Certified Data Scientist Program Overview

The Certified Data Scientist Program offered by AFRICDSA and accredited by International Association for Business Analytics Certification(IABAC) is designed to equip learners with in-demand data science skills, making them job-ready for a career in data science. The course spans 5 months (full-time) or 9 months (part-time) and offers both live online and physical training.

Course Overview:

1. Data Science Foundation

• Introduction to data science, its evolution, and terminologies.
• Differences between data science, business analytics, and big data.
• Understanding analytics types (descriptive, predictive, prescriptive).
• Industry roles, project workflows, and real-world applications.

2. Python Essentials for Data Science

• Installation and setup of Python environments like Anaconda and Jupyter.
• Python basics, data structures, control statements, and functions.

3. Statistics for Data Science

• Descriptive and inferential statistics, hypothesis testing, regression, and correlation.

4. Data Preparation with NumPy & Pandas

• Data manipulation using NumPy arrays and Pandas for indexing, merging, and cleaning.

5. Visualization with Python

• Data visualization techniques, plotting categorical and time-series data.

6. Machine Learning Associate

• Introduction to machine learning algorithms, regression, clustering, and supervised/unsupervised learning.

7. Advanced Machine Learning

• Decision trees, random forests, support vector machines, boosting techniques, and neural networks.

8. SQL for Data Science

• SQL basics, advanced queries, and working with relational databases.

9. Deep Learning – CNN Basics

• Fundamentals of convolutional neural networks (CNNs) and image classification.

10. Tableau & Power BI

• Data visualization using Power BI and Tableau, dashboards, and storytelling with data.

11. Machine Learning Model Deployment

• Deploying machine learning models using Flask APIs.

12. Big Data Essentials

• Introduction to big data, Hadoop, and Spark for machine learning.

13. Data Science Project Execution

• Managing and executing a data science project using the CRISP-DM framework.

Learning Outcomes:

By the end of the program, students will:

• Master Python, SQL, and big data technologies.
• Develop statistical and machine learning models.
• Gain proficiency in data visualization with Power BI and Tableau.
• Execute end-to-end data science projects.
• Deploy machine learning models via APIs.