Course 4. Data Preparation and Analysis
This course has 5 short lectures.
Lecture 1: This lecture will discuss some fundamentals of data – why they are important, what they are used for, and the things we must remember when we handle and deploy data. These are basic concepts that will hopefully enrich our appreciation and understanding of data, and how they relate to things that matter to us.
Lecture 2: This discussion will introduce what data driven development (3D) is and the importance of data driven. The session will give some examples of 3D, the gaps in 3D and how we can bridge the gap.
Lecture 3: The discussion will also provide an overview of the data that LGUs are required to collect under national policies. The discussion will provide a strategy on how to simplify data collection to meet the requirements for analysis and reporting.
Lecture 4. This session will focus on 3D and Planning and Budgeting in LGUs. The session will cover 4 key aspects: Introduction to Planning and Budgeting and Data, Planning and Budgeting Process in LGUs, 3D Principles and Elements in Local Planning and Budgeting, and finally, an application workshop.
Lecture 5. This session provides an overview of the Mandanas Garcia Ruling as a backdrop for data driven development at the local government level. The lecture discusses the challenges and opportunities of the Mandanas Ruling for LGU stakeholders.
| Module Title | Data Analysis |
| Course Outcomes | Lecture 1. ● Identify the key steps in performing data preparation ● Recognize the common data anomalies ● Have the know-how in cleaning and wrangling data Lecture 2. ● Recognize the concept and importance of quantitative analysis in decision making. ● Awareness of the common quantitative analysis tools. ● Develop the basic skills in performing quantitative analysis, interpreting results and formulating insights from data. Lecture 3. ● Understand the qualitative concepts ● Develop skills in qualitative analysis ● Apply qualitative data analysis. Lecture 4. ● Recognize the concept and importance of data visualization in decision making. ● Awareness of the common visualization tools. ● Develop the basic skills in performing data visualization, interpreting results and formulating insights from data. |
| Outline/ Content | 1. Data Preparation 1.1 Importance of data preparation 1.2 Data cleaning a. Ensure columns have proper labels b. Manage duplicates c. Standardize values d. Handle blank fields / missing values 1.3 Data wrangling a. Unifying or joining datasets from multiple sources b. Data transformation c. Examples using the COVID19 sample dataset from DOH 2. Quantitative Analysis 2.1 What is Quantitative Data Analysis? 2.2 Why use Quantitative Analysis? How does quantitative analysis work? 2.3 Common tools for quantitative analysis a. Excel b. SPSS c. SAS d. Stata e. R f. Python 2.4 Methods and interpretation a. Frequency/Distribution b. Measures of Central Tendency c. Measures of Dispersion d. Correlation 3. Qualitative Analysis 3.1 Understanding qualitative data 3.2 Interpretation and analysis 3.2.1 Codes and Coding a. Descriptive codes b. In vivo coding c. Process coding d. Emotion coding e. Values coding f. Evaluation coding g. Causation coding h. Attribute coding 3.2.2 Jotting 3.2.3 Analytic Memoing 4. Data Visualization 4.1 What is Data Visualization 4.2 Examples 4.3 Do’s and Don’ts 4.4 Common tools for data visualization a. Excel b. Tableau c. PowerBI (Microsoft) d. Data Studio (Google) e. R f. Python |
Lecturers    | Jeddahlyn Gacera is a Data Associate of Action for Economic Reforms. She worked as an ETL consultant for 5 years and is currently a Data Scientist at Accenture. There, she collaborates with and delivers data-driven solutions to a leading financial services provider in the country, and one of the biggest platform companies in the world. She has a bachelor’s degree in Mathematics from University of the Philippines Diliman, and a master’s degree in Data Science from the Asian Institute of Management. Carlo Angelo Cordero is currently AER’s data and research officer. He holds a Master’s Degree in Data Science from Asian Institute of Management. He has worked in the finance industry for a decade where he specialized in project management, financial modeling, budgeting and planning, and equity research, among other things. He finished his bachelor’s degree in Economics at the De La Salle University.. Dr. Reymund Flores is an Assistant Professor of Politics and Public Administration at the West Visayas State University. His work experience and research include disaster risk management planning, housing, risk/crisis communication, and development. Daffodil Santillan is a Data Associate of the PH3D Project co-funded by the Action for Economic Reforms and the European Union. She is currently pursuing a master’s degree in Development Economics at the School of Economics, University of the Philippines Diliman. Previously, she served as a Chief of Staff at the Social Security System (SSS) for five years. Her research interests are in economic growth & government, social welfare & income redistribution. |
About Instructor
+13 enrolled Course Includes
- 8 Lessons
- 8 Quizzes
- Course Certificate