6 Best Data Science Course Malaysia

Professional data scientists are in more demand than ever before in government, business, and academia. Data Science course Malaysia assist candidates prepare for industrial issues by providing them with the requisite information and high-end abilities for handling real-world problems. Whether you seek a fundamental understanding of Data Science or want to pursue a promising career in the subject, the specialist courses will provide you with the necessary experience.

Here is a selection of the finest data science courses to assist you expand your data science expertise.

In 2022, the top 6 data science course Malaysia will be available.

1. Statistics and Data Science MicroMasters Program

A five-course series is included in this curriculum to help students develop their foundation in machine learning, data science, and statistics. It is an excellent course for those interested in learning about big data analysis. You’ll also get a solid knowledge of how to use probabilistic modelling and statistical inference to make data-driven predictions. You may study more about statistics, data analysis methods, probability, machine learning algorithms, and more in this course.

The course covers a broad variety of subjects, including:

• Introduction to Probabilistic Models
• In social science, data analysis is important.
• Fundamentals of Statistics
• Python and Machine Learning
• Analyzing Big Data
• Deep Neural Networks are a kind of neural network.
• Methodologies for Clustering

Students who complete this specialty may apply for jobs such as system analyst, data analyst, and data scientist. In addition, you’ll get practical experience with both unsupervised and supervised learning approaches.

Prerequisites: Students must have a working knowledge of Python programming, mathematical reasoning, and college-level mathematics.

Intermediate level

14-month duration 10–14 hours each week (approximately)

2. Course on Data Science Specialization

This course covers all of the tools and ideas you’ll need to get started with data science. They begin by asking the correct questions in order to make conclusions, and then they publish the findings. The final capstone project showcases the skills you acquire by leveraging real-world data to create a data product. When this course is done, students will have a fantastic portfolio to show off their knowledge of the topic.

It’s a fun and entertaining programme where you may learn about the following subjects:

• Github
• R-programming.
• Artificial Intelligence (AI)
• Science of Data
• Analysis of Regression
• Rstudio
• Debugging
• Analyze the data
• Analysis of Clusters
• Expressions using a Regular Expression
• Manipulation of data
• Cleaning up your data

By enrolling in this course, you will gain knowledge of the major concepts and techniques found in the data scientist’s toolkit. You’ll learn about the tools, questions, and data that data scientists and analysts need to do their jobs. This course consists of two parts, one of which is the understanding of how to transform data into actionable information.

Programming experience is required (in any language). We also suggest that the student be familiar with math (linear and calculus algebra are not required).

Beginner’s level

11-month duration 7 hours each week (approximately)

3. Python for Machine Learning, Data Science, and Deep Learning

Python for Machine Learning, Data Science, and Deep Learning

Artificial neural networks, K-means clustering, and other significant subjects in machine learning are covered in this speciality. You’ll also learn the intricacies of data visualisation using Seaborn and MatPlotLib, as well as how to use MLLib Apache Spark to apply machine learning on a big scale.

The following are some of the major topics covered in this course:

• Keras with TensorFlow for Neural Networks and Deep Learning
• Learning Transfer
• Classification and identification of images
• Analysis of public opinion
• Models with Multiple Levels
• Analysis of Regression
• Regression with Multiple Steps
• Random Forests and Decision Trees are two types of decision trees.
• Experimentation and A/B Testing
• Filtering by Collaboration
• Learning through Reinforcement
• Vector Support Machines (SVMs)
• Engineering of Features
• Hyperparameter Tuning, among other things.

You’ll also learn how to identify attitudes, photos, and data using deep learning ideas in this course. It’s an excellent learning programme for experienced programmers and data analysts who want to change jobs. Even if you are new to Python, you may choose this specialty since it includes a crash course to help you learn the language.

Prerequisites:

A Linux, Mac, or Windows machine capable of running Anaconda 3.

It is required that you have prior scripting or programming expertise.

It would be advantageous if you were proficient in high school maths.

Intermediate level

14-hour duration (approximately)

4. Using Javascript for Machine Learning

Javascript for Machine Learning

This machine learning course, designed for Javascript developers, will go deep into sophisticated memory profiling, constructing Tensorflow JS library-powered applications, writing ML code, and other key subjects for a full grasp of the field.

You’ll also learn how to write applications that work with both Node JS and the browser. With Linear Algebra principles, the software also covers the strategies and approaches for speeding up matrix-based routines.

The following are the main topics covered in this course:

• Identifying Data That Is Relevant
• Observation Data Recording
• Overview of Algorithms
• Concatenation of Tensors
• Tensorflow is used in a variety of ways.
• Regression Linear
• Multiplication of Matrixes
• Increased performance using vectorized solutions
• Using Javascript to Plot MSE Values
• Logistic Regression is a technique for predicting the outcome of
• Gradient Descent: Stochastic and Batch

 

In addition to the above, you’ll learn how to adapt algorithms to different use situations. Furthermore, the course will provide you with access to Javascript code performance-enhancing approaches and tactics. The best aspect is that you may take this course even if you have no prior knowledge of mathematics since the lectures do not include difficult arithmetic topics.

Prerequisites

Basic command and terminal line skills are required.

The capacity to solve fundamental mathematical problems.

Intermediate level

17.5-hour duration (approximately)

5. Python Machine Learning: The Complete Course

Python’s Complete Machine Learning Course

If you’re seeking for a course that will help you create a solid foundation in Machine Learning, this programme is the one to go with. You’ll learn how to tell the difference between machine learning and traditional programming, as well as deep learning and machine learning. In addition, you’ll learn about neural networks, tensor operations, and advanced subjects like validation, dropout, testing, regularisation, underfitting, and overfitting.

The following subjects are covered in depth in this learning programme:

• Scikit-Learn Linear Regression
• Regression with Robustness
• Cross-validation
• Logistic Regression is a technique for predicting the outcome of
• Matrix of Perplexity
• Support Vector Machine Concepts
• The Radial Basis Function (RBF) is a mathematical function that
• SVM Linear Classification
• Boundary Visualization
• Methods of Machine Learning in Ensembles
• Machine for Increasing Gradients
• Introduction to kNN
• Concept of Dimensionality Reduction
• Clustering

You’ll get a solid grasp of the machine learning technologies that are used to solve real-world problems. It’s an excellent course for learning about machine learning performance indicators such as recall, R-squared, confusion matrix, MSE, prediction, and accuracy.

Prerequisites:

A basic understanding of Python programming is necessary.

Linear algebra knowledge.

Beginner-intermediate level

17.5 hours in length (approximately)

6. Machine Learning

The key topics covered in this course are as follows:

• Basics of Machine Learning
• Analyze the Principal Components
• Algorithms for Machine Learning
• Recommendation System for Buildings
• Regularization and its Applications
• Cross-Validation

The curriculum also teaches you how to work with training data and how to efficiently use a data set to find predictive associations. When you enrol in this course, you’ll learn how to use machine learning in a variety of products, including voice recognition, postal service, spam detectors, and more.

There are no prerequisites.

Beginner’s level

8-week programme — 2-4 hours each week (approximately)