Data Science Certification Programme Overview
The Certification Programme in Data Science Course is one of the most comprehensive Data Science course in Malaysia and the ASEAN region. It is specially designed to suit both data professionals and beginners who want to make a career in this fast-growing profession. In three months, students will learn key techniques such as Statistical Analysis, Regression Analysis, Data Mining, Machine Learning, Forecasting and Text Mining, and tools such as Python and R Programming. Understand the key concepts of Neural Networks and study Deep Learning Black Box techniques like SVM.
Data Science Course Learning Outcomes
Data Science Course 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.
Phillipines will need 340,880 data scientists by 2022, and Malaysia 20,000.
Block Your Time
Who Should Sign Up?
- IT Engineers
- Data and Analytics Manager
- Business Analysts
- Data Engineers
- Banking and Finance Analysts
- Marketing Managers
- Supply Chain Professionals
- HR Managers
- Math, Science and Commerce Graduates
Data Analysis courses, Data Science course and Big Data Analytics courses in Malaysia are in extremely high demand. 360DigiTMG Solutions has become the go-to place for Data Science certification training in Malaysia because of the choice of a wide variety of data analytics courses that are offered. Become a certified data science specialist in Malaysia by learning a plethora of data mining concepts. Alongside these advantages, Python and R programming will help by adding value to this certification not only in Malaysia but also across the globe. 360DigiTMG also offers Big Data analytics courses in Malaysia and has a unique position in the space of Big Data training in Malaysia.
Malaysia, a scenic country with its hills, beaches, forests and it's wildlife biodiversity is part of south-east Asia, consisting of three federal territories & 13 states. With its abundant natural resources in Petroleum, Palm oil, Rubber, and Spices, etc. Malaysia is an economic powerhouse with a future-looking populace. Malaysia offers a host of quality technical graduate & postgraduate programs. With the growing advent of Data Science in many counties, Malaysia has been one of the fast adopters of the same. Many organizations in Kuala Lumpur (Capital of Malaysia) are heavily investing in Data Science and are scouting for talent across various levels to drive this change. Malaysia is a place to keenly watch out for given that it offers both the worlds of technical excellence and varied touristy locations.
Register for a free orientation
Data Scientist Course Panel of Coaches
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
Sharat Chandra Kumar
- Areas of expertise: Data sciences, 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
- Areas of expertise: Data sciences, 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 at 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
Earn a certificate and demonstrate your commitment to the profession. Use it to distinguish yourself in the job market, get recognised at the workplace and boost your confidence. The Data Science Certificate is your passport to an accelerated career path.
FAQs for Data Science Bootcamp
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.
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.
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.
Heng Nguan Ting8 months ago
A company that give course from beginning level to advanced level. They will always keep in touch with their participant in order to get know about them and solve their problem accordingly. Nice place to start your learning.
Puteri ameena9 months ago
I joined the Data Science using R workshop and I really appreciated all the efforts that have been put into sharing the knowledge of Data Science. I learnt the reality of handling data unlike the theoretical classes we normally learn in university. I had so much fun too!! Thank you
Rong An Kiew9 months ago
I took part in the Jumpstart program 2018, I gained a lot of knowledge about Big Data from this program and there are also some experienced tutors teaching in this program. It provides some assignments to let us practise. Overall it is a good platform for learning Big Data.