igorscarvalho/FGV_Intro_DS

Introduction to Data Science @ FGV

FGV_Intro_DS

Introduction to Data Science @ FGV

Instructor: Renato Rocha Souza

This is the repository of code for the "Introduction to Data Science"

This class is about the Data Science process, in which we seek to gain useful predictions and insights from data. Through real-world examples and code snippets, we introduce methods for:

• data munging, scraping, sampling andcleaning in order to get an informative, manageable data set;
• data storage and management in order to be able to access data (even if big data);
• exploratory data analysis (EDA) to generate hypotheses and intuition about the data;
• prediction based on statistical learning tools;
• communication of results through visualization, stories, and interpretable summaries

Detailed Syllabus:

• Data Science Concepts and Methodologies

  • Data Science, Statistics, AI and Machine Learning ref1, ref2, ref3, ref4
  • Data Science process
    • Business Intelligence
    • CRISP-DM

• Data Formats

• Data Engineering ref1, ref2

  • Data Acquisition
  • Data Preparation
    • Handling Missing Values
    • Changing Datatypes
  • Exploratory Data Analysis ref1, ref2, ref3
  • Data Visualization ref1, ref2

• Feature Engineering

• Feature Selection

  • Numeric Data
  • Discrete/Categorical Data
  • Textual Data ref1, ref2

• Model Selection

• Cross Validation ref1, video1

• Oversampling and Undersampling ref1

• Regularization ref1, ref2

• Bias and Variance

• Overfitting and Underfitting

• Evaluation Metrics ref1, ref2

• Machine Learning Algorithms ref1, ref2, ref3, ref4, ref5

  • Unsupervised

    • Dimensionality reduction
      • PCA
      • SVD
      • t-SNE
    • Clustering ref1, ref2, ref3
      • K-Means
      • Hierarchical Clustering
      • K-Modes
    • Topic Modeling ref1, ref2
    • Unsupervised Deep Learning

  • Supervised

    • Regression ref1, ref2, ref3, ref4, ref5

    • Classification ref1, ref2, ref3

    • Linear Models

      • Linear Regression

        • Polynomial Regression ref1 ref2
        • Stepwise Regression
        • Ridge Regression
        • Lasso Regression
        • ElasticNet Regression

      • Logistic Regression (Categorical) ref1, ref2, ref3

      • Support Vector Machines ref1, ref2, ref3, video

        • SVR ref1, ref2
        • SVC ref1, ref2

      • Passive-Aggressive

      • Perceptron

    • Bayesian Models

      • Naive Bayes
      • Gaussian Naive Bayes
      • Bernoulli Naive Bayes

    • Guassian Mixtures package

    • k Nearest Neighbors (kNN)

    • Decision Tree ref1, ref2

    • Ensemble Models ref1, ref2 ref3, package

      • Bagging
        • Random Forest
        • Extremely Randomized Trees
        • Bagging Classifier, package
      • Boosting
        • Adaboost
        • GBM
        • XGBoost, package
        • LightGBM
        • CatBoost
        • Regularized Greedy Forests, package
      • Stacking
      • Voting

    • Genetic Algorithms

      • TPOT,
      • Auto SKLearn

    • Neural Networks and Deep Learning ref1, ref2, ref3, ref4, ref5, video, meme

      • Convolutional Neural Networks ref1, ref2, ref3
        • Convolution for NLP
      • Sequence Models
        • Word2vec ref1, trained-models
      • Generative Adversarial Networks
      • Neural Network concepts

        • Weight Initialization

        • Weight Averaging

        • Gradient Descent ref1, ref2

        • Backpropagation

        • Neural Network Visualization

• Data Science Tasks

  • Time Series Analysis ref1, ref2, ref3
  • NLP and Text Mining
  • Information Retrieval
  • Graphs and Network Analysis
  • Sentiment Analysis ref1, ref2, ref3, ref4, ref5
  • Recommender Systems

• Data Science and Visualization Tools

  • Versioning Tools
    • Git
    • Github
    • Gitlab
  • Exploratory Data Analysis Tools
    • Jupyter ref1, ref2
    • Numpy ref1, ref2
    • Pandas ref1, ref2
      • Pandas Machine Learning
      • Geopandas
    • Statsmodels
  • Machine Learning Tools
    • Scikit-Learn
      • Inbalanced Learn
      • ForestCI
    • Tensor Flow
    • Keras
    • Gensim
    • Orange
  • NLP Tools
    • NLTK
    • Spacy
    • TextBlob
  • Visualization Tools
    • Matplotlib
    • Seaborn
    • Bokeh
    • Plotly
    • Altair
    • GGPlot2
    • MPLD3
      • d3.js
    • HoloViews
    • Folium
      • Leaflet
  • Big Data and Distributed computing
    • Map Reduce
    • Spark
  • Analytical Pipelines
    • Scikit-Learn
    • Luigi
    • Airflow
  • Other Tools
    • NetworkX
    • ETE Toolkit
    • ODO
  • Relational databases and SQL
  • NoSQL Databases
  • Graph Databases