Yoav Ram

Python Training for Engineers, Data Scientists, and Everyone


About


‐ Faculty at School of Computer Science, IDC Herzliya
‐ Postdoctoral fellow at Stanford University
‐ PhD in Mathematical & Computational Biology from Tel Aviv University
‐ Excellence in Teaching Award from the Faculty of Engineering, Tel Aviv University & School of Computer Science, IDC Herzliya

‐ Programming with Python since 2002
‐ Teaching Python since 2011
‐ Specializing in Data Science & Scientific Computing with Python:
  Jupyter, NumPy, SciPy, Matplotlib, Pandas, Scikit-learn & Keras
‐ Based in Israel and California, available Worldwide
New! Now on Zoom, too!

I develop and give Python programming courses with focus on numerical, scientific, and statistical applications. To enhance participant learning, all course material is fully interactive, using Jupyter notebooks, and all lectures include hands-on exercises.

Browse the offered courses below or contact me for customized courses.

Testimonials


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Python for Engineers

Python Programming for MATLAB® Users

The course is intended for engineers with MATLAB® experience that are interested in applying their knowledge and skills using the Python programming language.

The course combines practical programming skills in the Python programming language with a comprehensive overview of major numerical, mathematical, and statistical libraries. The course is taught entirely using interactive notebooks and includes hands-on exercises.

  • Four-five days, 32-40 hours of interactive lectures in a computer lab
  • Built-in exercises
  • Python 3.8
  • All course material in interactive Jupyter notebooks
  • Topics:
    • Basic Python
    • Idiomatic Python
    • Object Oriented Programming
    • I/O
    • N-dimensional arrays
    • Plotting & Visualizations
    • Linear algebra
    • Calculus
    • Digital signal processing
    • Image analysis
    • Statistics
    • Stochastic processes
    • Curve fitting
    • Optimization
    • Machine learning
    • Deep learning
    • Symbolic mathematics
    • High performance computing
    • Testing and debugging
    • User interfaces
    • RESTful web services
    • Monitoring and event scheduling
  • Python libraries:

Introduction to Python

One Day Workshop on Data Science with Python for Developers and Engineers

This one day workshop is intended for software developers and engineers interested in a quick introduction to the Python programming language and its use for data science.

The one day workshop combines an introduction to the basics of the Python programming language with a preview of the common tools used for data analysis and visualization. The workshop is taught entirely using interactive notebooks and includes hands-on exercises.

Machine Learning with Python

One Day Workshop on Data Analysis and Machine Learning with Python

The workshop is intended for developers and engineers with Python experience interested in machine learning with Python.

The one day workshop provides an introduction to common tools used for data analysis and visualization in Python and to libraries used for machine learning (scikit-learn). The course is taught entirely using interactive notebooks and includes hands-on exercises.

  • 8 hours of interactive hands-on sessions
  • Built-in exercises
  • Python 3.8
  • All course material in interactive Jupyter notebooks
  • Topics:
    • Data processing and analysis
    • Plotting & Visualizations
    • Machine learning
    • Deep learning
    • Convolutional neural networks
  • Python libraries:

Bayesian Inference with Python

One Day Workshop on Bayesian Onferenece with Python

The workshop is intended for developers and engineers with Python experience interested in Bayesian inference with Python.

The one day workshop provides an introduction to Bayesian inference, including a comparison to maximum likelihood inference, use in linear models, approximate Bayesian computation, and use of popular Bayesian libraries (emcee or PyMC3). The course is taught entirely using interactive notebooks and includes hands-on exercises.

  • 8 hours of interactive hands-on sessions
  • Built-in exercises
  • Python 3.8
  • All course material in interactive Jupyter notebooks
  • Topics:
    • Statistical inference with maximum likelihood
    • Bayesian inference with MCMC
    • Sampler (emcee, SMC)
    • Bayesian linear models
    • Approximate Bayesian computation
  • Python libraries:

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