Programming:
Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn), R
Databases & Querying:
SQL, NoSQL (MongoDB)
Machine Learning:
Regression, Classification, Clustering, Ensemble Methods, Feature Engineering, Model Evaluation
Deep Learning:
TensorFlow, Keras, PyTorch (for trainers in advanced AI topics)
Data Visualization & BI Tools:
Power BI, Tableau, Excel (advanced functions, dashboards)
Big Data Tools (optional for advanced training):
Spark, Hadoop
Cloud Platforms:
AWS, Azure, GCP (basic ML and data handling services)
MLOps (optional advanced):
Git/GitHub, Docker, MLflow

2. Statistics & Mathematics

Descriptive & Inferential Statistics
Probability & Distributions
Hypothesis Testing & A/B Testing
Linear Algebra & Calculus (for ML/DL concepts)
Time Series Analysis

3. Teaching & Training Skills

Curriculum Design & Content Development
Creating structured learning paths, modules, and hands-on labs
Instructional Delivery
– Classroom, virtual, and corporate training methods
Simplifying Complex Concepts
– Explaining technical topics in beginner-friendly ways
Assessment & Feedback
– Designing quizzes, assignments, projects, and providing constructive feedback
Men

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