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Data Science Using Python & R Certification Course Training

Data Science is the prerequisite for making your data effective. Unravel and unearth new opportunities in Data Science. Learn Statistical Analysis, Machine Learning, Predictive Analytics, and much more.
  • 48 Hours Classroom & Online Sessions
  • 80+ Hours Assignments & eLearning
  • 100% Job Assistance
  • 2 Capstone Projects
  • Blockchain enabled tamper-proof security certificate
  • 100% HRD Corp Claimable!
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data science review in malaysia - 360digitmg
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Academic Partners & International Accreditations
  • Data Science foundation with innodatatics
  • Data Science foundation with SUNY
  • data scientist certification panasonic
  • Data Science foundation with Microsoft

Calendar-for-Virtual Interactive Classes

Start Date

Data Science Course

data science in malaysia - 360digitmg

Total Duration

3 Months

data science training in malaysia - 360digitmg

Prerequisites

  • Computer Skills
  • Basic Programming Knowledge
  • Analytical Mindset

Data Science Certificate Course Overview

This three-month Data Science certification with Python and R programming will empower students with skills in Data Analytics, Data Mining, Machine Learning, Predictive Modelling, and Regression Analysis. In this Data Science Training based in Malaysia, they will learn to unleash the power of Python and R to create machine learning and neural network algorithms. This course aims to serve the learning needs of IT professionals and college students gearing to make the transition to data science. The student will appreciate descriptive and predictive analytics and learn to analyze structured and unstructured data with various tools and techniques like Data Preparation, Data Cleansing, Exploratory Data Analysis, Feature Engineering, Feature Extraction, Feature Selection, and Text Mining. At the end of this course, they will build prediction models for daily operability. They can also master Black box techniques, Neural Network programming, and Data Visualization methods. Propel yourselves to great heights in your Data Science career with the aid of this program from the best Data Science training institute from Malaysia

 

What is Data Science?

Data science is an amalgam of methods derived from statistics, data analysis, and machine learning that are trained to extract and analyze huge volumes of structured and unstructured data.

 

Who is a Data Scientist?

A Data Scientist is a researcher who has to prepare huge volumes of Big Data for analysis, build complex quantitative algorithms to organize and synthesize the information, and present the findings with compelling visualizations to senior management.

 

A Data Scientist enhances business decision making by introducing greater speed and better direction to the entire process.

A Data Scientist must be a person who loves playing with numbers and figures. A strong analytical mindset coupled with strong industrial knowledge is the skill set most desired in a Data Scientist. He must possess above the average communication skills and must be adept in communicating the technical concepts to non - technical people.

Data Scientist Certification Malaysia need a strong foundation in Statistics, Mathematics, Linear Algebra, Computer Programming, Data Warehousing, Mining, and modeling to build winning algorithms.

They must be proficient in tools such as Python, R, R Studio, Hadoop, MapReduce, Apache Spark, Apache Pig, Java, NoSQL database, Cloud Computing, Tableau, and SAS.

Learning Outcomes of Data Science Course in Malaysia

In this data-driven environment certification in Data Science course in malaysia prepares you for the surging demand of Big Data skills and technology in all the leading industries. There is a huge career prospect available in the field of data science and this programme is one of the most comprehensive course in malaysia in the industry today. This training will equip the students with logical and relevant programming abilities to build database models. The students will explore the various stages of the Data Science Lifecycle in the trajectory of this course. Initially conceptualize Data preparation, Data Cleansing, Exploratory Data Analysis, and Data Mining (Supervised and Unsupervised). Progressively learn the theory behind Feature Engineering, Feature Extraction, and Feature Selection. Perform Predictive modeling using regression analysis. Build machine learning, Deep Learning, and Neural Network algorithms with Python and R language. Apprehend forecasting to take proactive business decisions. Script algorithms for neural networks, time series analysis and forecasting.

Work with various data generation sources
Perform Text Mining to generate customer Sentiment analysis
Analyse Structured and Unstructured data using different tools and techniques
Develop an understanding of Descriptive and Predictive Analytics
Apply data-driven, Machine Learning approaches for business decisions
Build prediction models for day-to-day applicability
Perform forecasting to take proactive business decisions
Use Data Visualisation concepts to represent data for easy understanding

Block Your Time

data science course - 360digitmg

48 hours

Classroom & Online Sessions

data science course - 360digitmg

80+ hours

Assignments & eLearning

data science course duration - 360digitmg

2

Capstone Projects

Who Should Sign Up?

  • Business Analysts, Data Analyst, Data Scientist, Data Engineer, Project Managers for Data Analytics or Data Stream projects
  • Schools, Universities and Colleges looking to upskill their faculties in Digital Courses
  • Graduates who are looking to build a career in Data Science, Machine Learning, Forecasting, Business Intelligence, etc.
  • Students who are aiming to work in IT industry on emerging technologies

Data Science Courses Modules in Malaysia

This course begins with an introduction to Statistics, Probability, Python and R programming, And Exploratory Data Analysis. Participants learn to perform Data Mining Supervised with Linear regression and Predictive Modelling with Multiple Linear Regression techniques. Data Mining Unsupervised using Clustering, dimension reduction, and association rules is also dealt with in detail. A module is dedicated to scripting machine learning algorithms and enabling Deep Learning and Neural Networks with Black Box techniques and SVM. Learn to perform proactive forecasting and time series analysis with algorithms scripted in Python and R.

Get about, CRISP - ML(Q) the perfect Project Management Methodology used for handling Data Mining projects. Understand the entire process flow including Business Problem definition, Data Collection, Data Cleansing, Feature Engineering, Feature Selection, Model Building, Deployment and Maintenance. Get introduced to the principles of big data and learn about the opportunities being created. Understand about how Data is generation and explosion of data, Innovations in the space of analytics. Learn how to distinguish between data types, Exploratory data analysis, the Various moments of Business decisions and various Graphical techniques. Learn about probability and probability distribution namely Z distribution and Student's t-distribution.

Learn about Hypothesis testing, the many Hypothesis testing Statistics, work with the Null Hypothesis & Alternative hypothesis and Types of hypothesis testing. Interpret the results of Hypothesis test and probabilities of Alpha error, understand Type I and Type II errors. Get introduced to Linear regression, various components of Linear regression viz regression line, Linear regression equation, the concept of Ordinary Least Square. Get introduced to Linear regression analysis, and Linear regression examples.

Understand the Linear regression in a multivariate scenario, understand collinearity and how to deal with it. Get introduced to the analysis of Attribute Data, understand the principles of Logistic regression, Binary Logistic regression analysis. Learn about the Multiple Logistic regression, Probability measures, and its interpretation. Get clarity on the confusion matrix and its elements. Get introduced to “Cut off value” estimation using AUC and ROC curve, understand False Positive Rate, False Negative Rate, Sensitivity, Specificity. Gain a birds-eye view to various advanced regression techniques and analysis of count data namely Poisson regression, Negative binomial regression. Learn when to use Poisson regression and negative binomial regression for predicting count data.

Learn about modeling using KNN, the K nearest neighbour algorithm using KNN algorithm examples. The KNN classifier is one of the most popular classifier algorithms. Decision tree & Random forest are one of the most powerful classifier algorithms today. Under this tutorial learn about Decision Tree analysis, Decision Tree examples and Random Forest algorithms. Also learn about the various ensemble machine learning algorithms. Text Mining or Text Data Mining are the most widely used analyzing tools for unstructured data. As part of the session, learn about Text analytics and the various text mining techniques in the text mining application, text mining algorithms, and sentiment analysis. Gain a ‘hands-on’ on how to extract data from Social Media, download user reviews from E-commerce sites and travel sites. Generate various visualizations using the downloaded data.

Under the Naïve Bayes classifier tutorial, learn how the classification modelling is done using Bayesian classification, understand the same using Naïve Bayes example. Learn about Naïve Bayes through the example of text mining. Artificial Neural Network and Support Vector Machines are the 2 powerful Deep learning algorithms. Get introduced to Perceptron Algorithms, Artificial Neural Networks, Multilayer Perceptron (MLP). Learn how to work with Support Vector Machine, SVM classifiers, and SVM regression. Get introduced to Association rules in data mining to decode the relationship between entities, understand how the Apriori algorithm works, and the association rule mining algorithm works.

Description: As part of data mining unsupervised, get introduced to various clustering algorithms, learn about Hierarchical clustering, K-means clustering using clustering examples, know what clustering machine learning is all about. Learn about K-means Clustering, Clustering ratio, and various clustering metrics. Get introduced to methods of making optimum clusters. Learn the need for data reduction in data mining using dimensionality reduction techniques. Learn about the advantages of dimensionality reduction using PCA. Get introduced to the difference between cross-sectional data and Time-series data. Various stages of forecasting projects, components of Time-series, visualization techniques, model-based techniques and learning how to evaluate the forecasting models accuracy.

Self-paced Data Science Course Modules

Extract meaningful information from temporal data, enabling accurate predictions and insights.

  • RNN, Bidirectional RNN, Deep Bidirectional RNN
  • Transformers for Forecasting
  • N-BEATS, N-BEATSx
  • N-HiTS
  • TFT - Temporal Fusion Transformer

Understands and uses advanced models that can generate consistent, contextual information across applications.

  • Sequence 2 Sequence Models
  • Transformers
  • Generative AI
  • ChatGPT
  • DALL-E-2
  • Mid Journey
  • Crayon

Harness the power of well-designed prompts to better interact with language models and unlock their true potential.

  • What Is Prompt Engineering?
  • Understanding Prompts: Inputs, Outputs, and Parameters
  • Crafting Simple Prompts: Techniques and Best Practices
  • Evaluating and Refining Prompts: An Iterative Process
  • Role Prompting and Nested Prompts
  • Chain-of-Thought Prompting
  • Multilingual and Multimodal Prompt Engineering
  • Generating Ideas Using "Chaos Prompting"
  • Using Prompt Compression
Exclusive30% Year-End Discount Limited Time Offer!

Practical Data Analytics: Work-Integrated Learning Course

Dive deep into analytics and transform your career in just 6 months.

Elevate your data insights and seamlessly transition from learning to working.

  • Work-Integrated Learning: Transition from learning to working in 6 Months - 30 days
  • Tackle 3 Industry-specific real-time projects to refine and showcase your skills
  • Secure 100 hours of credible working experience in data analytics
  • HRDC claimable and 6 months instalments available

Offer ends:17th Feb,2024

How We Prepare You
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    Additional Assignments of over 60-80 hours
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    Unlimited Mock Interview and Quiz Session
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    Hands-on Experience in a Live Project
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