abanskota/t81_577_data_science

T81 577 Applied Data Science for Practitioners

Washington University in St. Louis

Instructor: Asim Banskota

Spring 2021, Wednesday, 6:00 PM - 9:00 PM , Cupples II, Room L015

Course Description

Organizations are rapidly transforming the way they ingest, integrate, store, serve data, and perform analytics. In this course, students will learn the steps involved with designing and implementing data science projects. Topics addressed include: ingesting and parsing data from various sources, dealing with messy and missing data, transforming and engineering features, building and evaluating machine learning models, and visualizing results. Using Python based tools such as Numpy, Pandas, and Scikit-learn, students will complete a practical data science project that addresses the entire design and implementation process. Students will also become familiar with the best practices and current trends in data science including code documentation, version control, reproducible research, pipeline automation, and cloud computing. Upon completion of the course, students will emerge equipped with data science knowledge and skills that can be applied from day one on the job.

Syllabus

Week	Content
Week 1 1/27/2021	Introductions 1.1. Data science in practice 1.2. Introduction to Python Programming Language 1.2.1. Python introduction and set up 1.2.2. Jupyter Notebook and Lab 1.3. An introduction to Amazon Web Service (AWS) Assignment 1.1: Install anaconda and test Jupyter notebook Assignment 1.2: AWS fundamentals
Week 2 2/3/2021	Python Fundamentals 2.1. Basic data types 2.2. Data structure and iterables 2.3 Conditional, iteration, and function in Python 2.4. Map, filter, reduce, comprehension Assignment 2: Programming practice assignment
Week 3 2/10/2021	Coding Best Practices in Data Science 3.1. Top-ten best-practices 3.2. Style guide for python code 3.3. Documenting python code 3.4. Version control 3.5. Code linting Assignment 3: Version control, project structure, and code documentation
Week 4 2/17/2021	Modeling Overview 4.1. Types of models 4.1.1. Causal/Prescriptive/Predictive 4.1.2. Statistical vs Machine learning 4.1.3. Blackbox vs Explainable 4.2. Model development steps
Week 5 2/24/2021	Accessing Data 5.1. Introduction to RESTful APIs 5.2. Interacting with API using Postman 5.3. Getting data from API 5.4. Parsing JSON data 5.5. Reading text files 5.6. Fetching data from PostgreSQL database Assignment: Finalization of final project topic and data set (Not graded)
Week 6 3/3/2021	Numpy/Pandas for Data Wrangling 6.1. Numpy data structure 6.2. Numpy functions 6.3. Vectorization 6.4. Pandas basics 6.5.Data manipulation with Pandas 6.6. Vizualization in Pandas Assignment 4: Exercise with Numpy and Pandas
Week 7 3/10/2021	Exploratory Data Analysis (EDA) 7.1. Introduction to EDA 7.2. Univariate analysis 7.3. Bivariate analysis 7.4. Temporal analysis 7.5. Spatial analysis Assignment 5: Vizualization and data summary
Week 8 3/17/2021	Data Preprocessing 8.1.Scikit-learn preprocessing 8.2. Missing value treatments 8.3. Standardization and Binning 8.4. Categorical encodings 8.5. Resampling 8.6. Feature engineering Assignment 6: Data preprocessing
Week 9 3/24/2021	Algorithms for predictive modelling- Part I 9.1. Basic-modelling-terminologies 9.2. linear regression 9.3. Naive Bayes classification 9.4. Logistic regression 9.5. Tree based algorithms 9.6. K-means clustering
Week 10 4/14/2021	Algorithms for predictive modelling- Part II 10.1. Support vector machine 10.2. Neural Network 10.3. Streamlining workflows with pipeline 10.4. Model Evaluation Assignment 7: Model Fitting and evaluation
Week 11 4/21/2021	Best practices in Machine Learning 11.1. Reducing bias and variances 11.2. Selecting train/dev/test dataset 11.3. Hyperparameter tuning Assignment: hyperparameter tuning
Week 12 4/28/2021	Deploy a Machine Learning model- Part I 13.1. Overview of model deployment 13.2 Basic terminologies 13.3. Deploy a model as a Flask app
Week13 5/5/2021	Deploy a Machine Learning model- Part II 14.1. Docker for data science 14.2. Introduction to Airflow Assignment: Final project due on April 30