Data Scientist and Machine Learning Engineer Career Path

Introduction to AI & ML (5 Hours):
• Overview of AI and its various types
• Ethical considerations in AI
• Introduction to machine learning paradigms (supervised, unsupervised, reinforcement learning)
• Brief introduction to generative AI

Data Science Workflow (5 Hours):
• Data acquisition and cleaning
• Preprocessing and feature engineering
• Model selection, training, evaluation, and deployment
• Introduction to Agile development principles

Python Fundamentals for Data Science (5 Hours):
• Basic Python syntax and programming concepts
• Data structures (lists, tuples, dictionaries)
• Control flow and functions
• Object-oriented programming (OOP) basics

Descriptive & Inferential Statistics (5 Hours):
• Measures of central tendency and variability (mean, median, mode, variance, standard deviation)
• Overview of probability distributions
• Hypothesis testing and confidence intervals

Linear Algebra Fundamentals (5 Hours):
• Introduction to vectors and matrices
• Matrix operations and transformations
• Eigenvalues and eigenvectors with applications in machine learning

Data Wrangling with Pandas (5 Hours):
• Techniques for data cleaning, transformation, and manipulation
• Handling missing data and outliers
• Data merging, concatenation, and reshaping

Data Visualization with Matplotlib & Seaborn (5 Hours):
• Creating a variety of plots (bar, line, scatter, histogram)
• Using visualization to conduct exploratory data analysis and communicate insights

Relational Databases & SQL Fundamentals (5 Hours):
• Introduction to relational database concepts
• Basic SQL commands (SELECT, FROM, WHERE, JOINs, aggregate functions)

Advanced SQL & Database Design (5 Hours):
• Advanced querying techniques (subqueries, common table expressions, window functions)
• Fundamentals of database design and normalization
• Indexing for query optimization

Regression & Classification Algorithms (10 Hours):
• Linear regression and logistic regression techniques
• Decision trees and random forests
• Model evaluation metrics (confusion matrix, precision, recall, F1-score, AUC)
• Introduction to hyperparameter tuning

Ensemble Methods (5 Hours):
• Overview of bagging and boosting techniques

Clustering Techniques (5 Hours):
• K-means clustering fundamentals
• Overview of hierarchical clustering

Dimensionality Reduction (5 Hours):
• Principal Component Analysis (PCA) for reducing data complexity

Neural Networks Fundamentals (5 Hours):
• Basic principles of neural networks
• Understanding perceptrons, activation functions, and backpropagation

Deep Learning Frameworks (5 Hours):
• Hands-on introduction to TensorFlow/Keras or PyTorch
• Practical examples and basic usage of deep learning libraries

NLP Fundamentals (5 Hours):
• Text preprocessing and tokenization
• Techniques for stemming and word embeddings
• Basic text classification methods

Generative AI Introduction (5 Hours):
• Overview of generative models such as LLMs and GANs
• Introduction to prompt engineering and its applications

Model Deployment (5 Hours):
• Overview of deployment strategies for machine learning models
• Introduction to deploying models on cloud platforms and via APIs

MLOps Fundamentals (5 Hours):
• Principles of MLOps and best practices for model lifecycle management
• Basics of version control and continuous integration for models