Training Plan

 

Day Training Title Date
1 Introduction to Data Science January 7, 2024
2 Statistics Review for Data Science January 9, 2024
3 Introduction to Data Processing in Python 1 January 14, 2024
4 Introduction to Data Processing in Python 2 January 16, 2024
5 Introduction to Machine Learning 1 January 21, 2024
6 Machine Learning Applications in Python 1 January 23, 2024
7 Introduction to Machine Learning 2 January 28, 2024
8 Machine Learning Applications in Python 2 January 30, 2024
9 Introduction to Machine Learning 3 February 4, 2024
10 Machine Learning Applications in Python 3 February 6, 2024

Introduction to Data Science

  • Introduction to data science
  • Discovery-based data science
  • Basic data types: nominal values, binary values, ordinal values, numeric values, interval values, vb.
  • Other data types: time series, text, graph, multimedia, geographical
  • General overview of end-to-end data science problem solving process:
    • Identifying the main purpose
    • Turning business problem into data problem
    • Identifying data sources, collecting and storing data
    • Controlling data and checking data quality
    • Data preprocessing steps
    • Building model from data, training machine learning models
    • Evaluating and visualizing model outcomes
    • Putting the model into production and identifying the effects on company performance

Statistics Review for Data Science

  • How can we summarize and visualize data sets?
  • How can we detect trends and outliers in data sets?
  • How can we model relationships between variables?
  • How can we deal with missing data?
  • How can we perform single and multiple variable regression? Why is it useful?
  • How can we select meaningful variables?

Introduction to Data Processing in Python 1

  • Quick introduction to Python programming language
    • Basic data structures
    • Statements, conditions, loops
    • File input/output (I/O)
    • Programming exercises

Introduction to Data Processing in Python 2

  • Introduction to numpy library, “array” data structure and its basic functions
  • Visualization using matplotlib and seaborn libraries
  • Introduction to pandas library, “DataFrame” data structure and time series
  • Data loading, data analysis ve visualization exercises

Introduction to Machine Learning 1

  • What is machine learning?
  • What are supervised, semi-supervised and unsupervised learning?
  • What are classification, regression, clustering, dimensionality reduction and anomaly detection?
  • What are generalization and model capacity?
  • How should we divide the data for model training? What are training, validation, and test sets?
  • Regression methods
    • Linear regression
  • Classification methods 
    • Logistic regression
    • Decision trees

Introduction to Machine Learning 2

  • Support vector machines
  • Feature extraction methods
  • Feature selection methods
  • Model combination methods
  • Dimensionality reduction methods
    • Principal component analysis
    • Linear discriminant analysis
  • Clustering methods
    • K-means clustering
    • Hierarchical clustering

Introduction to Machine Learning 3

  • Introduction to deep learning
  • What is the reason that deep learning is so successful? Under which circumstances, it is inadequate?
  • How do we train deep learning models? (back-propagation algorithm, etc.)
  • What are special network architectures?
    • MLP, CNN, RNN, LSTM and GRU models
  • Parameters in deep learning models
    • Activation functions
    • Neuron counts in layers
    • Regularization methods

Machine Learning Applications in Python 1

  • Introduction to scikit-learn library
  • Linear regression
  • Logistic regression
  • Decision trees
  • Model and hyper-parameter selection methods
  • Support vector machines
  • Feature extraction methods
  • Feature selection methods

Machine Learning Applications in Python 2

  • Model combination methods
  • Tree-based models
  • Dimensionality reduction methods
    • Principal component analysis
    • Linear discriminant analysis
  • Clustering methods
    • K-means clustering
    • Hierarchical clustering

Machine Learning Applications in Python 3

  • Introduction to tensorflow and keras libraries
  • MLP models
  • CNN models
  • RNN models
  • LSTM and GRU models