Quantitative Data Management, Analysis, & Visualization With Python Course

Course Overview

Join our comprehensive course designed to transform your data analysis skills using Python. Whether you're a beginner with some programming background or a seasoned developer transitioning into data science, this course on Quantitative Data Management, Analysis, and Visualization with Python will equip you with the necessary skills to handle large datasets, perform robust analyses, and derive meaningful insights. With hands-on exercises and practical examples, you'll learn to leverage Python's powerful libraries and tools for effective data management, analysis, and visualization.

Course Objectives 

By the end of this course, participants will be able to:

• Understand Research Design: Grasp the fundamentals of research design and its applications.
• Utilize Python for Data Science: Employ Python and its libraries for data science and machine learning tasks.
• Implement Big Data Techniques: Use Spark for analyzing big data efficiently.
• Apply Machine Learning Algorithms: Execute complex algorithms like K-Means Clustering, Logistic and Linear Regression, Random Forests, and Decision Trees.
• Harness Python Libraries: Use NumPy, Pandas, Matplotlib, and Seaborn for data manipulation and visualization.
• Perform Natural Language Processing: Develop applications such as spam filters with NLP techniques.
• Develop Neural Networks and SVMs: Implement advanced machine learning models.
• Write Comprehensive Reports: Craft detailed research reports for data dissemination and decision-making.

Who Should Attend? 

Ideal for participants with basic knowledge of statistics, this course is perfect for professionals from fields such as agriculture, economics, food security, nutrition, education, medical or public health, and more, who wish to deepen their understanding of statistical modeling using Python.

Course Content 

Module 1: Basic Statistical Terms and Concepts

• Introduction to statistical concepts and methods
• Descriptive and inferential statistics

Module 2: Research Design

• Role and purpose of research design
• Types and selection of research designs

Module 3: Survey Planning and Execution

• Survey types, design, sampling, and implementation
• Post-survey activities

Module 4: Introduction to Python

• Setup, installation, and overview of Python IDEs
• Introduction to Jupyter Notebook

Module 5: Learning NumPy

• Creating and manipulating arrays
• Array operations and functions

Module 6: Introduction to Pandas

• DataFrames, data selection, alignment, and handling missing data

Module 7: Working with Data

• Reading, merging, reshaping, and handling data files

Module 8: Big Data and Spark with Python

• Introduction to Spark, PySpark, and RDD transformations

Module 9: Data Visualization

• Creating visualizations with Seaborn and Matplotlib

Module 10: Data Analysis

• Machine learning models and statistical distributions

Module 11: Report Writing and Data Dissemination

• Writing reports and developing communication strategies