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?

Why Learn Data Science?

Estimated time commitment for the roadmap.

Phases of the Data Science Roadmap

Phase 1: Fundamentals of Data Science

Introduction to Data Science

Understanding the Data Science Process

Applications of Data Science

Phase 2: Programming Foundations

Learning Python or R for Data Science

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

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

Phase 3: Mathematics and Statistics

Linear Algebra (Matrices, Vectors)

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

Hypothesis Testing and Confidence Intervals

Phase 4: Data Collection and Cleaning

Understanding Data Types and Formats

Handling Missing and Erroneous Data

Data Scraping and APIs for Data Collection

Exploratory Data Analysis (EDA)

Phase 5: Data Visualization

Creating Visualizations with Matplotlib and Seaborn

Dashboards Using Plotly or Tableau

Communicating Insights Through Visuals

Phase 6: Database Management

Introduction to Databases (SQL and NoSQL)

Writing SQL Queries for Data Analysis

Integrating Databases with Python

Phase 7: Introduction to Machine Learning

Understanding Machine Learning Basics (Supervised vs. Unsupervised Learning)

Building Your First ML Model (Linear Regression)

Key Libraries: Scikit-learn, TensorFlow, PyTorch

Phase 8: Supervised Learning Techniques

Regression (Linear, Logistic)

Classification Algorithms (SVM, Decision Trees, Random Forest)

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

Phase 9: Unsupervised Learning Techniques

Clustering (K-Means, DBSCAN)

Dimensionality Reduction (PCA, t-SNE)

Anomaly Detection

Phase 10: Deep Learning and Neural Networks

Basics of Neural Networks (Perceptron, Activation Functions)

Building Deep Learning Models with TensorFlow or PyTorch

Convolutional Neural Networks (CNN)

Recurrent Neural Networks (RNN) and LSTMs

Phase 11: Natural Language Processing (NLP)

Text Preprocessing (Tokenization, Lemmatization)

Sentiment Analysis and Text Classification

Working with NLP Libraries (NLTK, spaCy)

Phase 12: Big Data and Cloud Computing

Understanding Big Data Concepts

Tools for Big Data (Hadoop, Spark)

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

Phase 13: Data Science Tools and Technologies

Jupyter Notebooks for Data Science Projects

Using Git for Version Control

Automating Tasks with Python Scripts

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