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
Morning |
11.01 SQL I |
11.03 Introduction to Scala |
11.05 Classification & Regression in Spark |
11.07 Docker on AWS |
Lab/Project time |
Afternoon |
11.02 SQL II |
11.04 DataFrames in Spark |
11.06 Pipelines & GridSearch in Spark |
Outcomes programming |
Lab/Project time |
Labs |
11_01 SQL Lab |
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11_02 Spark Model |
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Deadlines |
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Capstone Check-in 5 |
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Week 11 - Bayesian Statistics
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Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
Morning |
9.01 Intro to Bayes |
9.03 PyMC & Bayesian Regression |
Flex Time |
Flex Time |
9.05 Markov chain Monte Carlo |
Afternoon |
9.02 Bayesian Inference |
9.04 Maximum Likelihood |
Flex Time |
Flex Time |
9.06 Bayesian Estimation & A/B Testing |
Labs |
9_01 Bayes Data |
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Deadlines |
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Capstone Check-in 6 |
Week 12 - Flex Time & Capstones
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Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
Morning |
Flex Time |
Flex Time |
Flex Time |
Flex Time |
Capstone Presentations |
Afternoon |
Flex Time |
Flex Time |
Flex Time |
Capstone Presentations |
Graduation! |
Deadlines |
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