Professional Certificate Course in Big Data & Analytics
- 120 Hours Classroom & Online Sessions
- 6 Months Assignments & eLearning
- 100% Job Assistance
- Domain Specific Virtual Internship
- Certification from Technology Leader - IBM
- 100% HRD Corp Claimable!
Academic Partners & International Accreditations
"The Big Data and Business Analytics market are projected to reach $27 billion (about SG $ 37 billion) by 2022.". 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
- Computer Skills
- Basic Math Knowledge
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. You will also get to discover the various applications of DBMS and how it makes it easier to retrieve, manipulate, and produce information when required. The student will also learn the differences between SQL and no SQL database systems. Students will also explore the basics of Python statements along with Python objects and Data structure. 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.
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. They will also learn how to work with database management systems to manage data efficiently and perform multiple tasks with ease. 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. Students will also learn to use Python for statistical analysis and grab the fundamentals of Python from syntax to modules. Will learn about Machine learning approaches for business decisions, and get equipped with knowledge on the application of various Big data and analytics.
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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. The modules begin with an introduction to Database Management System that is used to store huge data in their databases, sort data into databases and pull out data from the databases, and manipulate data using SQL. 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. The concluding module takes into the popular programming language, Python and you will learn to construct basic Python programs with expressions, statements, variables, blocks, functions, comments, logic, and conditionals, which are vital concepts in computer programming. By the completion, of course, students will be well prepared for joining in big companies and face the challenges.
You will learn the concepts and design of a Database Management System. How organizations store huge data in their databases, organize data into databases, extract the data, manipulate the data using query language SQL. You will also explore the difference between SQL and NoSQL databases.
- MySQL for Structured Database
- NoSQL Database MongoDB
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.
You will learn about the various components of programming and how to construct basic Python programs with expressions, statements, variables, blocks, functions, comments, logic, and conditionals, which are vital concepts in computer programming. These modules will teach you the fundamentals of data structures and you will learn how to create algorithms and how to test and debug Python code.
Big Data Analytics Trends in Malaysia
The future is all about the innovation and evolution of Big Data. In the applications of Machine learning and Artificial Intelligence, many automated tools will evolve and will be used effectively for production. It is used for improved speech processing with the help of Natural language processing (NLP). For forecasting Earthquakes and other natural disasters, organizations will depend on Robotic Process Automation(RPA) along with AI and Machine learning to furnish reliable predictions. Cloud computing will be on higher stride. Many enterprises are shifting to multi-cloud computing methodology for data optimization. Digital transformation will be a crucial component of the main data strategies. Numerous businesses are moving from traditional marketing to digital marketing. The benefits of digital transformation are specific to a particular business.
It is an investment and acts as a pivot in generating new business models to create a competitive advantage. No company will run without an ERP or CRM system, the reports and the decisions will be based on data analytics only. Spark and Databricks are the new and evolving trends where many companies are looking forward to taking up. They are gaining much traction as they are user friendly and have better interactive processing capabilities. The dependency on Big Data and Analytics will grow in research on climate change. Many research institutes for Geosciences, Meteorology, etc are using inputs from big data analytics for solutions. Another industry that gains more and effective benefits from Big data analytics is the Petroleum industry. Big data analytics will evaluate huge amounts of data extracted from seismic sensors. The real-time analysis will gain significant momentum in the coming years.
Start your course at RM 550
Full Course Fee RM 6600 (excl 6%SST)
0% Interest Free Installments options available.
How We Prepare You
Additional Assignments of over 100 hours
Live Free Webinars
Resume and LinkedIn Review Sessions
6 Months Access to LMS
Job Assistance in Big Data & Analytics Fields
Unlimited Mock Interview and Quiz Session
Hands-on Experience in a Live Project
Life Time Free Access to Industry Webinars
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