Data Scientist Learning Path
Your comprehensive resource for building expertise in data science.
01
Foundations of Data Science and Machine Learning
Section 1.1: What is Data Science? Overview and Roles
Section 1.2: Introduction to Machine Learning
Section 1.3: Applications of ML
- Video: Applications of ML (5min)
Section 1.4: Overview of the ML Workflow
Section 1.5: Ethics in AI
- Video: Ethics in AI (5min)
02
Python for Data Science
Section 2.1: Python Basics
- Video: Python Basics (10min)
Section 2.2: Data Manipulation with Pandas and NumPy
- Video: Data Manipulation with Pandas and NumPy (Short videos about 5-10min)
Section 2.3: Data Visualization with Matplotlib, Seaborn, and Plotly
Section 2.4: Working with Jupyter Notebook and Google Colab
Section 2.5: Overview of Python
03
Data Wrangling and Preparation
Section 3.1: Handling Missing Data and Outliers
Section 3.2: Feature Engineering
- Video: Feature Engineering (29min)
Section 3.3: Exploratory Data Analysis (EDA) Techniques
Section 3.4: Automating Data Pipelines with Python
04
Machine Learning Essentials
Section 4.1: Linear Regression and Logistic Regression
Section 4.2: Decision Trees, Random Forests
- Video: Decision Trees (10min)
- Video: Random Forests (8min)
- Course: Tree-Based Methods (Ch.8) (1 hour)
Section 4.3: Support Vector Machines (SVM)
Section 4.4: K-Nearest Neighbors (KNN)
Section 4.5: Clustering and Dimensionality Reduction
- Video: k-Means Clustering (8min)
- Video: PCA (6min)
- Course: Regularization (Ch.6) (1 hour)
Section 4.6: Model Evaluation
Supplemental: Machine Learning Specialization by Andrew Ng
05
Advanced Machine Learning
Section 5.1: Hyperparameter Tuning
Section 5.2: Ensemble Learning
Section 5.3: Time Series Analysis
Section 5.4: Advanced Clustering Techniques
Section 5.5: Feature Selection and Engineering Techniques
06
Mathematics for Machine Learning
Section 6.1: Linear Algebra
Section 6.2: Calculus for Optimization
Section 6.3: Probability and Statistics
Section 6.4: Optimization Techniques
Supplemental: Mathematics for Machine Learning
07
Deep Learning and Neural Networks
Section 7.1: Basics of Neural Networks
Section 7.2: Feedforward Neural Networks (FNNs) with TensorFlow/Keras
Section 7.3: Convolutional Neural Networks (CNNs) for Image Processing
Section 7.4: Recurrent Neural Networks (RNNs) and LSTMs for Sequence Data
Section 7.5: Transformers and Attention Mechanisms (BERT, GPT Models)
Section 7.6: Transfer Learning (Pretrained Models: ResNet, VGG, etc.)
Section 7.7: Deep Learning
- Course: Deep Learning (Ch.10) (1 hour)
08
Natural Language Processing (NLP)
Section 8.1: Text Preprocessing
Section 8.2: Vectorization Techniques
Section 8.3: Building NLP Models
- Video: Building NLP Models (Sentiment Analysis, Text Classification) (More videos for 5mins)
Section 8.4: Sequence Models and Transformers
09
Model Deployment and MLOps
Section 9.1: Building APIs with Flask and FastAPI
Section 9.2: Model Deployment on Cloud Platforms
Section 9.3: MLOps Best Practices
Section 9.4: Managing Data and Model Drift
10
Big Data and AI Strategy
Section 10.1: Big Data Technologies
Section 10.2: Data Strategy
Section 10.3: AI in Business
Section 10.4: Data Security and Privacy
11
Capstone Project
Step 1: Define a Business Problem and Select a Dataset
Define a business problem and select a relevant dataset for analysis.
Step 2: Perform EDA and Preprocessing
Conduct exploratory data analysis and preprocess the data for modeling.
Step 3: Build and Evaluate Machine Learning and Deep Learning Models
Build and evaluate machine learning and deep learning models.
Step 4: Deploy the Model and Present Findings with a Dashboard
Deploy the model and present findings using a dashboard.