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Professional Certificate Course in

Big Data & Analytics

Acquire the essential data engineering skills with this dual certification in Big Data Analytics and Data Science.
  • 72 Hours Classroom & Online Sessions
  • 140+ Hours Assignments & eLearning
  • 100% Job Assurance
  • 2 Capstone Projects
  • Industry Placement Training
big data & analytics course reviews in malaysia - 360digitmg
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big data & analytics course reviews in malaysia - 360digitmg
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Academic Partners & International Accreditations
  • Big Data & Analytics course with ibm certificate
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Calendar-On-Campus Classes

Big Data & Analytics

Professional big data analytics course duration

Total Duration

4 Months

Professional big data analytics course pre-requisites

Prerequisites

  • Computer Skills
  • Basic Math Knowledge

"The Big Data and Business Analytics market are projected to reach $27 billion (about SG $ 37 billion) by 2022."- (Source). Malaysia is striving to transform itself as the Southeast Asian hub on Big data and analytics. By the data from Malaysian Digital Economy Corporation (MDEC), 22 multinational BDA companies from around the world are established in Malaysia. Among them are IHS Markit and Sitecore. Malaysia is working hard to produce talent in Data Science and expecting to produce 17000 data professionals (local and foreign) by the end of 2020. MDEC is making an effort to build bridges with digital innovation ecosystems and Big Data Analytics which creates many opportunities. This generation is not looking for routine, mundane jobs. Advancements in technology are creating smarter talents that are essential for a business to be at a competitive edge and gain revenues. Furthermore, Big Data and Analytics will be extensively used to resolve prime problems in Malaysia to make its citizens happy.

Big Data & Analytics Course Programme Overview

The Professional Certification in Big Data and Analytics is a foundation course that is apt for both beginners and professionals. In the Big Data Analytics module, they will learn how to use Apache Hadoop to extract useful data and Apache Spark to analyse it further. In the Data Science module, they will learn the core principles of Python and R programming. They will also comprehend data ingestion tools and practical applications of SQOOP. The student will also learn the differences between SQL and no SQL database systems. Students will earn two certificates in this Big Data and Analytics course from 360DigiTMG Malaysia. With Big Data and Analytics certification course in Malaysia, students will be empowered with skills in Data Analytics, Data Mining, Machine Learning, Predictive Modelling, and Regression Analysis in addition to programming languages Python, R, Spark, Hadoop, VMware, and Hive.

Big Data and Analytics

Big Data Analytics is the process of exploring large varied data to extract the hidden patterns, correlations, and convert them to useful insights as per the business requirements. Many companies are adopting Big Data and Analytics to be more efficient and gain revenues. It is cost-effective and gives immediate results. The ability of Big Data and Analytics to work at speed and to be agile helps industries to gain a competitive edge.

Course Details

Big Data & Analytics Training Outcomes

With the emergence of new and advanced technologies like Machine learning, Data Science, Big Data Analytics, Artificial Intelligence transformed the operation of the business. 360DigiTMG is providing a Big Data and Analytics professional certification course with the vision to nurture talent and provide a smarter workforce for the digitally transformed business. In this course, students will learn the application of important tools like Spark, Hadoop, Hive, and advanced versions of it. Will be able to understand and differentiate Structured and Unstructured Data. Will learn the applications of Descriptive and predictive analytics. Learn about Data visualization, Data Mining, and be able to perform customer sentiment analysis. Will be able to forecast and take proactive decisions for the business. Will learn about Machine learning approaches for business decisions, and get equipped with knowledge on the application of various Big data and analytics.

Learn about the tools and techniques used to analyse structured and unstructured data
Understand the differences between Descriptive and Predictive Analytics
Perform text mining to generate customer sentiment analysis
Use data-driven, Machine Learning approaches for business decision-making
Build prediction models for day-to-day applicability
Perform forecasting to take proactive business decisions
Learn Data Visualization concepts to represent data in suitable formats
Install and setup Hadoop and Spark environments to store and process data
Understand the advantages of distributed batch processing using the Hadoop Distributed File System
Learn about Hadoop 1.x, 2.x and 3.x versions
Perform exploratory queries on data batches using Pig, Hive and Spark
Learn Spark RDD optimisation techniques
Write programs in the Big Data domain as per the system architecture
Block Your Time
professional big data & analytics training - 360digitmg

72 hours

Classroom Sessions

professional big data & analytics training - 360digitmg

100 hours

Assignments &
e-Learning

professional big data & analytics training - 360digitmg

100 hours

Live Projects

Who Should Sign Up?
  • Those aspiring to be Data Scientists, Big Data Analysts, Analytics Managers/ Professionals, Business Analysts, Data Analytics Developers
  • Graduates interested in Data Science related fields
  • Interested in mid-career shift to Big Data
  • Academicians and Researchers

Big Data & Analytics Modules in Malaysia

By Big Data and Analytics professional certification course in Malaysia, students will get introduced to the world of Big Data and understand the 4 V’s which define Big Data. Learn about the challenges concerning Big Data and the workaround technique called distributed framework tools used for churning Big Data. Learn how these challenges Big Data is addressed by a distributed computing framework. Learn about the most user-friendly and the first multi-user operating system which is the preferred OS for the implementation of an open-source distributed framework tool called Hadoop. Students will have hands-on exposure on Linux OS, Spark, Hive, and Apache pig which are high-level programming languages to assist developers. Students will understand how enterprises use tools to move the data from legacy systems on to Big data. Learn about the concept of Data Ingestion. Understand the need to migrate the data from a traditional database system (SQL) to Big Data tools. Learn about quick migration of data into HBase tables from RDBMS systems and vice versa. Learn to use the open-source tool SQOOP (the combination of Hadoop and SQL) to create a pipeline from the SQL database to Hadoop. Will be exposed to Black box machine learning algorithms that are extremely important in the field of machine learning. Learn about the Perceptron algorithm and Multi-layered Perceptron algorithm or MLP. Understand about Kernel tricks used within Support Vector Machine algorithms. Understand about linearly separable boundaries as well as non-linear boundaries and now to solve these using Deep learning algorithms. By the completion, of course, students will be well prepared for joining in big companies and face the challenges.

Get introduced to the world of Big Data and understand the 4 V’s which define Big Data. Learn about the challenges concerning Big Data and the workaround technique called distributed framework tools used for churning Big Data. Learn how these challenges Big Data is addressed by a distributed computing framework.

Learn about the most user-friendly and the first multi-user operating system which is the preferred OS for the implementation of an open-source distributed framework tool called Hadoop. The filesystem for the Hadoop framework should be distributed to handle the huge amount of data. The filesystem of Linux OS (ext3, ext4, and xfs) are capable of supporting the distributed framework. Having hands-on exposure on Linux OS is a very relevant requirement to excel in working with Big Data tools. You will learn to install and work with Linux OS. You will also learn to install a pseudo-single-node Hadoop environment cluster. Hadoop Distributed File System.

Learn how HDFS stores a huge volume of data without data loss and fault tolerance. You will understand the concepts of replication and partitioning that is used in HDFS. Learn about the java background services also known as Demons working to make Hadoop capable of storing Big Data that cannot be fit into a single System.

Learn the logic of the distributed computing framework implemented by Google. Learn the concept of Map jobs and Reduce jobs. Learn how Mapper functions and Reducer functions work in tandem to process huge volumes of data. Understand the functionality of the processes of the MapReduce component of Hadoop. Understand input splits and learn how they are different from blocks in HDFS.

Understand the Big Data Ecosystem and its projects. Learn about the drawbacks of distributed computing, MapReduce framework. You have learned about the low-level language used for MapReduce framework, Apache Pig is a high-level programming language to assist the developers. Learn about the high-level programming languages developed by Yahoo on the MapReduce framework. Learn about the ETL tool Apache Pig, the features, components and the execution model. Learn about the ways to execute the Apache Pig Latin scripts on Mapreduce and Local mode.

An open-source programming tool developed by Facebook to handle structured data on Big Data framework. Get introduced to the SQL programming tool, Apache Hive. Understand its applications as a Data warehousing tool. You will learn how Hive manages and handles the schema of the tables created using an RDBMS database called Metastore. Learn about internal and external tables that can be created using Hive.

Learn about the first database on the distributed file system & HBase. Understand how NoSQL databases are different from SQL based databases. Learn about the installation of HBase on Hadoop, its use and advantages. Understand the architecture of HBase and its components. Learn about Hfiles and Memstore concept used in HBase to store the data.

Understand how enterprises use tools to move the data from legacy systems on to Big data. Learn about the concept of Data Ingestion. Understand the need to migrate the data from a traditional database system (SQL) to Big Data tools. Learn about quick migration of data into HBase tables from RDBMS systems and vice versa. Learn to use the open-source tool SQOOP (the combination of Hadoop and SQL) to create a pipeline from the SQL database to Hadoop.

Understand the need for a new age tool to handle the Big Data as the latency of MapReduce programs are very high. Learn about the lightning-fast Unified stack programming language framework in the Analytics community which was developed for general purpose, in-memory computing to attain super speeds of execution, and distributed computing - Apache Spark. Understand Apache Sparks architecture and its building blocks and components. You will learn about the default data abstraction used by spark called RDD.

Understand various data sources and why organizations are gearing up to store the data like never before. Learn on what are the various applications of data science in various industries ranging from FSI to LSHC to Retail and many more. Also one will appreciate the job opportunities in the space of data science, data modeling, and data analysis. Finally understand the golden rule on how to become a successful data scientist, data modeler, data analyst, etc.

Learn about the Project Management Methodology, CRISP-DM, for handling Data Science projects and various concepts used in defining business problems and then performing data collection in line with business problems. Understand the importance of documenting the business objectives and business constraints so that the entire project is performed to solve business problems. Project charter overview will help participants understand the real-world documentation aspect as well.

Learn about data preparation and data cleansing in data science projects to ensure that appropriate data is provided to the next step. Outlier analysis or treatment, handling missing values using imputation, transformation, normalization/standardization, etc., will be explained in thorough detail. Understand the various moments of a business decision and graphical representation so that structured descriptive analytics or descriptive statistics is performed. This exploratory data analytics is the first step in data analytics to draw meaningful insights.

Learn about applying domain knowledge to the data so that more meaningful variables are derived. Understand two main modules of feature engineering including feature extraction and feature selection. Knowing how to shortlist the critical inputs from trivial many inputs is the key to ensuring the high performance of the machine learning models. Understand about extracting features from structured as well as unstructured data such as videos, images, audio, textual files, etc.

Understand one of the key inferential statistical techniques called Hypothesis testing. Understand various parametric hypothesis tests. Learn about the implementation of a Regression method based on the business problems to be solved. Understand about Linear Regression as well as Logistic Regression techniques used to handle continuous as well as discrete output prediction. Evaluation techniques by understanding the measure of Error (RMSE), problems while building a Regression Model like Collinearity, Heteroscedasticity, overfitting, and Underfitting are explained in detail.

Understand the advanced regression models such as Poisson Regression, Negative Binomial Regression, Zero-Inflated models, etc., used to predict the count output variables. Learn about the various scenarios which trigger the application of advanced regression techniques. Understanding and evaluating the models using appropriate performance and accuracy measures of regression are explained in detail.

Data Mining branch called unsupervised learning is extremely important in solving problems, which require the application of only unsupervised learning tasks and also used to support predictive modeling. Clustering or segmentation has two prime techniques – K-Means clustering, as well as Hierarchical clustering and both, are explained in finer detail. Alongside, participants will also learn about handling datasets with large variables using dimension reduction techniques such as Principal Component Analysis or PCA. Finally one will learn about Association rules also called affinity analysis or market basket analysis or relationship mining.

The majority of unstructured data is in textual format and analyzing such data requires special techniques such as text mining or also called as text analytics. Techniques such as DTM/TDM using Term Frequency, Inverse Document Frequency, etc. are explained in this module. One will also learn about generating a word cloud, performing sentiment analysis, etc. Also, advanced Natural Language Processing techniques such as LDA, topic mining, etc., are explained using practical use cases. Also, the learning includes extracting unstructured data from social media as well as varied websites.

A major branch of study in data science is Machine Learning also called Data Mining Supervised Learning or Predictive Modelling. One will learn about K Nearest Neighbors (KNN), Decision Tree (Boosting), Random Forest (Bagging), Stacking, Ensemble models and Naïve Bayes. One will learn about the various regularization techniques as well as understand how to evaluate for overfitting (variance) and underfitting (bias). All these are explained using industry relevant use cases and mini-projects.

Black box machine learning algorithms are extremely important in the field of machine learning. While there is no interpretation in the models, accuracy is unmatched in comparison to other shallow machine learning algorithms. Learn about the Perceptron algorithm and Multi-layered Perceptron algorithm or MLP. Understand about Kernel tricks used within Support Vector Machine algorithms. Understand about linearly separable boundaries as well as non-linear boundaries and now to solve these using Deep learning algorithms.

Understand the difference between cross-sectional data versus time series data. Search about the forecasting strategy employed in solving business problems. Understand various forecasting components such as Level, Trend, Seasonality & Noise. Also, learn about various error functions and which one is the best given a business scenario. Finally, build various forecasting models ranging from linear to exponential to additive seasonality to multiplicative seasonality.

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Big Data & Analytics Training Panel of Coaches

professional big data & analytics certification trainers - 360digitmg

Bharani Kumar Depuru

  • Areas of expertise: Data Analytics, Digital Transformation, Industrial Revolution 4.0.
  • Over 14+ years of professional experience.
  • Trained over 2,500 professionals from eight countries.
  • Corporate clients include Hewlett Packard Enterprise, Computer Science Corporation, Akamai, IBS Software, Litmus7, Personiv, Ebreeze, Alshaya, Synchrony Financials, Deloitte.
  • Professional certifications - PMP, PMI-ACP, PMI-RMP from Project Management Institute, Lean Six Sigma Master Black Belt, Tableau Certified Associate, Certified Scrum Practitioner, AgilePM (DSDM Atern).
  • Alumnus of Indian Institute of Technology, Hyderabad and Indian School of Business.
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professional big data & analytics certification trainers - 360digitmg

Sharat Chandra Kumar

  • Areas of expertise: Data Science, Machine Learning, Business Intelligence and Data Visualisation.
  • Trained over 1,500 professional across 12 countries.
  • Worked as a Data Scientist for 14+ years across several industry domains.
  • Professional certifications: Lean Six Sigma Green and Black Belt, Information Technology, Infrastructure Library.
  • Experienced in Big Data Hadoop, Spark, NoSQL, NewSQL, MongoDB, R, RStudio, Python, Tableau, Cognos.
  • Corporate clients include DuPont, All-Scripts, Girnarsoft (College-dekho, Car-dekho) and many more.
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Nitin Mishra

  • Areas of expertise: Data Science, Machine Learning, Business Intelligence and Data Visualisation.
  • Over 20+ years of industry experience in Data Science and Business Intelligence.
  • Trained professionals from Fortune 500 companies and students from prestigious colleges.
  • Experienced in Cognos, Tableau, Big Data, NoSQL, NewSQL.
  • Corporate clients include Time Inc., Hewlett Packard Enterprise, Dell, Metric Fox (Champions Group), TCS and many more.
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Certificate

Those aspiring to be Data Scientists, Big Data Analysts, Analytics Managers/ Professionals, Business Analysts, Data Analytics Developers.

**All certificate images are for illustrative purposes only. The actual certificate may be subject to change at the discretion of the Certification Body.
Alumni Speak
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"The course was well structured and our trainer was very thorough in the concepts. I liked the interview preparation and thanks to 360DigiTMG I got placed well.”

Pavan Satya

Senior Software Engineer

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"The faculty went to great depths to clarify our doubts. They gave us a lot of assignments and one very challenging live project. Great place to study.”

Venu Panjarla

AVP Technology

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"Data Science is an esoteric subject but this course made it so simple for me. I loved the course material and quizzes. The instructor was very helpful. You can learn a lot from this institute.”

Chetan Reddy

Data Scientist

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"The course material and infrastructure is sound. They keep a watch on us most of the time. They really helped me secure a job. I liked their placement assistance. Very sound institute.”

Santosh Kumar

Business Intelligence Analyst

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"Countless benefits of this course.Special Thanks to my mentors.It feels great to finally start working.”

Kadar Nagole

Data Scientist

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"Good ambience and a good team. They really guided me well from day one. I like my mentor very much. The course material is excellent.”

Gowtham R

Data Engineer

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"The trainers displayed good communication skills and made the sessions more interactive. This course has been really great.”

Wan Muhamad Taufik

Associate Data Scientist

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FAQs for Big Data Analytics Course in Malaysia

Data that is so large that it cannot be handled by traditional tools that are being used in the market.

Big Data professionals are the most sought after in the present world. They earn more than other software professionals. You can apply for roles that ask for knowledge and skills in Big Data tools and technologies. However, job titles may differ from company to company such as Big Data developer or Big Data analyst.

If you miss a class, we will arrange for a recording of the session. You can then access it through the online Learning Management System.

No. You need not pay separately for the certification.

You will be assigned a trainer who will mentor you and guide you subsequent to the training. The trainer will guide you personally and clarify all doubts.Our research associates will also be available to resolve your doubts.

Our faculty is our key strength. All our instructors are professionals with 10-15 years of experience in various domains. We handpick them for their subject matter expertise, level of experience, and passion and talent for training. All our trainers are recognised as among the best faculty in the industry.

Jobs in the field of big data in malaysia

Jobs in the field of Big Data and Analytics in Malaysia

As Malaysia is emerging with many industries, the need for Big Data Analysts is crucial. The main job roles in Malaysia are Data Analyst, Big Data Engineer, Machine learning engineer, Systems Architect, IT/Operations Manager, Business Analyst, and Data Scientist.

Salaries in malaysia for big data & analytics

Salaries for Big Data and Analytics In Malaysia

In Malaysia, the average salary for a Big Data Analyst is RM 86k. A fresher Big Data Analyst can expect a pay of RM 4k - RM 51k. The senior Data Analyst can expect an average salary of RM 61,740.

big data & analytics Projects in malaysia

Big Data and Analytics Projects in Malaysia

Big Data Analytics Digital government Lab was launched in Malaysia in 2015. Big Data and Analytics usage are very vast. Many projects are in the ongoing process in the fields of Agriculture, Finance, Health care, Biodiversity, Logistics, Legal, Life Sciences, and many more.

Role of Open Source Tools in big data and analytics

Role of Open Source Tools in Big Data and Analytics/h2>

Big Data and Analytics is operated with Python, R, Hadoop, Spark, Linux, Apache Pig, Hive, VMware, Apache Hbase. Students will be exposed and well trained in the applications of these prime tools.

Modes of Training for big data & analytics

Modes of Training of Big Data and Analytics Course

The Big Data and Analytics professional certification course in Malaysia is designed to prepare students to be part of the workforce of the future. 360DigiTMG offers students the option of both classroom and online learning. We also support e-learning as part of our curriculum.

Industry Application of big data and analytics

Industry Applications of Big Data and Analytics

Big Data and analytics has great potential in industries like Health care, Transportation, Education, Life Sciences, Banks, Telecommunication, Financial Services. Big data applications play a vital role in solving key problems.

Companies That Trust Us

360digitMG offers customised corporate training programmes that suit the industry-specific needs of each company. Engage with us to design continuous learning programmes and skill development roadmaps for your employees. Together, let’s create a future-ready workforce that will enhance the competitiveness of your business.

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