📊 Data Scientist Roadmap
Master data skills from foundations to advanced ML & business impact
📌 Phase 1: Programming & Foundations
🐍 Python/R Basics
Learn syntax, data types, loops, and functions essential for data manipulation.
🧮 Math & Stats
Master linear algebra, probability, and statistics for data science foundations.
📁 Data Structures
Understand arrays, lists, dictionaries, and dataframes for effective data handling.
📌 Phase 2: Data Wrangling & Analysis
🧹 Data Cleaning
Handle missing values, outliers, and formatting issues in real-world datasets.
📊 Exploratory Data Analysis (EDA)
Use visualizations and descriptive stats to explore patterns and trends.
📑 Pandas & NumPy
Use powerful libraries for data manipulation, aggregation, and transformation.
📌 Phase 3: Machine Learning
🧠 Supervised Learning
Implement classification and regression models using scikit-learn.
📉 Model Evaluation
Use metrics like accuracy, precision, recall, and AUC to assess models.
📈 Unsupervised Learning
Explore clustering and dimensionality reduction techniques like K-Means and PCA.
📌 Phase 4: Advanced Topics
🤖 Deep Learning
Build neural networks with TensorFlow or PyTorch for advanced AI tasks.
📦 MLOps & Deployment
Deploy models using Flask, FastAPI, or cloud services (AWS/GCP).
🧠 NLP & Time Series
Handle sequential data using techniques like LSTMs and transformers.
📌 Phase 5: Business & Domain Knowledge
💼 Data Storytelling
Communicate insights effectively using dashboards and narratives.
📈 Business Metrics
Understand KPIs, ROI, and domain-specific metrics for analytics.
📊 BI Tools
Use Power BI, Tableau, or Looker to visualize and present data.