Data Analytics I

• • Introduction to Statistics & Analytics
  • - What is Statistics?
  • - Types of Data, Data Collection and Sampling
  • Analysing Data
  • - Graphical Descriptive Techniques - Nominal Data
  • - Graphical Descriptive Techniques - Numerical Data
  • - Measures of Central Locations
  • - Measures of Variability
  • - Measures of Relative Standings
  • Probability & Chance
  • - Probability
  • - Random Variables and Discrete Probability Distributions
  • - Random Variables and Continuous Probability Distributions
  • Estimation & Hypothesis Testing
  • - Statistical Inference and Sampling Distribution
  • - Estimation - Single Population
  • - Hypothesis Testing
  • - Estimation - Two Populations
  • Correlation and Regression
  • - Covariance and Correlation
  • - Linear Regression Model

Others

Successful completion and passing of four core open access courses and admission into an award program.

Additional requirements

• Equipment requirements - Headphones or speakers (required to listen to lectures and other media). Headset including microphone (highly recommended). Webcam (may be required for participation in virtual classrooms and/or presentations.
• Software requirements - It is essential for students to have reliable internet access in order to participate and complete your units. MyUni is the University of Adelaide's online learning environment. MyUni provides access to various features including announcements, course materials, discussion boards and assessments.
• Other requirements - Calculator. Students will need a calculator; a basic one that can take squares, square roots etc is sufficient.

In today's world, good decision-making relies on data and data analysis. This course helps students develop the understanding that they will need to make informed decisions using data and to communicate the results effectively. The course is an introduction to the essential concepts, tools and methods of statistics for students in business, economics and similar disciplines, although it may have wider interest. The focus is on concepts, reasoning, interpretation and thinking rather than computation, formulae and theory. Much of the work will require students to write effectively and communicate their ideas with clarity. The course covers two main branches of statistics: descriptive statistics and inferential statistics. Descriptive statistics includes collecting data and summarising and interpreting them through numerical and graphical techniques. Inferential statistics includes selecting and applying the correct statistical technique in order to make estimates or test claims about a population based on a sample. Topics covered may include descriptive statistics, correlation and simple regression, probability, point and interval estimation, hypothesis testing, multiple regression, time series analysis and index numbers. Students will also develop some understanding of the limitations of statistical inference and of the ethics of data analysis and statistics.

All assessment is assignment based.

• Weekly quizzes (20%)
• Descriptive Statistics Case Study (25%)
• Probability & Hypothesis Testing Case Study (30%)
• Regression Case Study (25%)

For textbook details check your university's handbook, website or learning management system (LMS).