$319.89 – $425.98

4 Weeks Data Science Training in Hong Kong | Introduction to Data Science f...

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TruVs

Hong Kong

China

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Refunds up to 1 day before event

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Description

4 Weeks Data Science Training is being delivered as Instructor-led, guided training with Real-life, Practical Hands-On Lab exercises from April 6, 2020 - April 29, 2020 for 16 hours over 4 weeks, 8 sessions, 2 sessions per week, 2 hours per session.

  • All Published Ticket Prices are in US Dollars
  • This course will be taught in English


4 WEEKS Data Science TRAINING SCHEDULE



FEATURES AND BENEFITS
  • 4 weeks, 8 sessions, 16 hours of total Instructor-led and guided, Practical Hands-On training
  • Training material, instructor handouts and access to useful resources on the cloud provided
  • Practical Hands-on Lab exercises provided
  • Actual code and scripts provided
  • Real-life Scenarios


Data Science Training Course Pre-requisite Skills

It is not required but preferred that you have some basic understanding of:

  • Mathematics

  • Statistics

  • Any Programming Language


Who should take this this Course?
  • Any IT Professional interested in enhancing or building their career in in the field of Data Science or becoming Data Scientist.
  • Any Working Professional.
  • Data Science Enthusiasts.


Data Science Training Course Objectives

After completion of the Data Science Course, you will have the following knowledge:

  • Explore the data science process

  • Probability and statistics in data science

  • Data exploration and visualization

  • Data ingestion, cleansing, and transformation

  • Introduction to machine learning

  • The hands-on elements of this course leverage a combination of R, Python, and Machine Learning



Data Science Training Course Outline
  1. Introduction to Data Science
  2. Data Science Deep Dive
  3. Data Manipulation
  4. Data Import Techniques
  5. Exploratory Data Analysis
  6. Data Visualization
  7. Statistics
  8. Statistics basics
  9. Introduction to Machine Learning
  10. Understanding Supervised and Unsupervised Learning Techniques
  11. Clustering
  12. Implementing Association rule mining
  13. Understanding Process flow of Supervised Learning Techniques
  14. Decision Tree Classifier
  15. Random Forest Classifier
  16. What is Random Forests
  17. Naive Bayes Classifier.
  18. Problem Statement and Analysis
  19. Linear Regression
  20. Logistic Regression
  21. Text Mining
  22. Sentimental Analysis
  23. Support Vector Machines
  24. Deep Learning
  25. Time Series Analysis
  26. Data Preprocessing
  27. Linear And Logistic Regression Models.
  28. K-means and Hierarchical Clustering.
  29. Natural Language Processing.
  30. Artificial Neural Networks.
  31. Convolutional Neural Network.


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Date and Time

Location

TruVs

Hong Kong

China

View Map

Refund Policy

Refunds up to 1 day before event

Eventbrite's fee is nonrefundable.

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