Postgraduate Diploma In Applied Data Science

Course 1: Tools and Data Management

Python Basics - How to Translate Procedures into Code

Python data types (basic and Boolean), conditional statements, functions, assignment operations

Intermediate Python - Data Structures for Analysis

Lists, dictionaries, mutability, and iterations with examples on data structures

Relational Databases - Where Big Data Is Typically Stored

Basics of databases and normalization

SQL - Ubiquitous Database Format/Language

Using SQL for Python, SQL workbench, working with multiple tables

Data Extraction - Getting Data from the Internet - Part 1

Extracting data from the web using JSON, Google API, and XML

Data Extraction - Getting Data from the Internet - Part 2

Using the Beautiful Soup mechanism to extract data, the Epicurious example

Course 2: Statistics and Exploratory Data Analysis

Statistical Distributions - The Shape of Data

Types of distributions: Normal (examples), Poisson, Geometric, Exponential, Lognormal, and Bernoulli

Sampling - When You Can't or Won't Have ALL the Data

Size and sampling techniques, central limit theorem and motivation, sample size distribution (fixed sample size), polling techniques (given sample size, given target accuracy), estimating proportions

Hypothesis Testing - Answering Questions About Your Data

Calculating and interpreting confidence levels, t-tables and t-multipliers, determining P-values and A/B testing example

Data Analysis and Visualization - using Python's NumPy for analysis

Introduction to using Numpy and Pandas for data visualization, Pandas datareader, time-series analysis, risk return analysis, regression

Data analysis and visualization - using Python's Pandas for Data Wrangling

Data cleaning and data visualization using Pandas, using the groupby function to organize data

Course 3: Fundamentals of Machine Learning

Machine Learning - Basic Regression and Classification

Machine learning using wines dataset and rocks and mines dataset, classification metrics, classification metrics using rocks and mines dataset

Linear Regression

Introduction to linear regression, using dummy variables in regression, measuring outputs of regression, making predictions with regression, collinearity, overfitting and how to prevent it

Logistic Regression

Introduction to regression, classification problems, and building a logistic regression model, and practice

Machine Learning - Decision Trees and Clustering

Understanding decision trees – example and visualization, regression trees (using the wines dataset), classification trees (rocks and mines dataset)

Ensemble Methods

Decision trees, bagging and boosting concepts, feature importance, and hyperparameter tuning

Naïve Bayes Classifiers

Discrete and conditional probabilities, Baye’s theorem, spam filtering, and practice

Neural Networks

Neural networks in keras, the perceptron, real-life examples: movie review classification and predicting housing prices

K-means Clustering

Unsupervised models, k-means clustering models and examples, Gaussian mixtures and examples

Dimensionality Reduction

Data projections, dimensionality reduction (DR), other DR techniques, principal component analysis

Text Mining - Automatic Understanding of Text

Text mining techniques: sentiment analysis, complexity analysis, and named entity analysis, text summarization, and topic modelling techniques

Time Series Analysis

Datetime and introduction to time series, exploring time series, descriptive statistics, partial autocorrelation, autoregressive models, the ARIMA model

Capstone Project

Acting in the role of consultant, test the efficacy of an office supply company’s telemarketing campaigns for a select audience and help them leverage the test results to their advantage.