2024 iPlan

 Current Plan Overview

1. DataCamp Tracks:
• Data Analyst in Python (100% complete)
• Associate Data Engineer in SQL (65% complete)
• Associate Data Scientist in Python (54% complete)
• Deep Learning Skill Track (100% complete)
• Developing Large Language Models in Python (100% Complete)
• Associate AI Engineer for Data Scientists (43 % Complete)
• Associate AI Engineer for Developers (13% Complete)
• Data Engineer in Python (to follow)
• Professional Data Engineer in Python (to follow)
• Machine Learning Scientist (to follow*)
  
  
2. Complementary Studies:
• Computer Systems: A Programmer's Perspective
• The C Programming Language
• Structure and Interpretation of Computer Programs
• Designing Data-Intensive Applications
• Computer Architecture: A Quantitative Approach
• Programming Massively Parallel Processors
• Active Inference by Karl Friston
3. NVIDIA Deep Learning Course (to complement the DataCamp Deep Learning track)
4. Cisco Skills for All Courses 
5. CFA Charter after completing the above

Suggested Improvements

1. Prioritise Deep Learning and Data Science for AI-Driven Growth:

• Why: AI and deep learning are rapidly transforming industries, and given Namibia’s emerging sectors, investing time in deep learning and AI will be critical. The skills learned will tie directly into your long-term goals in data science, tech, and finance.
• Adjustment: Move the Deep Learning Skill Track and NVIDIA Deep Learning Course higher on the list and focus on them after finishing your DataCamp certifications.

2. Integrate Real-World Data Projects:

• Why: Given the theoretical nature of your current studies, hands-on experience will help you solidify the skills and stand out in competitive job markets. You can do this by applying what you learn in real-world contexts (e.g., Namibia’s oil sector or sustainability efforts).
• Adjustment: Start working on a capstone project that applies the skills you're developing in Python, SQL, and AI to a specific industry problem in Namibia (e.g., data analysis of consumer behavior, energy usage forecasting, or green energy projects).

3. Leverage New Tools for Financial Modeling:

• Why: Given your plans to eventually pursue the CFA, you should start integrating financial modeling tools now. Python libraries like Pandas, NumPy, and QuantLib will be invaluable.
• Adjustment: Add a Financial Modeling in Python track or project alongside your existing technical studies. This could include building basic portfolio models, performing risk analysis, or working on derivatives modeling with Python.

4. Focus on Systems Programming and Computer Architecture After Core Data Skills:

• Why: While systems programming and architecture are valuable, they are secondary to the data science and AI focus in the current global landscape. After you’ve built a strong foundation in data skills and deep learning, you can then dive into more niche areas like systems architecture.
• Adjustment: Shift Computer Systems: A Programmer’s Perspective and The C Programming Language to after the completion of your core Data Science and AI work. These should come into play when you're more focused on specialized technical roles.

5. Set Milestones and Time Allocation:

• Why: A clear breakdown of what to focus on when will ensure you don't get overwhelmed. It’s critical to maintain balance, especially when studying multiple fields like data engineering, AI, and finance.
• Adjustment: Break the plan down into monthly goals to keep your momentum going. Focus on completing certifications and working on hands-on projects before moving on to additional readings and CFA prep.

6. Apply Cisco Skills for All to Enhance Your Network Security Knowledge:

• Why: As part of working in AI, data science, or finance, understanding cybersecurity and network architecture is valuable, especially if you’re interested in building secure, scalable systems.
• Adjustment: Incorporate relevant Cisco Skills for All courses into your studies, perhaps focusing on network architecture or security, depending on your interest in protecting data systems or handling infrastructure projects.