Advanced Diploma in Data Science (Machine Learning)
To understand the relationships within big data and develop intelligent applications to gain competitive edge within your industry.
Course Outline
Participants will learn to analyse and visualise data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist statistical inference and modelling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualise data with R packages for data analysis.
Introduction
- Data science Basics
- Data importation
- Data cleaning
- Data manipulation
- Data visualization
Statistics
- Hypothesis testing
- Univariate / Bivariate/ Multivariate Analysis
- Correlation and Regression
- Chi-square, T-Test, ANOVA
Machine Learning
- Supervised and unsupervised learning
- Regression vs Classification
- Basics of sentiment Analysis
- Basics of forecasting and time series
Data Science Basics
- Data importation
- Data cleaning
- Data manipulation
- Data visualization
Statistics
- Hypothesis testing
- Univariate/ Bivariate/ Multivariate Analysis
- Correlation and Regression
- Chi-square, T- Test, ANOVA
Machine Learning
- Supervised and unsupervised learning
- Regression vs Classification
Advanced Analytics
- Tree Based Algorithm
- Bagging and Boosting
- Cross validation
- Model Tuning
- Deep Learning(Neural Network)
- Sentiment Analysis
- Forecasting and Time Series Analysis
Analytics Project Lifecycle
- Data Collection
- Data Cleaning
- Data Exploration
- Feature Engineering
- Model Building
- Model Evaluation and Tuning
- Model Deployment
