Your Ultimate Data Science Roadmap

Master data analysis, visualization, machine learning, and AI with this step-by-step guide.

Basic overview

What is Data Science?

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Why Learn Data Science?

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Estimated time commitment for the roadmap.

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Phases of the Data Science Roadmap

Phase 1: Fundamentals of Data Science

Introduction to Data Science

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Understanding the Data Science Process

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Applications of Data Science

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Phase 2: Programming Foundations

Learning Python or R for Data Science

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Basics of Python (Variables, Loops, Functions, Libraries)

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Key Libraries for Data Science: NumPy, Pandas, Matplotlib, Seaborn

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Phase 3: Mathematics and Statistics

Linear Algebra (Matrices, Vectors)

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Probability and Statistics (Mean, Median, Mode, Variance, Distribution)

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Hypothesis Testing and Confidence Intervals

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Phase 4: Data Collection and Cleaning

Understanding Data Types and Formats

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Handling Missing and Erroneous Data

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Data Scraping and APIs for Data Collection

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Exploratory Data Analysis (EDA)

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Phase 5: Data Visualization

Creating Visualizations with Matplotlib and Seaborn

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Dashboards Using Plotly or Tableau

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Communicating Insights Through Visuals

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Phase 6: Database Management

Introduction to Databases (SQL and NoSQL)

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Writing SQL Queries for Data Analysis

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Integrating Databases with Python

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Phase 7: Introduction to Machine Learning

Understanding Machine Learning Basics (Supervised vs. Unsupervised Learning)

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Building Your First ML Model (Linear Regression)

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Key Libraries: Scikit-learn, TensorFlow, PyTorch

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Phase 8: Supervised Learning Techniques

Regression (Linear, Logistic)

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Classification Algorithms (SVM, Decision Trees, Random Forest)

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Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)

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Phase 9: Unsupervised Learning Techniques

Clustering (K-Means, DBSCAN)

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Dimensionality Reduction (PCA, t-SNE)

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Anomaly Detection

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Phase 10: Deep Learning and Neural Networks

Basics of Neural Networks (Perceptron, Activation Functions)

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Building Deep Learning Models with TensorFlow or PyTorch

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Convolutional Neural Networks (CNN)

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Recurrent Neural Networks (RNN) and LSTMs

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Phase 11: Natural Language Processing (NLP)

Text Preprocessing (Tokenization, Lemmatization)

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Sentiment Analysis and Text Classification

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Working with NLP Libraries (NLTK, spaCy)

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Phase 12: Big Data and Cloud Computing

Understanding Big Data Concepts

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Tools for Big Data (Hadoop, Spark)

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Cloud Platforms for Data Science (AWS, GCP, Azure)

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Phase 13: Data Science Tools and Technologies

Jupyter Notebooks for Data Science Projects

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Using Git for Version Control

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Automating Tasks with Python Scripts

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