Professional Certificate Course in
- 72 Hours Classroom & Online Sessions
- 140+ Hours Assignments & eLearning
- 100% Job Assistance
- 2 Capstone Projects
- Industry Placement Training
- Computer Skills
- Basic Mathematical Concepts
- Analytical Mindset
"The Business Analytics market in Malaysia and Singapore is projected to reach $37.5 billion by 2022. Each year the data industry is said to contribute around S$1 billion to Malaysia’s economy." - (Source). Malaysia is emerging as one of the best countries for nurturing talent and creating opportunities in the field of Data Science. By 2020, Big Data and Data Analytics global revenues will cross by 205$bn. Big Data will play a vital role in generating benefits and productivity in Malaysia, which was discussed by Karl Ng, data economy director of MDEC (Malaysian Digital Economy Corporation) on many occasions. Many programs were launched by MDEC to pool the talent for potential Data Scientists and to increase the number from 150 to 1500 by the end of 2020. The vision of MDEC is to place Malaysia superior in Knowledge and productivity. SAP and other academia have collaborated to increase the Business Analytics professionals through programs such as the ASEAN Data Science Explorers (ASEANDSE). The prime focus of ASEANDSE is to adopt Data analytics to solve problems that are disturbing the environment and people. By this, we can conclude that there is a bright and blossoming career for Business Analytics in Malaysia.
Business Analytics Certification Course Overview
The Business Analytics certification course in Malaysia is designed for students and professionals. This course is a blend of theory and applications of statistical tools. Become a Business Intelligence and Data Visualisation expert and surge ahead in your career. The nine-day Business Analytics certification course covers all the essential Analytical and Statistical techniques for effective business decision making. In this program technical languages Python, R, R studio, and Tableau are explained with use cases. This course helps students to enable them to perform well and makes them proficient in leveraging data and to predict future events related to market trends and design models for organizations to make data-driven and effective decisions.
Business Analytics is the application of statistical tools for the process of extracting, categorizing, optimizing, and analyzing data. Segregated data is used to bring meaningful business insights. The main purpose of Business Analytics is to differentiate which datasets are essential and how they could be efficiently used in escalating production and revenue.
Business Analytics Training Learning Outcomes
Many industries are adopting Business Analytics in their process to gain a competitive advantage and to be operationally efficient. By this Business Analytics certification program in Malaysia, students will learn to categorize the huge amounts of structured and unstructured data. Learn about the types of Business Analytics i.e Descriptive Analytics, Prescriptive Analytics, Predictive Analytics, and Diagnostic Analytics and applications of it. Students will be able to build models by using various statistical tools. Learn various components of business analytics like Data Mining, Forecasting, Text Mining, Data Aggregation, Optimization, and Data Visualization. Their applications are explained in detail with real-time projects. By the end of this course, students will be able to create business reports, dashboards, models, etc to draw valid business insights. The students will learn about R, Python, R studio, and Tableau which are very important and fundamental programming languages.
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Who Should Sign Up?
- Science, Math and Commerce graduates
- IT professionals who want to specialise in digital tech
- Professionals who want to move into Data Analytics
- Professionals who want to add Data Analytics to current job skills
- Academicians and researchers working in Data Analytics
Professional Business Analytics Course Modules
Business Analytics professionals are very much crucial for today’s business developments. By the modules of the Business Analytics course in Malaysia, students will learn about data preparation and data cleansing in data science projects to ensure that appropriate data is provided to the next step. 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. Understand about extracting features from structured as well as unstructured data such as videos, images, audio, textual files, etc. 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. Tableau is a Data Visualization tool that has been in the Leader’s Quadrant in Gartner’s Magic Quadrant for many years. Tableau Desktop is the authoring and publishing tool that is used to create and share views. In this module, you will get familiar with Tableau User Interface, different components, and terminologies in Tableau. You will learn different methods available to save and share the work created in Tableau and many more important topics will be covered in these modules.
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.
Data visualization refers to the graphical representation of data and the results of data analysis, to derive meaningful insights for better decision making. In this module, you will learn everything that is to be known about Data Visualization. You will be able to answer questions like: What is Data Visualization?, Why do we need Data Visualization?, How to create views?., etc. You will be introduced to Data Visualization principles and best practices.
Tableau is a Data Visualization tool that has been in the Leader’s Quadrant in Gartner’s Magic Quadrant for many years. Tableau Desktop is the authoring and publishing tool that is used to create and share views. In this module, you will get familiar with Tableau User Interface, different components, and terminologies in Tableau. You will learn different methods available to save and share the work created in Tableau. You will understand Tableau Architecture.
Visual Analytics is the science of generating charts, graphs, views, and maps. This module will help you create basic charts in Tableau like Text Tables, Highlight Tables, Bar Charts, Side by Side Bars, Stacked Bars, Line Charts, Area Charts, Circle Views, and Pie Charts. You will also learn about Exploratory data analysis using Tableau.
Customer data these days need not come from a single system, it could be split across multiple databases in multiple systems (heterogeneous systems). This module will help you master the data connectivity options available in Tableau which includes Single Database Joins, Multiple Database Joins, Unions, Data Extraction and Data Blending.
The scope of Data Visualization is seamless. This module will introduce you to Advanced and Custom charts in Tableau like Scatter Plots, Histograms, Box Plots, Pareto Charts, Animation Charts, etc. You will also learn about Statistical Analysis features of Tableau like Trend Analysis and Distribution Analysis.
Applying filters limits the amount of data to include specific fields and records that meet certain criteria. You will learn everything that is to be known about Filters in Tableau here. Dimension Filters, Measure Filters, Date Filters applied at different levels like Tableau Data source level, Extract level and Worksheet level. You will learn about Tableau Quick Filters and Interactive Filters.
Creating groups will help you put similar members together. Set is a collection of well-defined objects which can be created based on computations. Calculations will help you manipulate the data and transform the data into the desired format. LOD (Level of Detail) expressions will help perform calculations at a desired level of detail, different from the default used in Tableau, to meet the requirements given.
Anything and everything there is to know about Geographic Maps in Tableau is included in this Module. A Geo-Spatial analysis is a great way to represent geographic data, as it adds rich context to the data being represented through Maps. Tableau has its own Geocoding data which helps it handle Geoinformation beautifully. You will learn how to add context to the data that might not be geographical by representing it on Custom Background Images.
The Dashboard is a real-time, easy to read user interface that presents data graphically. It is a collection of views, filters, parameters, and images that work closely and are arranged on a single canvas. The story is a narrated walkthrough or a guided analytics platform where the business users can see their data and get answers to all their questions. You will learn how to create Dashboards, how to make them Interactive, adding objects to the dashboard, designing Dashboards for different devices and creating stories.
Business Analytics Trends in Malaysia
AI and predictive analytics will be always hot in the coming years too. Predictive Analytics data can be analyzed instantly. Data modeling, Deep learning algorithms, Data mining, and Machine learning are used for statistical analysis of data and extract data which is used for business to predict the trends and give appropriate solutions. Mobile BI is used to transfer information for road warriors. The use case is the mobilization of content from basic reports to dashboards. Highly applicable to the Transportation industry.
Natural language processing will notice many advancements and many industries are going to rely on it. Cloud computing is the recent buzzword, it involves huge amounts of data from structured and unstructured data and delivers data that is manageable and resourceful. It takes data from audio files, images video files Website, server text files, documents, etc. Visual tools help to create impactful presentations through Geographical maps, pivot tables. We can also observe there would be rapid development in the Internet of Things which would be part and parcel of our lifestyle. Along with IoT, Edge computing will become popular in the coming days. Cloud Computing will also play an essential role in business. This trend can be considered as a hybrid solution for business.
How We Prepare You
Additional Assignments of over 60-80 hours
Live Free Webinars
Resume and LinkedIn Review Sessions
3 Month Access to LMS
Job Assistance in Business 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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Professional Business Analytics Panel of Coaches
Bharani Kumar Depuru
- Areas of expertise: Data Analytics, Process Management, Quality Management.
- Over 13 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
- Business Analytics, Quality Management, Data Visualization with Tableau, COBOL, CICS, DB2 and JCL.
- Electronics and communications engineer with over 13 years of industry experience
- Senior Tableau developer, with experience in analytics solutions development in domains such as retail, clinical and manufacturing
- Trained over 750+ professionals across the globe in three years
- Worked with Infosys Technologies,
iGate, Patni Global Solutions as technology analyst
Dr Nitin Mishra
- Areas of expertise: Data Sciences, Machine Learning, Business Intelligence and Data Visualization.
- Over 15 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)
Gain industry recognition for niche skills with the Professional Certification in Business Analytics. The certificate illustrates your ability to extract actionable insights from business data for improved decision - making.
FAQs for Business Analytics in Malaysia
Different organisations use different terms for Data Professionals. You will sometimes find these terms being used interchangeably. Though there are no hard rules that distinguish one from another, you should get the role descriptions clarified before you join an organisation.
With growing demand, there is a scarcity of Data Science/Business Analytics professionals in the market. If you can demonstrate strong knowledge of Data Science concepts and algorithms, then there is a high chance for you to be able to make a career in this profession.
360DigiTMG provides internship opportunities through Innodatatics, our USA-based consulting partner, for deserving participants to help them gain real-life experience. This greatly helps students to bridge the gap between theory and practical.
There are plenty of jobs available for Data Professionals. Once you complete the training, assignments and the live projects, we will send your resume to the organisations with whom we have formal agreements on job placements.
We also conduct webinars to help you with your resume and job interviews. We cover all aspects of post-training activities that are required to get a successful placement.
While there are a number of roles pertaining to Data Professionals, most of the responsibilities overlap. However, the following are some basic job descriptions for each of these roles.
As a Data Analyst, you will be dealing with Data Cleansing, Exploratory Data Analysis and Data Visualisation, among other functions. The functions pertain more to the use and analysis of historical data for understanding the current state.
As a Data Scientist, you will be building algorithms to solve business problems using statistical tools such as Python, R, SAS, STATA, Matlab, Minitab, KNIME, Weka etc. A Data Scientist also performs predictive modelling to facilitate proactive decision-making.
A data engineer primarily does programming using Spark, Python, R etc. It often compliments the role of a Data Scientist.
A Data Architect has a much broader role that involves establishing the hardware and software infrastructure needed for an organisation to perform Data Analysis. They help in selecting the right Database, Servers, Network Architecture, GPUs, Cores, Memory, Hard disk etc.
After you have completed the classroom sessions, you will receive assignments through the online Learning Management System that you can access at your convenience. You will need to complete the assignments in order to obtain your Data Scientist certificate.
In this blended programme, you will be attending 48 hours of classroom sessions over six days on campus in Kuala Lumpur, Malaysia. After completion, you will have access to the online Learning Management System for another three months for recorded videos and assignments. The total duration of assignments to be completed online is 40-60 hours. Besides this, you will be working on a live project for a month.
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.
We assign mentors to each student in this programme. Additionally, during the mentorship session, if the mentor feels that you require additional assistance, you may be referred to another mentor or trainer.
No, the cost of the certificate is included in the programme package.
Jobs in the Field of Business Analytics in Malaysia
Business Analysts have a promising career. The job roles for Business Analyst professionals in Malaysia are Business Analyst, Senior Business Analyst, Quantitative Analyst, Market Research Analyst, Operations Business Analyst, Management Analyst, Business process engineer, and so on.
Salaries for Business Analytics In Malaysia
The Business Analyst average salary in Malaysia is RM 59,452. The salary range for entry-level is RM 14k - RM 65k for middle-level RM 44k - RM 129k and the average salary for experienced Business Analyst is RM 101,736. This varies as per the job roles.
Business Analytics Projects in Malaysia
In many streams like Real estate, Retail, Manufacturing, Environment monitoring, Automobiles, Life insurance, Banking, Designing forecasting tools, etc projects on Business Analytics are in the process.
Role of Open Source Tools in Analytics
Programming tools like R and Python are considered to be the most essential tools for learning Business Analytics, Data Science, Data Analytics. In this course, you will be exposed to the basics and applications of R, Python, Tableau, and R studio.
Modes of Training of Business Analytics Certification Course
360DigiTMG offers students the option of both classroom and online learning. We also support e-learning as part of our curriculum. Individual attention is guaranteed to all the participants. The Business Analytics certification course in Malaysia is designed based on the requirements of students and working professionals.
Industry Applications of Business Analytics in Malaysia
Prime sectors like Education, Banking, Manufacturing, Health care, Retail, Airlines, Construction, Pharmaceutical, and Real estate will mainly depend upon Business Analytics to make data-driven decisions that generate revenue.
360DigiTMG - Data Science, IR 4.0, AI, Machine Learning Training in Malaysia
Level 16, 1 Sentral, Jalan Stesen Sentral 5, Kuala Lumpur Sentral, 50470 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia
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