Data Science & Advanced Analytics Roadmap

The journey of transforming raw data into insights and business value.

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Phase Key Area Technologies & Concepts Objective
1. Math & Programming Fundamentals Math, Statistics & Programming
  • Programming: Python (Pandas, NumPy, Matplotlib)
  • Databases: SQL
  • Mathematics: Linear Algebra, Calculus, Probability & Statistics
 Build a solid foundation in programming and the mathematical concepts essential for data science.
2. Data Collection & Processing Data Engineering & Preprocessing
  • Collection: Web Scraping (BeautifulSoup), APIs
  • Processing: ETL Pipelines, Data Cleaning, Handling Missing Values
  • Feature Engineering
 Master the skills to collect, clean, and transform raw data into a format ready for analysis.
3. Analysis & Visualization Exploratory Data Analysis (EDA) & Storytelling
  • Visualization: Seaborn, Plotly
  • BI Tools: Tableau, Power BI
  • Concepts: Statistical Analysis, Dimensionality Reduction (PCA)
 Discover patterns, relationships, and anomalies in data, and communicate findings effectively.
4. Machine Learning Predictive Modeling
  • Library: Scikit-learn
  • Supervised Learning: Regression, Classification
  • Unsupervised Learning: Clustering
  • Model Evaluation, Cross-validation
 Build and implement machine learning models to predict outcomes and classify data.
5. Deep Learning & Big Data Neural Networks & Distributed Systems
  • Frameworks: TensorFlow, PyTorch, Keras
  • Architectures: CNNs, RNNs, Transformers
  • Big Data: Spark, Hadoop, NoSQL Databases
 Process massive datasets and build complex models for image recognition, NLP.
6. Deployment & AI Ethics MLOps & Social Impact
  • MLOps: Docker, Kubernetes, CI/CD, A/B Testing
  • Cloud: AWS, Google Cloud Platform, Azure
  • Ethics: Explainable AI (XAI), Bias and Fairness
 Deploy models into production environments and consider the ethical and social impacts of AI applications.

Core Mindset

1. Data-Driven Thinking

Every decision and hypothesis must be validated and backed by data. Don't rely solely on intuition.

2. Curiosity & Skepticism

Always ask "why" behind the numbers and don't easily accept surface-level results. Dig deeper to find the truth.

3. Communication & Storytelling

The ability to turn complex analyses into understandable stories that influence business decisions.

4. Continuous Learning

This field is constantly evolving. You must always update your knowledge of new tools, algorithms, and methods to stay relevant.