Introduction:

Welcome to the comprehensive Data Science course that combines the power of Python, R, and NVivo! In this course, we will take you on an exciting journey through the world of data analysis and qualitative research, equipping you with essential skills to handle both quantitative and qualitative data effectively.

Data science has emerged as a critical discipline in various industries, driving decision-making processes and uncovering valuable insights from complex datasets. Python and R are two of the most popular programming languages used for data analysis, each offering unique strengths in handling data, performing statistical analysis, and implementing machine learning algorithms.

In addition to data science, qualitative research is a fundamental approach to understanding human experiences, opinions, and behavior. NVivo, a powerful qualitative data analysis software, complements the data science toolkit by enabling in-depth exploration and interpretation of qualitative data.

Throughout this course, we will delve into the core concepts of Python and R programming, data manipulation, exploratory data analysis, statistical analysis, and predictive modeling. We will demonstrate how to perform text analysis, sentiment analysis, and natural language processing (NLP) using Python, R, and NVivo. Moreover, we will explore advanced data visualization techniques to present your findings effectively.

As we progress, we will delve into the integration of Python, R, and NVivo, enabling you to work seamlessly with both quantitative and qualitative data for mixed-methods research analysis. You will learn how to combine the strengths of these tools to extract deeper insights and draw data-driven conclusions.

Furthermore, this course emphasizes ethical considerations in data science and qualitative research, emphasizing data privacy, confidentiality, and responsible data usage. We will discuss project management best practices to ensure you can execute data science projects efficiently and effectively.

Whether you are a data analyst, researcher, student, or decision-maker, this course is designed to provide you with a diverse skill set to make informed decisions, gain a competitive edge in your career, and contribute to your organization's success.

Get ready to embark on a transformative learning experience that empowers you to harness the power of Python, R, and NVivo to extract valuable knowledge from data and embark on exciting research journeys. Let's dive in!

Duration:

The course duration is designed to cover the essential concepts of data science using Python and R and qualitative data analysis with NVivo. It takes 10 days to ensure participants have enough time for hands-on practice and a thorough understanding of the topics covered.

Course Objective:

 The course aims to provide participants with the following skills and knowledge:

1. Proficiency in Python and R programming for data manipulation, analysis, and visualization.
2. Mastery of data science techniques, including data cleaning, exploration, and statistical analysis in both Python and R.
3. Ability to perform sentiment analysis, text mining, and natural language processing (NLP) using Python and R.
4. Competence in qualitative data analysis using NVivo, including coding, categorization, and data visualization.
5. Understanding of integrating Python, R, and NVivo to analyze mixed-methods research data.
6. Application of data science and NVivo to solve real-world problems and make data-driven decisions.

Organization Benefit:

• Empower participants with a wide range of data analysis and qualitative research skills to enhance the organization's data capabilities.
• Improve data-driven decision-making processes within the organization through advanced analytics.
• Enable efficient and accurate analysis of both quantitative and qualitative data, leading to better insights and strategic planning.
• Foster innovation and creativity in problem-solving using data-driven approaches.

Target Participants:

• Data analysts and researchers seeking to expand their skills in data science and qualitative analysis using NVivo.
• Professionals involved in mixed-methods research projects and data integration.
• Business analysts and decision-makers interested in using data to inform organizational strategies.
• Students and academics aiming to enhance their data analysis capabilities for research projects.
• Anyone interested in exploring the integration of Python, R, and NVivo for comprehensive data analysis.

Course Outline:

Module 1: Introduction to Data Science and Qualitative Research

• Overview of data science and its applications in Python, R, and NVivo
• Understanding qualitative research and its significance in data analysis

Module 2: Introduction to Python Programming

• Basics of Python programming language
• Data types, variables, and control structures in Python

Module 3: Introduction to R Programming

• Basics of R programming language
• Data types, variables, and control structures in R

Module 4: Data Manipulation with Python

• Data cleaning and preprocessing techniques in Python
• Handling missing data and outliers in Python

Module 5: Data Manipulation with R

• Data cleaning and preprocessing techniques in R
• Handling missing data and outliers in R

Module 6: Exploratory Data Analysis (EDA) with Python

• Data visualization and graphical representation using Python libraries
• Statistical summaries and insights from EDA in Python

Module 7: Exploratory Data Analysis (EDA) with R

• Data visualization and graphical representation using R libraries
• Statistical summaries and insights from EDA in R

Module 8: Statistical Analysis with Python

• Understanding statistical measures and hypothesis testing in Python
• Performing statistical tests on datasets using Python libraries

Module 9: Statistical Analysis with R

• Understanding statistical measures and hypothesis testing in R
• Performing statistical tests on datasets using R libraries

Module 10: Introduction to NVivo Interface

• Familiarizing with the NVivo software environment
• Importing and managing qualitative data in NVivo

Module 11: Qualitative Data Coding in NVivo

• Coding qualitative data for analysis in NVivo
• Applying different coding techniques in NVivo

Module 12: Text Analysis with Python

• Text mining and natural language processing (NLP) in Python
• Sentiment analysis and topic modeling using Python libraries

Module 13: Text Analysis with R

• Text mining and natural language processing (NLP) in R
• Sentiment analysis and topic modeling using R libraries

Module 14: Integrating Python, R, and NVivo

• Importing and exporting data between Python, R, and NVivo
• Leveraging Python and R libraries for advanced analysis in NVivo

Module 15: Thematic Analysis in NVivo

• Conducting thematic analysis on qualitative data in NVivo
• Identifying patterns and themes in NVivo

Module 16: Advanced Data Visualization with Python

• Creating interactive and advanced data visualizations in Python
• Presenting data insights effectively

Module 17: Advanced Data Visualization with R

• Creating interactive and advanced data visualizations in R
• Presenting data insights effectively

Module 18: Mixed-Methods Research Analysis

• Integrating quantitative and qualitative data for analysis
• Methods for conducting mixed-methods research using Python, R, and NVivo

Module 19: Predictive Analytics with Python

• Introduction to predictive modeling and machine learning in Python
• Implementing predictive analytics using Python libraries

Module 20: Predictive Analytics with R

• Introduction to predictive modeling and machine learning in R
• Implementing predictive analytics using R libraries

Module 21: Data Interpretation and Visualization in NVivo

• Visualizing qualitative data in NVivo for presentation
• Drawing conclusions from qualitative analysis in NVivo

Module 22: Big Data Analytics with Python and R

• Handling and analyzing large datasets with Python and R
• Distributed computing and parallel processing techniques

Module 23: Time-Series Analysis with Python

• Analyzing time-series data using Python libraries
• Time-series forecasting and applications

Module 24: Time-Series Analysis with R

• Analyzing time-series data using R libraries
• Time-series forecasting and applications

Module 25: Data Ethics and Privacy

• Ethical considerations in data science and qualitative research
• Ensuring data privacy and confidentiality

Module 26: Data Science Project Management

• Best practices for managing data science projects
• Developing effective data analysis workflows

Module 27: Deep Learning and Neural Networks

• Introduction to deep learning and neural networks
• Implementing deep learning models in Python and R

Module 28: Machine Learning Interpretability

• Techniques for interpreting machine learning models
• Understanding model decisions and insights

Module 29: AI in Qualitative Research

• Exploring AI applications in qualitative research
• AI-assisted coding and analysis with NVivo

Module 30: Course Conclusion and Future Applications

• Recap of key concepts and techniques covered in the course
• Exploring future applications of data science and qualitative analysis in various fields

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