Awareness Series: Foundations of Data Collection and Presentation

Describe basic data collection and preparation – how do you present the data, how do you capture the data, what are the considerations required for data preparation for data analysis, the importance of data visualisation for interpreting statistical data and presenting statistical findings

List the common types of statistical analysis (regression analysis, time series analysis, cluster analysis, decision tree, hypothesis testing)

Explain key statistical concepts and terminologies and their significance (what do they mean), e.g, sampling, confidence level, variables, mean, medium, mode, standard deviation, normal distribution, correlation coefficient, chi square, t-test, random variables etc

Select the appropriate type of statistical tool for required data analysis 

Identify variables that will be required for the analysis

State examples of application of different statistical analysis / techniques in real life and introduction to the various level of data analysis

Upon completion of this workshop, learners will be able to:

1. Demonstrate proficiency in data collection and preparation techniques.
2. Identify and apply various types of statistical analysis, including regression analysis, time series analysis, cluster analysis, decision trees, and hypothesis testing.
3. Explain key statistical concepts and terminologies, such as sampling, confidence level, variables, mean, median, mode, standard deviation, normal distribution, correlation coefficient, chi-square, t-test, and random variables.
4. Select and utilize appropriate statistical tools within Microsoft Excel for data analysis.
5. Recognize and manipulate variables required for statistical modeling.
6. Provide real-life examples illustrating the practical applications of statistical analysis techniques.
7. Distinguish between different levels of data analysis, from descriptive to inferential.

1. Students: Ideal for students from various disciplines who want to build a strong foundation in statistical analysis.
2. Business Professionals: For professionals seeking to enhance their data analysis skills and make data-driven decisions.
3. Researchers: Researchers in academia and industry looking to improve their data analysis and presentation capabilities.
4. Data Enthusiasts: Individuals with a passion for data who want to expand their knowledge and skillset in statistical analysis.
5. Excel Users: For those familiar with Microsoft Excel who want to leverage its capabilities for statistical analysis.

Dwight graduated with a degree in Biotechnology (from NUS in 2001), Advanced diploma in Pharmaceutical management (from MDIS in 2007) and Masters in Education (from NTU in 2013). In 2014, Dwight ventured into Data Analytics and Database Management and picked up Statistics/Machine Learning, Big Data analytics, text mining & social media analytics and web applications. His other expertise includes data science programming languages such as R and Python. He is also deeply passionate in deep learning using sequential models and sequence to sequence models using Keras and h2oAI machine & deep learning platforms and libraries. He is also a Senior Data Analyst, where he performs data analytics and create predictive models using healthcare data.