Course Introduction:

Welcome to the Advanced Research Design and Data Analysis Course! This comprehensive course has been designed to equip you with the skills and knowledge necessary to conduct advanced research and analyze various types of data using cutting-edge tools and techniques.

In this course, you will delve into the world of research design and learn how to develop robust research methodologies that address complex research questions. You will explore different data collection methods, including the use of SurveyCTO and ODK, to gather high-quality data efficiently and effectively.

Next, you will gain expertise in Geographic Information System (GIS) and spatial data analysis, enabling you to visualize and analyze data with geographical components, opening new avenues for research insights.

The course also covers both qualitative and quantitative data analysis using Nvivo, Excel, SPSS, SAS, Stata, R, and Python. You will learn to explore, clean, and analyze data, derive meaningful insights, and draw evidence-based conclusions.

Moreover, you will master data visualization techniques using Power BI and Tableau, enabling you to create interactive and compelling visualizations that facilitate effective communication of research findings to diverse audiences.

The course includes a hands-on approach, allowing you to work on real-world projects and case studies. Additionally, you will learn about reproducible research practices, version control, and data security to ensure the integrity and credibility of your research.

By the end of this course, you will be equipped with a diverse skillset to tackle complex research problems, collect and analyze data efficiently, and create impactful visualizations. Whether you are a researcher, data analyst, or anyone looking to enhance their research and data analysis capabilities, this course will empower you to take your skills to the next level.

We are excited to have you on this learning journey. Let's embark on this enriching experience together and unlock the potential of advanced research design and data analysis!

Course Objective:

The primary objective of the Advanced Research Design and Data Analysis Course is to equip participants with the necessary knowledge and skills to conduct advanced research studies, collect data using modern data collection tools like SurveyCTO and ODK, analyze qualitative and quantitative data using various software packages such as Nvivo, Excel, SPSS, SAS, Stata, R, and Python, and create powerful data visualizations using Power BI and Tableau. By the end of this course, participants will be able to:

1. Understand and apply advanced research design principles to formulate research questions and design studies effectively.
2. Utilize SurveyCTO and ODK for efficient data collection in the field, ensuring accurate and reliable data capture.
3. Apply GIS techniques to analyze and visualize geographic data, enabling spatial insights and decision-making.
4. Master qualitative data analysis using Nvivo, gaining insights from textual, audio, and multimedia data.
5. Conduct quantitative data analysis using Excel, SPSS, SAS, Stata, R, and Python to draw meaningful conclusions from numerical data.
6. Explore machine learning and deep learning algorithms to analyze complex datasets and make predictive models.
7. Create interactive and impactful data visualizations using Power BI, Tableau, R, and Python to communicate research findings effectively.

Organization Benefit:

By investing in this course for their employees, organizations can reap several benefits:

1. Enhanced Research Capabilities: Participants will acquire advanced research and data analysis skills, allowing them to conduct more sophisticated studies and gain deeper insights into their business or industry.
2. Improved Data Management: Learning to use data collection tools like SurveyCTO and ODK helps organizations streamline their data collection processes, reducing errors and enhancing data accuracy.
3. Informed Decision-Making: Qualitative and quantitative data analysis techniques enable participants to make data-driven decisions, leading to more effective strategies and improved outcomes.
4. Better Visualization and Reporting: Participants will learn to create compelling data visualizations using Power BI and Tableau, facilitating clearer communication of research findings to stakeholders and management.
5. Data-Driven Innovation: The application of machine learning and advanced analytics techniques can foster innovation within the organization, uncovering new opportunities and potential areas for growth.
6. Efficient Resource Allocation: Accurate research design and data analysis can help organizations allocate resources more efficiently, optimizing costs and maximizing returns.

Course Duration:

20 days (The Duration, module sequence, and content can be customized based on specific training needs and objectives of participants)

Target Participants:

The course is ideal for individuals from diverse backgrounds, including but not limited to:

1. Students: Those pursuing undergraduate or graduate degrees in fields related to social sciences, business, engineering, or any discipline involving research and data analysis.
2. Researchers: Professionals engaged in research activities in academia, non-profit organizations, or corporate settings, seeking to enhance their research capabilities.
3. Data Analysts: Individuals working with data in various domains, aiming to expand their skills to encompass advanced data analysis and visualization techniques.
4. Data Scientists: Those interested in applying machine learning and deep learning algorithms to solve complex problems and make data-driven predictions.
5. Professionals in GIS and Geography: Those involved in geographic information systems, urban planning, environmental studies, or related fields, looking to strengthen their spatial data analysis and mapping abilities.

 

Course Outline

Module 1: Introduction to Research Methodology

1.1 Understanding the research process

1.2 Types of research methods

1.3 Formulating research questions and objectives

1.4 Developing a research proposal

 

Module 2: Research Ethics and Data Protection

2.1 Ethical considerations in research

2.2 Informed consent and confidentiality

2.3 Data protection and privacy regulations

 

Module 3: Advanced Research Design

3.1 Longitudinal and cross-sectional studies

3.2 Experimental and quasi-experimental designs

3.3 Case-control and cohort studies

3.4 Factorial and nested designs

 

Module 4: Survey Design and Sampling Techniques

4.1 Designing effective surveys

4.2 Types of sampling methods

4.3 Sample size determination

4.4 Handling non-response bias

 

Module 5: Introduction to SurveyCTO

5.1 Understanding SurveyCTO and its features

5.2 Designing data collection forms in SurveyCTO

5.3 Setting up data collection using mobile devices

5.4 Managing and exporting SurveyCTO data

 

Module 6: Introduction to ODK (Open Data Kit)

6.1 Understanding ODK and its features

6.2 Designing data collection forms in ODK

6.3 Setting up data collection using mobile devices

6.4 Managing and exporting ODK data

 

Module 7: Introduction to GIS and Spatial Data

7.1 Understanding GIS and its applications

7.2 Geographic data types and formats

7.3 GIS data collection methods

 

Module 8: Spatial Analysis with GIS

8.1 Spatial data visualization and exploration

8.2 Spatial query and analysis

8.3 Spatial interpolation and geostatistics

 

Module 9: Introduction to Qualitative Research

9.1 Qualitative research paradigms

9.2 Data collection methods (interviews, focus groups, etc.)

9.3 Data analysis techniques in qualitative research

9.4 Using Nvivo for qualitative data analysis

 

Module 10: Advanced Qualitative Data Analysis using Nvivo

10.1 Using queries for data exploration

10.2 Thematic analysis and coding in Nvivo

10.3 Framework analysis in Nvivo

10.4 Visualizing qualitative data in Nvivo

 

Module 11: Introduction to Quantitative Data Analysis

11.1 Overview of quantitative research

11.2 Data preparation and cleaning

11.3 Descriptive statistics

11.4 Data visualization techniques

 

Module 12: Data Analysis using Excel

12.1 Basic data manipulation in Excel

12.2 Data visualization in Excel

12.3 Statistical analysis in Excel

12.4 Pivot tables and advanced Excel functions

 

Module 13: Data Analysis using SPSS

13.1 Data management in SPSS

13.2 Descriptive statistics in SPSS

13.3 Hypothesis testing in SPSS

13.4 ANOVA and regression analysis in SPSS

 

Module 14: Data Analysis using SAS

14.1 Introduction to SAS software

14.2 Data preparation in SAS

14.3 Basic statistical procedures in SAS

14.4 Advanced statistical analysis in SAS

 

Module 15: Data Analysis using Stata

15.1 Data management in Stata

15.2 Basic statistical analysis in Stata

15.3 Regression and multivariate analysis in Stata

15.4 Survival analysis in Stata

 

Module 16: Data Analysis using R

16.1 Introduction to R programming

16.2 Data manipulation and visualization in R

16.3 Statistical analysis with R

16.4 Linear and nonlinear modeling in R

 

Module 17: Data Analysis using Python

17.1 Introduction to Python programming

17.2 Data manipulation and visualization in Python

17.3 Statistical analysis with Python

17.4 Introduction to machine learning with Python

 

Module 18: Advanced Quantitative Data Analysis

18.1 Factor analysis and principal component analysis

18.2 Structural equation modeling (SEM)

18.3 Hierarchical linear modeling (HLM)

18.4 Time series analysis with Python or R

 

Module 19: Mixed-Methods Research

19.1 Integrating qualitative and quantitative data

19.2 Triangulation and validation techniques

19.3 Analyzing mixed-methods data using Nvivo and statistical software

 

Module 20: Text Mining and Natural Language Processing (NLP)

20.1 Text preprocessing and tokenization

20.2 Sentiment analysis and text classification

20.3 Named Entity Recognition (NER) and topic modeling

 

Module 21: Web Scraping for Data Collection

21.1 Web scraping fundamentals

21.2 Extracting data from websites using Python or R

21.3 Handling challenges and ethical considerations

 

Module 22: Data Wrangling and Cleaning

22.1 Data quality assessment and improvement

22.2 Techniques for data cleaning and validation

22.3 Automation of data cleaning processes using Python or R

 

Module 23: Data Visualization with Power BI

23.1 Introduction to Power BI and its features

23.2 Data connection and transformation in Power BI

23.3 Creating interactive visualizations and dashboards

 

Module 24: Data Visualization with Tableau

24.1 Introduction to Tableau and its features

24.2 Data connection and transformation in Tableau

24.3 Creating interactive visualizations and dashboards

 

Module 25: Advanced Data Visualization in R with ggplot2

25.1 Customizing visualizations using ggplot2

25.2 Combining multiple plots and facets

25.3 Interactive visualizations with Shiny

 

Module 26: Advanced Data Visualization in Python with Matplotlib and Seaborn

26.1 Creating custom plots with Matplotlib

26.2 Statistical data visualization with Seaborn

26.3 Interactive visualizations with Plotly

 

Module 27: Data Visualization with D3.js

27.1 Introduction to D3.js library

27.2 Creating interactive and dynamic visualizations

27.3 Customizing D3.js visualizations

 

Module 28: Introduction to Machine Learning

28.1 Overview of machine learning concepts

28.2 Supervised and unsupervised learning

28.3 Applications of machine learning in research

 

Module 29: Advanced Machine Learning with Python

29.1 Model evaluation and hyperparameter tuning

29.2 Feature engineering and selection

29.3 Ensemble learning methods

 

Module 30: Deep Learning Fundamentals

30.1 Introduction to neural networks

30.2 Training and optimizing neural networks

30.3 Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)

 

Module 31: Applying Deep Learning with TensorFlow/Keras

31.1 TensorFlow and Keras overview

31.2 Implementing deep learning models in TensorFlow/Keras

31.3 Transfer learning and fine-tuning

 

Module 32: Time Series Analysis and Forecasting

32.1 Introduction to time series data

32.2 Time series visualization and decomposition

32.3 Forecasting using ARIMA, SARIMA, and Prophet

 

Module 33: Bayesian Data Analysis

33.1 Introduction to Bayesian statistics

33.2 Bayesian inference and Markov Chain Monte Carlo (MCMC)

33.3 Bayesian modeling in Python or R

 

Module 34: Introduction to Econometric Analysis

34.1 Understanding econometric models

34.2 Estimation methods: OLS, 2SLS, etc.

34.3 Causality and endogeneity

 

Module 35: Advanced Econometric Analysis

35.1 Panel data analysis

35.2 Time series econometrics

35.3 Instrumental variable methods

 

Module 36: Experimental Design and A/B Testing

36.1 Principles of experimental design

36.2 Conducting A/B tests and interpreting results

36.3 Validity and pitfalls in experimentation

 

Module 37: Advanced Statistical Modeling Techniques

37.1 Generalized linear models (GLMs)

37.2 Mixed-effects models

37.3 Non-parametric methods

 

Module 38: Data Mining and Machine Learning Applications

38.1 Association rule mining

38.2 Anomaly detection

38.3 Recommender systems

 

Module 39: Reproducible Research and Version Control

39.1 Importance of reproducibility in research

39.2 Creating reproducible workflows

39.3 Version control with Git

 

Module 40: Project Management and Collaboration

40.1 Organizing research projects

40.2 Collaboration tools and techniques

40.3 Managing data and files efficiently

 

Module 41: Data Security and Privacy

41.1 Data security best practices

41.2 Protecting sensitive information

41.3 Compliance with data protection regulations

 

Module 42: Reporting and Presenting Research Findings

42.1 Creating effective research reports

42.2 Data visualization for effective communication

42.3 Presenting research findings to diverse audiences

 

Module 43: Practical Applications and Case Studies

43.1 Applying research methodologies to real-world scenarios

43.2 Case studies in various research fields

43.3 Research project presentations by participants

 

Module 44: Course Conclusion and Final Project

44.1 Review of key concepts covered in the course

44.2 Participants' final projects presentation and feedback

 

General Notes

·         All our courses can be Tailor-made to participants' needs

·         The participant must be conversant in English

·         Presentations are well-guided, practical exercises, web-based tutorials, and group work. Our facilitators are experts with more than 10 years of experience.

·         Upon completion of training the participant will be issued with a Foscore development center certificate (FDC-K)

·         Training will be done at the Foscore development center (FDC-K) centers. We also offer inhouse and online training on the client schedule

·         Course duration is flexible and the contents can be modified to fit any number of days.

·         The course fee for onsite training includes facilitation training materials, 2 coffee breaks, a buffet lunch, and a Certificate of successful completion of Training. Participants will be responsible for their own travel expenses and arrangements, airport transfers, visa application dinners, health/accident insurance, and other personal expenses.

·         Accommodation, pickup, freight booking, and Visa processing arrangement, are done on request, at discounted prices.

·         Tablet and Laptops are provided to participants on request as an add-on cost to the training fee.

·         One-year free Consultation and Coaching provided after the course.

·         Register as a group of more than two and enjoy a discount of (10% to 50%)

·         Payment should be done before commence of the training or as agreed by the parties, to the FOSCORE DEVELOPMENT CENTER account, so as to enable us to prepare better for you.

·         For any inquiries reach us at training@fdc-k.org or +254712260031

 

·         Website:www.fdc-k.org