Centennial Tower

5F, Jl. Gatot Subroto No.24-25, RT.2/RW.2, Karet Semanggi, Kecamatan Setiabudi, Kota Jakarta Selatan, Daerah Khusus Ibukota, Jakarta Selatan, Indonesia, 12930

Grand Slipi Tower

Jl. Letjen S. Parman No.24, RT.1/RW.4, Palmerah, Kec. Palmerah, Kota Jakarta Barat, Daerah Khusus Ibukota Jakarta , Jakarta Barat, Indonesia, 11480

The City Tower

12F, Jl. M.H. Thamrin No.81, Dukuh Atas, Menteng, Kec. Menteng, Kota Jakarta Pusat, Daerah Khusus Ibukota , Jakarta Pusat, Indonesia, 10310

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Mathematica

Explore Our Courses

Data Science: Analysis and Presentation

7 Hours
The Wolfram System's integrated environment makes it an efficient tool for both analyzing and presenting data. This course covers aspects of the Wolfram Language relevant to analytics, including statistical computation, visualization, data import and export and automatic generation of reports.
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Mathematica - wprowadzenie, wizualizacja i prezentacja danych

14 Hours
The "Mathematica - wprowadzenie, wizualizacja i prezentacja danych" training is aimed at participants with basic mathematical knowledge. Mathematica, as a powerful mathematical engine, enables effective calculations, creating text documents, building charts and presenting data. Participants will gain practical skills in working with Mathematica, used, among others, in mathematics, physics, biology, financial analysis and other fields.
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Mathematica for Machine Learning

14 Hours
This instructor-led, live training in Jakarta (online or onsite) is aimed at data scientists who wish to use machine learning in Mathematica for data analysis. By the end of this training, participants will be able to:
  • Build and train machine learning models.
  • Import and prepare data for machine learning.
  • Separate training data from test data.
  • Explore deep learning and neural network applications in data analysis.
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Mathematica: An Introduction

7 Hours
Introduces the core technologies needed to become an adept user of Mathematica, including the Wolfram Language, the notebook interface, programming fundamentals, visualization and interactivity features, and mathematics and statistics functionality. Small class sizes allow for interaction with the instructor, and in-person courses allow additional time for discussion, exercises, and Q&A.
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Testimonials (2)

The theoretical explanations

Molatelo Tloubatla - University Of South Africa
Course - Data Science: Analysis and Presentation

Machine learning, python, data manipulation

Siphelo Mapolisa - University Of South Africa
Course - Data Science: Analysis and Presentation

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