Professional Certificate Course in
Data Science & AI
- 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
Data Science & AI Programme Overview
Learn how to harness the power of data for smart business decision-making with the Professional Certificate in Data Science and AI. The nine-day Data Science training course in Malaysia is designed for both beginners and professionals who want to build a career in Data Science. Participants will develop a strong foundation in Data Science, AI and Deep Learning using Python and R. Students will dive into the architecture of the Relational database and learn to store and manipulate data using SQL and will also discover the difference between SQL and NoSQL databases. Potential techniques such as Statistical Analysis, Regression Analysis, Data Mining Unsupervised, Machine Learning, and Forecasting are trained with real-time projects. Students will get exposure to all the advanced Data Science tools such as Python, Tensorflow, Keras, OpenCV, and R. A module is dedicated to Python programming that will teach the fundamentals of data structures and you will learn how to create algorithms and how to test and debug Python code. With a dedicated team of trainers and personalized mentorship, the students will gain adequate knowledge and will be able to deliver outstanding results.
Data Science and AI
Data Science is related to analyzing, processing, and maintaining data sets. It aims at data modeling and data warehousing to trace the uncontrollably growing data set reaching the organizational goals. Artificial intelligence is the emerging technology used in machines to execute at reasoning by cloning human intelligence. With the aid of Deep learning and Natural language processing, AI technologists enable machines in identifying inferences and patterns. Artificial Intelligence automation has become easy and the development of Intelligent products is possible. Our Data Science and Artificial Intelligence certification program will help you in obtaining extensive knowledge and prepares you to be settled in high paid jobs.
Data Science & AI Learning Outcomes
By this Professional Certificate course in Data Science and Artificial Intelligence in Malaysia, students/ professionals work with tools and advanced techniques used for the analysis of structured and unstructured data. Participants will learn concepts of 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. Students will also discover the difference between SQL and NoSQL databases along with the various concepts involved. Will be able to perform text mining to generate customer sentiment analysis. Perform forecasting to take proactive business decisions. There is enormous scope for a lucrative career in this domain. By using the cutting edge and appropriate tools like Python, R, Keras the freshers and professionals will be able to build algorithms and analyze huge data. Students will also learn about the various components of Python 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. By using the opportunity of individual attention given by experts at 360DigiTMG, the students will be adequately trained and will be able to understand the course very effectively. Students will be exposed to real-time projects, at the learning level only they are prepared to face the challenges that are inclined to be in industries. Data Science and AI are not confined to a specific industry, so the professionals in data science and Artificial Intelligence will have the liberty to work in the areas of their interest. The primary objective of Data Science and Artificial training at 360DigiTMG is to deliver skilled professionals by providing quality training, guiding them to implement and gain hands-on experience.
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Who Should Sign Up?
- Those aspiring to be Data Scientists, AI experts, Business Analysts, Data Analytics developers
- Graduates looking for a career in Data Science, Machine Learning, Forecasting, AI
- Professionals migrating to Data Science
- Academicians and Researchers
- Students entering the IT industry
Data Science and AI Training Modules
By the professional certification course in Data Science and AI in Malaysia, students will learn important tools like Python, Keras, Tensor flow, R, and many more. The modules begin with an introduction to a database management system where they will explore the various applications of DBMS and how this technology is being leveraged to store and retrieve data with utmost efficiency. Learn about the Linear regression, Logistic regression, Naive Bayes, Decision Trees, Support Vector systems, and so on. Will Understand the evolution of AI and Deep Learning and learn the various applications of Deep Learning in building Artificial Intelligence applications. Challenges faced in deep learning along with the best practices to overcome the challenges is also explained in detail. Understand the various network architectures along with different layers including input layers, hidden layers, output layers, etc. Also learn about the various activation functions, error functions, optimization algorithms including Batch Gradient Descent, Stochastic Gradient Descent, Mini-batch SGD, etc. Students will 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. 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. Students will gain knowledge about the various regularisation techniques and will be able to evaluate overfitting (Variance)and Underfitting(bias). The concluding module will teach you coding with the help of Python programming its use to create web applications. And many effective and significant modules are covered to generate workforce to the changing business trends. All these are explained using industry relevant use cases and mini-projects.
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
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.
Understand the evolution of AI and Deep Learning and learn the various applications of Deep Learning in building Artificial Intelligence applications. A brief history of Deep Learning and the pace of progress in the space of deep learning is pivotal for budding and emerging data scientists as well as AI experts. Challenges faced in deep learning along with the best practices to overcome the challenges is also explained in detail.
While there are a lot of statistical software and programming languages to perform deep learning activities, Python stands out from the rest. There are a lot of deep learning libraries such as Keras, TensorFlow, Theano, PyTorch, etc., and one will learn about Keras as well as TensorFlow as part of the training module. Image processing is an amazing field to become proficient at and hence you will also learn OpenCV, which stands for Open Computer Vision. The future belongs to Open-source libraries and the fastest development on emerging algorithms will happen in this space. Learning these concepts will help us gain an edge over competitors.
Understanding the treatment of both linearly separable boundaries as well as non-linear boundaries is pivotal for the success of AI experts as well as Data Scientists. In this module, one will learn about handling linear boundaries using the Perceptron algorithm. Understand how weights are assigned and how they are updated each time to reduce the error function. Learn about the Backpropagation algorithm and its application in reducing error using the Perceptron algorithm.
Artificial Neural Network, also called MLP or Multilayer Perceptron is used to handle nonlinear problems. Understand the various network architectures along with different layers including input layers, hidden layers, output layers, etc. Also learn about the various activation functions, error functions, optimization algorithms including Batch Gradient Descent, Stochastic Gradient Descent, Mini-batch SGD, etc.
Understand working with videos and images because the amount of data getting generated in this space is outstripping the volume of textual data. Understand the various features to be extracted from images including edges, textures, etc., by applying various kinds of filters such as Sobel, Harris Corner Detector. Also, learn about face detection using Viola-Jones and tracking human faces in videos. Alongside this also learn about a few image-related models such as image segmentation, image recognition, etc.
Understand how to work with images and videos for building predictive models. Learn about convolution layers as well as handling very small datasets. Understand how to improve the accuracy of models by performing data augmentation activities. Also one should be aware of the use of pre-trained models using feature extraction, fine-tuning, etc., in solving business problems. Finally visualizing the activation layers and heat maps for activation will complete the study to the fullest.
Understand working with textual sequence data and how to perform a one-hot encoding of words and characters. Learn about bi-directional RNNs as well as deep bi-directional RNNs. Learn about various RNN topologies and network architectures. Vanishing and exploding gradient problems are very prevalent in the field of recurrent neural networks. Understand Backpropagation Through Time, which is a different but slight variation from the regular backpropagation algorithm.
Advanced techniques in handling textual and sequential data are LSTMs and GRUs. Also, understand about forecasting temperature. Learn about bi-directional LSTMs and deep bi-directional LSTMs . Also, understand the stacking of various recurrent layers. Stacking recurrent layers will improve accuracy and understanding the same is extremely pivotal for the success of AI algorithms. Also, learn about 1D convolution for time series data. Finally combining CNN and RNN models is an art, which is explained in detail.
Data Science and AI Trends in Malaysia
Data Science with AI is an emerging technology where its benefits are immense in Malaysia. The latest trends in Data Science is the invention of energy-efficient technique with the help of flower pollination algorithm for Cloud Datacentres. Using Data mining techniques are being used in healthcare to predict the risk of disease based on clinical and non-clinical data of individuals. For virtual screening, Deep networks are being used. Artificial Intelligence is evolving from Perpetual intelligence to Cognitive intelligence, this will enable machines in better understanding and utilizing knowledge for delivering better outputs. Advancements in Artificial Intelligence will be used in protecting Data.
Technologies in IoT will be essential for manufacturing companies enabling them to accomplish machine automation and getting introduced to smart manufacturing. Cloud computing is the technology that is being lauded and gaining accolades for its friendly and easy features. It is easily accessible to the users as it is the core of the digital economy. Cloud is being used in all IT technologies like chips, databases, IoT, Blockchain, Quantum computing, and many more. The other trends will be observed in Patent Analytics, market sizing tools, and in Earning Transcripts. There would be a great shift towards real-time analytics, which helps companies to be more productive by making data-driven decisions.
Start your course at RM 566.66
Full Course Fee RM 6800 (excl 6%SST)
0% Interest Free Installments options available.
How We Prepare You
Additional Assignments of over 140+ hours
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6 Months Access to LMS
Job Assistance in RPA 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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