Format: Live instructor-led online training via Zoom / Microsoft Teams

Data Science Using Python, R, and NVivo

Introduction

The Data Science Using Python, R, and NVivo Course is a comprehensive training designed for professionals looking to master data analysis, visualization, and qualitative research. In today's data-driven world, proficiency in tools like Python, R, and NVivo is crucial for making informed decisions. This course equips participants with the skills to handle large datasets, perform statistical analysis, and interpret qualitative data effectively. By the end of the training, participants will be proficient in using Python for data manipulation, R for statistical analysis, and NVivo for qualitative data analysis.

The course focuses on building strong foundational knowledge in data science methodologies, including data cleaning, exploration, and advanced analytics. Participants will learn to use Python libraries like Pandas, NumPy, and Matplotlib for data processing, while R will be used for statistical modeling and visualization through ggplot2 and other key packages. The integration of NVivo will provide a unique skill set, allowing participants to handle complex qualitative datasets for a holistic view of data insights, making them adept at mixed-methods research.

This training will also cover data visualization techniques, machine learning concepts, and predictive analytics. Through practical, real-life case studies, participants will learn to apply these tools to solve real-world problems. The course is designed for data professionals, researchers, and decision-makers looking to harness the power of both qualitative and quantitative data. Additionally, hands-on exercises will ensure that learners gain practical skills that are directly applicable in sectors like healthcare, finance, marketing, and academia.

By the end of this course, participants will be able to conduct comprehensive data analysis, develop predictive models, and produce meaningful visualizations. This training will not only enhance technical skills but also improve the participants' ability to communicate findings effectively to stakeholders. This course is ideal for those seeking to build a career in data science, research, and analytics, aiming to drive evidence-based decision-making in their respective fields.

Course Objectives

1. Understand the fundamentals of data science and its applications.
2. Learn data manipulation and analysis using Python and R.
3. Develop expertise in data visualization with Python's Matplotlib and R's ggplot2.
4. Conduct qualitative data analysis using NVivo.
5. Gain proficiency in machine learning algorithms for predictive analytics.
6. Master techniques for data cleaning and preparation.
7. Explore statistical analysis using R for data-driven insights.
8. Implement mixed-methods research combining quantitative and qualitative data.
9. Build skills in developing data dashboards for business intelligence.
10. Communicate data insights effectively through clear visualizations and reports.

Organization Benefits

1. Enhanced decision-making capabilities with comprehensive data insights.
2. Improved data analysis efficiency with advanced tools like Python, R, and NVivo.
3. Access to high-quality data visualizations for clear communication of findings.
4. Ability to predict trends and outcomes using machine learning techniques.
5. Strengthened research capabilities with a mixed-methods approach.
6. Optimized data processing through automation and programming.
7. Cost savings by equipping teams with in-house data science skills.
8. Higher productivity due to streamlined data analysis workflows.
9. Competitive advantage by making evidence-based business decisions.
10. Increased organizational knowledge with robust qualitative and quantitative analysis.

Target Participants

• Data analysts looking to advance their skills in Python, R, and NVivo.
• Researchers aiming to integrate quantitative and qualitative analysis.
• Business professionals seeking to leverage data for strategic decisions.
• Academics conducting mixed-methods research.
• Professionals in healthcare, finance, marketing, and social sciences.
• Data science enthusiasts looking to enter the field.
• Project managers handling data-driven projects.
• Statisticians interested in advanced data visualization techniques.
• Policy makers who require data insights for decision-making.
• IT professionals interested in data science and analytics tools.

Course Outline

Module 1: Introduction to Data Science and Tools

1. Overview of data science concepts and significance.
2. Introduction to Python, R, and NVivo for data analysis.
3. Setting up the environment for Python and R.
4. Understanding the role of NVivo in qualitative data analysis.
5. Case study on data science applications in different industries.
6. Case Study: Application of data science in healthcare analytics.

Module 2: Data Collection and Preparation

1. Techniques for data collection using online and offline methods.
2. Data cleaning and preprocessing in Python.
3. Handling missing data and outliers in R.
4. Importing and organizing qualitative data in NVivo.
5. Creating and managing codebooks in NVivo.
6. Case Study: Cleaning large datasets for predictive modeling.

Module 3: Data Manipulation with Python and R

1. Data manipulation using Python’s Pandas library.
2. Exploring data frames, arrays, and data types.
3. Data aggregation and grouping in R.
4. Using dplyr and tidyr for data manipulation in R.
5. Combining datasets from multiple sources.
6. Case Study: Data manipulation for customer segmentation.

Module 4: Statistical Analysis and Modeling

1. Basic statistical concepts and descriptive statistics in R.
2. Hypothesis testing and regression analysis.
3. ANOVA and Chi-square tests in R.
4. Time-series analysis for forecasting in Python.
5. Introduction to predictive modeling and machine learning.
6. Case Study: Predictive modeling for sales forecasting.

Module 5: Qualitative Data Analysis using NVivo

1. Setting up projects and importing data into NVivo.
2. Coding and analyzing qualitative data.
3. Creating themes and patterns from qualitative insights.
4. Using NVivo for mixed-methods research.
5. Data visualization within NVivo.
6. Case Study: Thematic analysis of interview data for market research.

Module 6: Data Visualization Techniques

1. Creating visualizations with Python's Matplotlib and Seaborn.
2. Advanced data visualization using R's ggplot2.
3. Customizing graphs for impactful presentations.
4. Creating dashboards with Python’s Dash and R’s Shiny.
5. Integrating NVivo visualizations with quantitative data.
6. Case Study: Visualizing survey data for public policy insights.

Module 7: Machine Learning Basics with Python

1. Introduction to machine learning algorithms.
2. Supervised learning: Linear and Logistic Regression.
3. Unsupervised learning: Clustering and Principal Component Analysis (PCA).
4. Model evaluation techniques: Cross-validation and ROC curves.
5. Using Python’s Scikit-learn for machine learning tasks.
6. Case Study: Predictive analytics in financial markets.

Module 8: Advanced Data Visualization with Python and R

1. Creating interactive visualizations with Plotly in Python.
2. Developing advanced visualizations in R.
3. Data storytelling techniques for effective communication.
4. Visualization best practices for business intelligence.
5. Integrating qualitative and quantitative visualizations.
6. Case Study: Dashboard creation for business performance monitoring.

Module 9: Sentiment and Text Analysis

1. Introduction to text analysis and sentiment analysis in Python.
2. Text mining and content analysis using R.
3. NVivo for qualitative content analysis.
4. Creating word clouds and frequency analysis in NVivo.
5. Sentiment analysis for social media data.
6. Case Study: Social media sentiment analysis for brand monitoring.

Module 10: Advanced Statistical Modeling in R

1. Linear and Non-linear regression models.
2. Survival analysis and time-to-event data.
3. Multivariate analysis techniques.
4. Random Forest and decision trees in R.
5. Practical implementation of advanced models.
6. Case Study: Predictive modeling for healthcare outcomes.

Module 11: Big Data Analysis Using Python

1. Introduction to Big Data concepts and tools.
2. Analyzing large datasets using Python's Dask and PySpark.
3. Handling structured and unstructured data.
4. Data analysis using Jupyter Notebooks.
5. Case study on handling big data challenges.
6. Case Study: Big data analysis for e-commerce insights.

Module 12: Data Reporting and Communication

1. Creating reports and presentations from data analysis.
2. Effective communication of data insights to stakeholders.
3. Tools for automated reporting using Python.
4. Best practices for data storytelling and visualization.
5. Integrating qualitative data findings with quantitative insights.
6. Case Study: Data-driven report for policy decision-making.

Module 13: Capstone Project and Case Study Analysis

1. Defining project scope and objectives.
2. Data collection, analysis, and visualization.
3. Developing a comprehensive data report.
4. Presentation of findings to a simulated client or stakeholder.
5. Feedback and iteration on project results.
6. Case Study: Complete data science project for business strategy.

General Information

1. Customized Training: All our courses can be tailored to meet the specific needs of participants.
2. Language Proficiency: Participants should have a good command of the English language.
3. Comprehensive Learning: Our training includes well-structured presentations, practical exercises, web-based tutorials, and collaborative group work. Our facilitators are seasoned experts with over a decade of experience.
4. Certification: Upon successful completion of training, participants will receive a certificate from Foscore Development Center (FDC-K).
5. Training Locations: Training sessions are conducted at Foscore Development Center (FDC-K) centers. We also offer options for in-house and online training, customized to the client's schedule.
6. Flexible Duration: Course durations are adaptable, and content can be adjusted to fit the required number of days.
7. Onsite Training Inclusions: The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a Certificate of Successful Completion. Participants are responsible for their travel expenses, airport transfers, visa applications, dinners, health/accident insurance, and personal expenses.
8. Additional Services: Accommodation, pickup services, freight booking, and visa processing arrangements are available upon request at discounted rates.
9. Equipment: Tablets and laptops can be provided to participants at an additional cost.
10. Post-Training Support: We offer one year of free consultation and coaching after the course.
11. Group Discounts: Register as a group of more than two and enjoy a discount ranging from 10% to 50%.
12. Payment Terms: Payment should be made before the commencement of the training or as mutually agreed upon, to the Foscore Development Center account. This ensures better preparation for your training.
13. Contact Us: For any inquiries, please reach out to us at training@fdc-k.org or call us at +254712260031.
14. Website: Visit our website at www.fdc-k.org for more information.