Schedule

Schedules can be found in their respective week folders.

Contact Information

Our course Slack channel: dsi-sg-11

Instructor: Vidyut

Email: vidyut@babel.sg

Instructional Assistants:

Instructor Manager: Melanie Wu

Email: melanie.wu@generalassemb.ly
Slack: https://ga-students.slack.com/messages/@U3ZTGDZ9A

Student Experience Coordinator: Aurelia Tan

Email: aurelia.tan@generalassemb.ly
Slack: https://ga-students.slack.com/messages/@UDXRRRU0Z

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	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	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	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		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	Lab/Project 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	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
Deadlines				Capstone Check-in 3	Project 4

Week 9 - Deep Learning

	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		10_02 Applied Neural Networks Lab
Deadlines				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	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		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

Name		Name	Last commit message	Last commit date
Latest commit History 77 Commits
.ipynb_checkpoints		.ipynb_checkpoints
1.01-lesson-python-intro		1.01-lesson-python-intro
1.02-lesson-python-control-flow		1.02-lesson-python-control-flow
1.03-lesson-python-functions		1.03-lesson-python-functions
1.04-lesson-list-comprehension		1.04-lesson-list-comprehension
1.05-lesson-intro_to_probability		1.05-lesson-intro_to_probability
1.06-lesson-discrete-distributions		1.06-lesson-discrete-distributions
1.07-lesson-continuous-distributions		1.07-lesson-continuous-distributions
1.08-lesson-central-limit-theorem		1.08-lesson-central-limit-theorem
10.01-lesson-intro-to-neural-networks		10.01-lesson-intro-to-neural-networks
10.02-lesson-keras		10.02-lesson-keras
10.03-lesson-regularizing-optimizing-nn		10.03-lesson-regularizing-optimizing-nn
10.04-lesson-cnn		10.04-lesson-cnn
10.05-lesson-rnn		10.05-lesson-rnn
10.06-lesson-tensorflow		10.06-lesson-tensorflow
10_01-lab-neural_network_conceptual		10_01-lab-neural_network_conceptual
10_02-lab-neural_network_applied		10_02-lab-neural_network_applied
11.01-lesson-sql_I		11.01-lesson-sql_I
11.02-lesson-sql_II		11.02-lesson-sql_II
11.03-lesson-scala		11.03-lesson-scala
11.04-lesson-dataframes		11.04-lesson-dataframes
11.05-lesson-spark-ml		11.05-lesson-spark-ml
11.06-lesson-spark-pipelines		11.06-lesson-spark-pipelines
11.07-docker-on-aws		11.07-docker-on-aws
11_01-lab-sql		11_01-lab-sql
11_02-lab-spark-model		11_02-lab-spark-model
12-capstone project		12-capstone project
12-capstone-project		12-capstone-project
1_01-lab-pokemon		1_01-lab-pokemon
1_02-lab-distributions		1_02-lab-distributions
2.01-2.02-intro-to-pandas		2.01-2.02-intro-to-pandas
2.03-lesson-pandas-concatenation		2.03-lesson-pandas-concatenation
2.04-lesson-data-visualization		2.04-lesson-data-visualization
2.05-advanced-transformations-pandas		2.05-advanced-transformations-pandas
2.06-lesson-eda		2.06-lesson-eda
2.07-2.08-inference_extra practice		2.07-2.08-inference_extra practice
2.07-lesson-statistical_inference_confidence_intervals		2.07-lesson-statistical_inference_confidence_intervals
2.08-lesson-hypothesis_testing		2.08-lesson-hypothesis_testing
2_01-lab-pandas-titanic		2_01-lab-pandas-titanic
3.01-lesson-simple_linear_regression		3.01-lesson-simple_linear_regression
3.02-regression-metrics-lesson		3.02-regression-metrics-lesson
3.03-bias-variance-tradeoff		3.03-bias-variance-tradeoff
3.04-lesson-cross-validation-train-test-split		3.04-lesson-cross-validation-train-test-split
3.05-lesson-feature-engineering		3.05-lesson-feature-engineering
3.06-lesson-regularization-regression		3.06-lesson-regularization-regression
3.07-lesson-model-workflow		3.07-lesson-model-workflow
3_01-lab-linear_regression-regression		3_01-lab-linear_regression-regression
3_02-lab-cross_validation_regularization-regression		3_02-lab-cross_validation_regularization-regression
4.01-lesson-logistic_regression		4.01-lesson-logistic_regression
4.02-lesson-knn		4.02-lesson-knn
4.03-lesson-classification_metrics_i		4.03-lesson-classification_metrics_i
4.04-lesson-classification_metrics_ii		4.04-lesson-classification_metrics_ii
4.05-optimization-gridsearch_hyperparameters-lesson		4.05-optimization-gridsearch_hyperparameters-lesson
4.06-api-integration-and-consumption		4.06-api-integration-and-consumption
4_01-lab-classification_model_comparison		4_01-lab-classification_model_comparison
4_02-lab-classification_model_evaluation		4_02-lab-classification_model_evaluation
5.01-lesson-html-css		5.01-lesson-html-css
5.02-lesson-webscraping		5.02-lesson-webscraping
5.03-intro-to-apis-and-flask		5.03-intro-to-apis-and-flask
5.04-lesson-intro-to-aws		5.04-lesson-intro-to-aws
5.05-lesson-nlp_i		5.05-lesson-nlp_i
5.06-lesson-nlp_ii		5.06-lesson-nlp_ii
5.07-lesson-naive_bayes		5.07-lesson-naive_bayes
5.08-lesson-regex		5.08-lesson-regex
5.09-lesson-object-oriented-programming		5.09-lesson-object-oriented-programming
5_01-lab-beautiful_soup-webscraping		5_01-lab-beautiful_soup-webscraping
5_02-lab-intro_to_nlp		5_02-lab-intro_to_nlp
6.01-lesson-decision_trees		6.01-lesson-decision_trees
6.02-lesson-bagging		6.02-lesson-bagging
6.03-lesson-random-forests		6.03-lesson-random-forests
6.04-lesson-boosting		6.04-lesson-boosting
6.05-lesson-support_vector_machines-svm		6.05-lesson-support_vector_machines-svm
6.06-lesson-generalized_linear_models		6.06-lesson-generalized_linear_models
6.07-lesson-gradient_descent-master		6.07-lesson-gradient_descent-master
6_01-lab-supervised_learning_models		6_01-lab-supervised_learning_models
7.01-Intro-to-Correlated-Data		7.01-Intro-to-Correlated-Data
7.02-Intro-to-Time-Series-Autocorrelation		7.02-Intro-to-Time-Series-Autocorrelation
7.03-AR-MA-ARMA		7.03-AR-MA-ARMA
7.04-Advanced-Time-Series-Analysis		7.04-Advanced-Time-Series-Analysis
7.05-Spatial-Data-Analysis		7.05-Spatial-Data-Analysis
7.06-Network-Analysis		7.06-Network-Analysis
7.07-Benfords-Law		7.07-Benfords-Law
7_01-Correlated-Data-Lab		7_01-Correlated-Data-Lab
7_02-Time-Series-Lab		7_02-Time-Series-Lab
8.01-lesson-intro-to-Clustering-kmeans		8.01-lesson-intro-to-Clustering-kmeans
8.02-lesson-dbscan		8.02-lesson-dbscan
8.03-lesson-clustering-hierarchical_clustering		8.03-lesson-clustering-hierarchical_clustering
8.04-lesson-pca-intro-pca		8.04-lesson-pca-intro-pca
8.05-lesson-recommender-systems-i		8.05-lesson-recommender-systems-i
8.06-lesson-recommender-systems-ii		8.06-lesson-recommender-systems-ii
8.07-lesson-data_science_with_missing_data		8.07-lesson-data_science_with_missing_data
8.08-lesson-word_vectors		8.08-lesson-word_vectors
8.10-Extra Projects		8.10-Extra Projects
8_01-lab-battle-of-clusters		8_01-lab-battle-of-clusters
8_02-lab-principal_component_analysis		8_02-lab-principal_component_analysis
9.01-lesson-intro-to-bayes		9.01-lesson-intro-to-bayes
9.02-lesson-bayesian_inference		9.02-lesson-bayesian_inference
9.03-lesson-pymc3-bayes		9.03-lesson-pymc3-bayes
9.04-lesson-bayes-conjugacy-mle		9.04-lesson-bayes-conjugacy-mle
9.05-lesson-mcmc		9.05-lesson-mcmc

persocom01/GA-lessons

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