Data Science
Roadmap for individuals starting from scratch to become proficient Data Scientists, covering foundational knowledge, core skills, machine learning, advanced topics, deployment, ethics, and career development.
Data Science
Roadmap for individuals starting from scratch to become proficient Data Scientists, covering foundational knowledge, core skills, machine learning, advanced topics, deployment, ethics, and career development.
Learning Modules
The Aspiring Data Scientist: From Zero to Insights
This roadmap guides individuals from scratch through the multidisciplinary field of Data Science, covering foundational mathematics, programming, core skills, machine learning, advanced topics, deployment, ethics, and career development.
Phase 1: Foundational Knowledge & Setup
Start by understanding what Data Science is, its real-world applications, the typical lifecycle of a data science project, the mindset required, and build a solid foundation in mathematics, statistics, and programming (Python).
What is Data Science?
Define Data Science as an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Understand its role in decision-making.
Why Data Science Matters & Its Applications
Explore the importance of Data Science across various industries (e.g., healthcare, finance, e-commerce, technology) and its applications (e.g., recommendation systems, fraud detection, medical diagnosis, personalized marketing).
The Data Science Lifecycle (Overview)
Learn about the typical stages in a data science project: business understanding, data acquisition, data preparation (cleaning/preprocessing), exploratory data analysis (EDA), modeling, evaluation, and deployment.
The Data Scientist Mindset & Key Soft Skills
Cultivate the mindset of a data scientist: strong curiosity, analytical and critical thinking, problem-solving abilities, attention to detail, effective communication, and a commitment to ethical practices.
Branch: Mathematics & Statistics Fundamentals
Build a necessary foundation in mathematics and statistics, crucial for understanding data science algorithms and interpreting results.
Linear Algebra Basics
Understand basic concepts of linear algebra: vectors, matrices, dot products, and their relevance in representing data and performing operations in machine learning algorithms.
Calculus Basics (Conceptual)
Grasp fundamental calculus concepts: derivatives (rates of change) and gradients (used in optimization algorithms like gradient descent). Focus on conceptual understanding.
Descriptive Statistics
Learn descriptive statistics: measures of central tendency (mean, median, mode), measures of dispersion (variance, standard deviation, range), and understanding data distributions.
Probability Basics
Understand basic probability concepts: random variables, probability distributions (e.g., Normal, Binomial - conceptual), conditional probability, and Bayes' theorem (introductory).
Inferential Statistics (Hypothesis Testing Intro)
Learn the fundamentals of inferential statistics: hypothesis testing, p-values, confidence intervals, and basic tests like t-tests and chi-squared tests (conceptual overview and when to use).
Branch: Programming Fundamentals (Python Focus)
Develop programming skills, focusing on Python as the primary language for data science due to its extensive libraries and community support.
Python Basics: Syntax, Variables, Data Types
Learn Python syntax, variables, basic data types (integers, floats, strings, booleans), and operators.
Python Control Flow, Loops, and Functions
Understand control flow (if/else statements, conditional expressions) and loops (for, while) for iteration. Learn to define and use functions (parameters, return values, scope).
Python Data Structures (Lists, Dictionaries, etc.)
Master Python's built-in data structures: lists, dictionaries, tuples, and sets, including their common methods and use cases.
Python OOP Basics (Introduction)
Get an introduction to Object-Oriented Programming (OOP) concepts in Python: classes, objects, methods, inheritance (basic understanding).
NumPy for Numerical Computing
Learn NumPy for efficient numerical computations, focusing on creating and manipulating multi-dimensional arrays (ndarrays) and performing vectorized operations.
Pandas for Data Manipulation & Analysis
Master Pandas for data manipulation and analysis: working with DataFrames and Series, data loading (CSV, Excel), indexing, selection, filtering, grouping, merging, and handling missing data.
Matplotlib & Seaborn for Basic Data Visualization
Learn Matplotlib and Seaborn for data visualization: creating basic plots like line charts, bar charts, histograms, scatter plots, and box plots to explore and present data.
Branch: Essential Tools & Environment Setup
Set up your development environment with essential tools for data science work.
Python Environment Setup (Anaconda, Pip, Virtualenv)
Install Python and manage packages/environments using Anaconda (recommended for beginners), pip, and virtual environments (venv or conda environments) to handle project dependencies.
IDEs & Notebooks (Jupyter, VS Code)
Get comfortable with an Integrated Development Environment (IDE) like VS Code (with Python and Jupyter extensions) and interactive computing environments like Jupyter Notebooks or JupyterLab for data analysis and experimentation.
Git & Version Control Basics for Data Science
Learn the basics of Git for version control to track changes in your data science projects, collaborate with others, and manage code repositories (e.g., on GitHub).
Phase 2: Core Data Science Skills
This phase focuses on developing the hands-on skills required to work with data: collecting it, cleaning it, exploring it, and preparing it for modeling.
Branch: Data Collection & Acquisition
Learn various methods for acquiring data needed for analysis and modeling.
Understanding Data Sources
Understand different data sources: structured data from databases (SQL, NoSQL), semi-structured data from APIs (JSON, XML), and unstructured data from files (text, images, web pages).
Web Scraping Basics (Requests, BeautifulSoup)
Learn basic web scraping techniques using Python libraries like Requests (for fetching web pages) and BeautifulSoup (for parsing HTML) to extract data from websites. Understand ethical considerations and terms of service.
SQL Querying for Data Retrieval (Basics)
Learn basic SQL querying (SELECT, FROM, WHERE, JOIN) to retrieve data from relational databases. This is a crucial skill for accessing structured data.
Branch: Data Cleaning & Preprocessing
Raw data is often messy and requires significant cleaning and transformation before it can be used for analysis or modeling.
Handling Missing Values
Learn techniques for identifying and handling missing values in datasets (e.g., imputation methods like mean/median/mode replacement, or deletion strategies).
Data Type Conversion & Formatting
Understand how to convert data between different types (e.g., string to numeric, date formats) and ensure consistent formatting for analysis.
Outlier Detection & Treatment (Basics)
Learn basic methods for detecting outliers (e.g., using z-scores, box plots) and strategies for treating them (e.g., removal, capping, transformation), considering their potential impact.
Data Transformation (Normalization, Standardization)
Understand data transformation techniques such as normalization (scaling data to a range, e.g., 0-1) and standardization (scaling data to have zero mean and unit variance) to prepare data for certain machine learning algorithms.
Branch: Exploratory Data Analysis (EDA) & Visualization
EDA is the process of examining datasets to summarize their main characteristics, often with visual methods, to uncover patterns, spot anomalies, test hypotheses, and check assumptions.
Understanding Data Distributions
Use summary statistics (mean, median, mode, standard deviation, quartiles) and visualizations (histograms, density plots) to understand the distribution of individual variables.
Univariate & Bivariate Analysis
Conduct univariate analysis (exploring single variables) and bivariate analysis (exploring relationships between pairs of variables using scatter plots, correlation matrices, etc.).
Data Visualization Techniques
Master using Matplotlib and Seaborn for creating a variety of visualizations like histograms, scatter plots, box plots, bar charts, heatmaps, and pair plots to explore data effectively.
Identifying Patterns, Anomalies & Generating Insights
Develop the skill of interpreting visualizations and statistical summaries to identify interesting patterns, potential anomalies, correlations, and formulate initial hypotheses or insights from the data.
Introduction to Feature Engineering
Get an introduction to feature engineering, the process of using domain knowledge to create new input features from raw data to improve machine learning model performance.
Creating New Features & Basic Selection Techniques
Learn basic techniques like creating interaction terms, polynomial features, binning continuous variables, and one-hot encoding categorical variables.
Phase 3: Machine Learning Fundamentals
This phase introduces the core concepts of Machine Learning (ML), common types of ML tasks, and the process of building and evaluating models, primarily using the scikit-learn library in Python.
What is Machine Learning? Types of ML
Define Machine Learning and understand its main categories: Supervised Learning (learning from labeled data), Unsupervised Learning (finding patterns in unlabeled data), and Reinforcement Learning (learning through rewards/penalties - conceptual overview).
Branch: Supervised Learning
Focus on algorithms that learn from labeled datasets (input-output pairs).
Regression: Linear & Polynomial Regression (Intro)
Learn about regression tasks (predicting continuous values). Understand Linear Regression (simple and multiple) and an introduction to Polynomial Regression for non-linear relationships. Implement with scikit-learn.
Classification: Logistic Regression & k-NN (Intro)
Learn about classification tasks (predicting discrete categories). Understand Logistic Regression for binary classification and K-Nearest Neighbors (KNN) as a simple instance-based classifier. Implement with scikit-learn.
Decision Trees & Random Forests (Intro)
Understand Decision Trees for both regression and classification, and their interpretability. Introduction to Random Forests as an ensemble of decision trees for improved performance. Implement with scikit-learn.
Support Vector Machines (SVMs - Introduction)
Get an introduction to Support Vector Machines (SVMs) for classification (and regression), understanding the concept of hyperplanes and margins. Conceptual overview of kernels. Implement with scikit-learn.
Regression Model Evaluation Metrics
Learn common metrics for evaluating regression models: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (Coefficient of Determination).
Classification Model Evaluation Metrics
Learn common metrics for evaluating classification models: Accuracy, Precision, Recall, F1-score, Confusion Matrix, and an introduction to ROC curve and AUC.
Branch: Unsupervised Learning
Focus on algorithms that find patterns in unlabeled datasets.
Clustering: K-Means & Hierarchical (Intro)
Learn about clustering tasks (grouping similar data points). Understand K-Means clustering and an introduction to Hierarchical Clustering. Implement with scikit-learn.
Dimensionality Reduction: PCA (Intro)
Understand dimensionality reduction for reducing the number of features while preserving important information. Learn Principal Component Analysis (PCA) as a common technique. Implement with scikit-learn.
Reinforcement Learning (Conceptual Overview)
Get a conceptual overview of Reinforcement Learning (RL): agents learning optimal actions in an environment through trial and error, guided by rewards and penalties. Understand key terms like agent, environment, state, action, reward. (No implementation focus at this stage).
Model Building Process with Scikit-learn
Understand the general workflow of building an ML model using scikit-learn: importing libraries, loading data, splitting data, choosing a model, training the model (`fit`), making predictions (`predict`), and evaluating performance.
Train/Test Split & Cross-Validation
Learn the importance of splitting data into training and testing sets to evaluate model performance on unseen data. Understand k-fold cross-validation as a more robust evaluation technique.
Bias-Variance Tradeoff (Overfitting & Underfitting)
Understand the concepts of bias (underfitting, model too simple) and variance (overfitting, model too complex, doesn't generalize well). Learn about the trade-off between them.
Hyperparameter Tuning (Grid Search, Random Search - Intro)
Learn about hyperparameters (model settings not learned from data) and techniques for tuning them to optimize model performance, such as Grid Search and Randomized Search, using scikit-learn.
Phase 4: Advanced Machine Learning & Specializations
This phase explores more advanced machine learning techniques and introduces specializations like Deep Learning, NLP, Time Series Analysis, and Big Data.
Ensemble Methods (Boosting, Bagging, XGBoost/LightGBM Intro)
Learn about ensemble methods that combine multiple models to improve performance: Bagging (e.g., Random Forests revisited), Boosting (e.g., AdaBoost, Gradient Boosting), and an introduction to powerful libraries like XGBoost and LightGBM.
Branch: Deep Learning
Dive into Deep Learning, a subfield of ML based on artificial neural networks with many layers.
Neural Networks Basics
Understand the basics of artificial neural networks: neurons (nodes), layers (input, hidden, output), weights, biases, activation functions (e.g., ReLU, sigmoid, tanh), and the concept of forward and backward propagation (conceptual).
Deep Learning Frameworks (TensorFlow/Keras, PyTorch - Basics)
Get an introduction to popular deep learning frameworks: TensorFlow (with Keras API) and PyTorch. Learn basic setup, defining simple models, and training them.
Convolutional Neural Networks (CNNs - Introduction)
Learn about Convolutional Neural Networks (CNNs) and their application in image recognition and computer vision. Understand key components like convolutional layers, pooling layers, and fully connected layers (conceptual overview).
Recurrent Neural Networks (RNNs, LSTMs - Introduction)
Learn about Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks for processing sequential data like text or time series. Understand their basic architecture and applications (conceptual overview).
Transfer Learning & Fine-Tuning (Introduction)
Understand the concept of transfer learning: using pre-trained deep learning models (trained on large datasets) and fine-tuning them for specific tasks, which can save significant training time and resources.
Branch: Natural Language Processing (NLP)
Explore Natural Language Processing (NLP), a field of AI focused on enabling computers to understand, interpret, and generate human language.
Text Preprocessing
Learn common text preprocessing techniques: tokenization (splitting text into words/sentences), stop word removal, stemming (reducing words to root form), and lemmatization (reducing words to dictionary form).
Feature Extraction from Text
Understand feature extraction methods for text: Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF). Introduction to word embeddings (dense vector representations) like Word2Vec and GloVe (conceptual).
Sentiment Analysis Basics
Learn the basics of sentiment analysis: classifying text (e.g., reviews, social media posts) as positive, negative, or neutral using NLP techniques and machine learning.
Topic Modeling (LDA - Introduction)
Get an introduction to topic modeling techniques like Latent Dirichlet Allocation (LDA) for discovering hidden thematic structures in large collections of text documents.
Transformers & Hugging Face (Conceptual Overview)
Conceptual overview of advanced NLP models like Transformers (e.g., BERT, GPT). Introduction to using pre-trained models from libraries like Hugging Face Transformers for tasks like text classification, question answering, etc.
Branch: Time Series Analysis & Forecasting
Focus on analyzing and forecasting time-ordered data points.
Introduction to Time Series Data & Components
Understand the characteristics of time series data: trend, seasonality, cyclical patterns, and irregular noise. Learn about stationarity and its importance.
Smoothing Techniques
Learn basic smoothing techniques like Moving Averages and Exponential Smoothing to identify trends and reduce noise in time series data.
ARIMA/SARIMA Models (Introduction)
Get an introduction to classical time series forecasting models like ARIMA (Autoregressive Integrated Moving Average) and SARIMA (Seasonal ARIMA) for modeling and predicting future values.
Facebook Prophet (Introduction)
Explore modern time series forecasting libraries like Facebook Prophet, designed for ease of use and handling common time series features automatically.
Branch: Big Data Technologies (Conceptual Introduction)
Get an introduction to technologies and frameworks for handling and processing datasets that are too large or complex for traditional data processing applications.
What is Big Data? (The Vs & Challenges)
Understand what constitutes 'Big Data' (Volume, Velocity, Variety, Veracity) and the challenges associated with storing, processing, and analyzing it.
Hadoop Ecosystem (HDFS, MapReduce - Conceptual)
Conceptual overview of the Hadoop ecosystem, including HDFS (Hadoop Distributed File System) for distributed storage and MapReduce for parallel processing of large datasets.
Apache Spark Basics (RDDs, DataFrames, PySpark Intro)
Introduction to Apache Spark as a fast and general-purpose cluster computing system. Learn basic concepts like RDDs (Resilient Distributed Datasets), DataFrames, and Spark APIs (e.g., PySpark for Python users).
Cloud Platforms for Big Data (Brief Overview)
Brief overview of how major cloud platforms (AWS, GCP, Azure) offer managed services for big data storage, processing (e.g., EMR, Dataproc, HDInsight), and analytics.
Phase 5: Data Storytelling, Deployment & Ethics
This phase focuses on effectively communicating data-driven insights, deploying models into production (conceptually), understanding ethical implications, and ensuring reproducibility.
Advanced Data Visualization & Storytelling
Master advanced data visualization techniques for creating compelling narratives and dashboards. Introduction to interactive visualization tools like Tableau, Power BI (conceptual overview), or Python libraries like Plotly/Dash.
Communicating Insights to Non-Technical Audiences
Develop skills in presenting complex data science findings and insights clearly and persuasively to non-technical stakeholders, focusing on actionable recommendations and business impact.
Branch: Model Deployment Basics (Conceptual)
Understand the process of making machine learning models available for use in real-world applications.
Saving & Loading Models
Learn how to save trained machine learning models (e.g., using pickle or joblib in Python) and load them for making predictions later.
Building Simple APIs for Models (Intro)
Get an introduction to building simple REST APIs (e.g., using Flask or FastAPI in Python) to expose your ML model's prediction capabilities over the web.
Docker Basics for Model Deployment (Intro)
Understand the basics of Docker for containerizing your ML models and their dependencies, ensuring they run consistently across different environments.
Cloud Deployment Options (AWS, GCP, Azure - Intro)
Brief overview of cloud platforms for deploying ML models (e.g., AWS SageMaker, Google Cloud Vertex AI, Azure Machine Learning) and their managed services.
MLOps Basics (Versioning, Monitoring, Retraining - Conceptual)
Introduction to MLOps (Machine Learning Operations) concepts: model versioning, monitoring model performance in production, and establishing processes for retraining models as needed.
Branch: Ethics & Responsible AI
Understand the critical ethical considerations and responsibilities associated with practicing data science.
Bias, Fairness, Accountability, Transparency (BFAT)
Explore issues of bias in data and algorithms, fairness in model outcomes, accountability for AI decisions, and the importance of transparency in how models work (Explainable AI - XAI).
Data Privacy & Security Considerations
Understand data privacy principles and regulations (e.g., GDPR, CCPA). Learn about data security best practices for protecting sensitive information used in data science projects.
Explainable AI (XAI - Introduction)
Introduction to Explainable AI (XAI) techniques (e.g., SHAP, LIME - conceptual) that help understand why a machine learning model makes certain predictions, increasing transparency and trust.
Reproducibility & Advanced Version Control
Focus on ensuring that data science work is reproducible and well-documented for collaboration and verification.
Experiment Tracking Tools (MLflow, W&B - Introduction)
Learn about tools for tracking machine learning experiments, parameters, metrics, and artifacts, such as MLflow or Weights & Biases (W&B), to improve reproducibility and collaboration.
Data Version Control (DVC - Introduction)
Get an introduction to Data Version Control (DVC) for versioning datasets and machine learning models alongside code, ensuring full reproducibility of pipelines.
Phase 6: Career Development & Continuous Learning
This phase focuses on building a portfolio, navigating the job market, understanding specialization paths, and committing to lifelong learning in the rapidly evolving field of data science.
Building a Data Science Portfolio
Create a portfolio of data science projects (e.g., on GitHub) that showcase your skills in data analysis, visualization, machine learning, and problem-solving. Include diverse projects and clear documentation.
Kaggle Competitions & Open Source Contributions
Participate in Kaggle competitions or other data science challenges to practice your skills on real-world datasets and learn from others. Contribute to open-source data science projects.
Networking & Community Engagement
Build your professional network by engaging with the data science community: follow experts on LinkedIn/Twitter, attend local meetups or online webinars, and participate in relevant forums.
Job Search: Resume, Interviews, Case Studies
Prepare for the job search: craft an effective data science resume and cover letter, practice common interview questions (technical, behavioral, case studies), and learn how to present your portfolio projects.
Exploring Specialization Paths
Understand different specialization paths within data science, such as Machine Learning Engineer, NLP Specialist, Data Analyst, Business Intelligence Developer, Data Engineer, or Research Scientist, and identify areas of interest for future growth.
Staying Updated: Continuous Learning in Data Science
Commit to lifelong learning by staying updated with new tools, techniques, research papers, industry blogs, online courses, and attending conferences to keep your skills sharp and relevant in the fast-evolving field of data science.