Statistics for Data Analytics
About This Course
This two-day module offers an overview of statistics in data science, covering common reasoning such as deductive and inductive reasoning, populations and samples, and sampling methods, including non-probability and probability sampling. It emphasises exploring statistical studies with treatment and control groups, between and within-subject designs, and descriptive statistics, including measures of central tendency, dispersion, association, and asymmetry. Additionally, participants will learn about the four levels of measurement, probability concepts including probability distribution, the central limit theorem, estimation, and confidence intervals.
What You'll Learn
• Differentiate between various sampling methods and their respective applications.
• Analyse data by employing descriptive statistics, encompassing measures of central tendency, dispersion, association, and asymmetry.
• Understand concept of probability distribution, the central limit theorem, estimation, and confidence intervals, along with their practical implications.
Entry Requirements
• Participants should preferably have passed mathematics at least ‘O’ Level or equivalent.
• Participants should preferably have basic knowledge of statistics (descriptive and inferential statistics).
• Participants should be conversant with basic IT skills such as software installation, file management and web navigation.
• Participants are encouraged to complete the Foundation of Data Science before enrolling in this course.
• Participants are required to pass a pre-course assessment to ensure participants have the requisite knowledge of Python programming. This assessment can be waived if participants have completed both Fundamentals in Python (Basic) and Fundamentals in Python (Intermediate).