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dsi's Introduction

GA Logo Schedule

Schedules can be found in their respective week folders.

Contact Information

Our course Slack channel: dsi-sg-11

Instructor: Vidyut

Instructional Assistants:

Instructional Assistants:

Instructor Manager: Melanie Wu

Student Experience Coordinator: Aurelia Tan

Career Coach:

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

Monday Tuesday Wednesday Thursday Friday
Morning Public Holiday 1.01 Datatypes 1.03 Functions 1.05 Probability 1.07 Distributions - Continuous
Afternoon Public Holiday 1.02 Control Flow 1.04 Loops & List Comprehension 1.06 Distributions - Discrete 1.08 Central Limit Theorem
Labs Public Holiday 1_01 Pokemon Lab 1_02 Distributions Lab
Deadlines

Week 2 - Exploratory Data Analysis

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 - Project 1 2.03 Pandas Concatenation 2.06 Exploratory Data Analysis (EDA) 2.08 Inference - Hypothesis Testing Outcomes Programming
Labs 2_01 Titanic EDA Lab
Deadlines

Week 3 - Regression

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
Deadlines Project 1

Week 4 - Classification

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 - Project 2 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
Deadlines

Week 5 - Web Scraping, APIs and NLP

Monday Tuesday Wednesday Thursday Friday
Morning Explore APIs 5.01 Intro to HTML 5.03 API & Flask 5.05 NLP I 5.07 Naive Bayes
Afternoon Project 2 Presentations 5.02 Web Scraping using BeautifulSoup 5.04 Introduction to AWS 5.06 NLP II 5.08 Regex
Labs 5_01 Scraping Lab 5_02 NLP Lab Outcomes Programming
Deadlines Project 2

Week 6 - Advanced Supervised Learning

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 Project 3 Time 6.02 Bootstrapping and Bagging 6.04 Boosting 6.06 GLMs Project 3 Review & Prep
Labs 6.01 Supervised Model Comparison Lab Outcomes Programming
Deadlines Capstone Check-in 1

Week 7 - Unsupervised Learning

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 8_01 Clustering Lab 8_02 PCA Lab
Deadlines Project 3 Capstone Check-in 2

Week 8 - Correlated Data

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 8.04 PCA
Afternoon 7.02 Intro to Time Series/Autocorrelation 7.04 Advanced Time Series Analysis 7.06 Network Analysis Outcomes Programming 8.07 Missing Data Imputation
Labs 7_01 Correlated Data Lab 7_02 Time Series Lab 8_02 PCA Lab
Deadlines Capstone Check-in 3

Week 9 - Deep Learning

Monday Tuesday Wednesday Thursday Friday
Morning 10.01 Introduction to Neural Networks 10.02 Introduction to Keras 10.04 Convolutional Neural Networks 10.05 Recurrent Neural Networks 10.06 Introduction to TensorFlow
Afternoon Project 4 Presentations 10.03 Deep Learning Regularization 1-on-1 Outcomes Programming 1-on-1
Labs 10_01 Conceptual Neural Networks Lab 10_02 Applied Neural Networks Lab
Deadlines Capstone Check-in 2 Capstone Check-in 3 Capstone Check-in 4

Week 10 - Big Data & Data Engineering

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 Project 4 Presentations
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 11_02 Spark Model
Deadlines Capstone Check-in 5

Week 11 - Bayesian Statistics

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
Deadlines Capstone Check-in 6

Week 12 - Flex Time & Capstones

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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