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Data Science is a way of processing large data sources to glean valuable insights into what makes organisations and industry tick. Data Scientists are expected to analyse information to allow organisations to improve the way they run, enhance their products and services and connect with a targeted group of stakeholders.
The best Data Scientists combine their skills in statistics, programming, data visualization and communications to enhance organizational efficiency.
- Gain ability to write Python code to solve problem using statistics and data science approach
- Able to do basic preparation work for datasets, ask relevant questions and uncover basic inferences by data visualization method
- Learn basic statistics and linear algebra concepts, such as mean, mean, mode, standard deviation, correlation, vector and matrices
Week 1-4: Fundamentals
Learn the basics of Data Science using Python, and become familiar with the core data science tools and libraries in Python including, Pandas, NumPy, Scikit-Learn and others, on Unix-like operating systems. We will also cover the basics of how computations are performed on the computer and other aspects related to software architecture that any aspiring data scientist should know.
- Learn the basics of Python, data manipulation with Pandas and mathematical and scientific libraries using NumPy and Scikit-learn
- Document the data exploration process using Jupyter Notebooks.
- Familiarize yourself with common data storage methods and file formats.
- Understand the data preparation and discovery pipeline.
- Mini Project 1: Exploration and data analysis
Week 5 -7: Statistical Modelling
Statistical modelling techniques are the bedrock of data science – in this module we delve further into mathematics and statistics. Students will evaluate and explore various prepared datasets using a variety of classical statistical methods to learn key statistical concepts in preparation for the more contemporary machine learning methods.
- Basic Math with Python
- Normal Distribution, Binomial Distribution and Poisson Distribution
- Understanding of Confidence Interval
- Preparation of training and hypothesis testing
- Analysis of Variance
- Deriving some standard statistical models like logistic regression to understand the mathematical theory behind the common library implementations.
- Full Tuition Fee: RM 6,000
- Monthly Instalment: Applicable with credit card payments
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