Certificate Course in
Data Science Certification Course in Penang
- 48 Hours Classroom & Online Sessions
- 80+ Hours Assignments & eLearning
- 100% Job Assurance
- 2 Capstone Projects
- Industry Placement Training
- HRDF SBL-KHAS Claimable!
Academic Partners & International Accreditations
Data Science Certification Programme Overview
This three-month Data Science certification course with Python and R programming will empower students with skills in Data Analytics, Data Mining, Machine Learning, Predictive Modelling, and Regression Analysis. In this Data Science course based in Penang, they will learn to unleash the power of Python and R to create machine learning and neural network algorithms. This Data Science training course aims to serve the learning needs of IT professionals and college students gearing to make the transition to data science. The student will appreciate descriptive and predictive analytics and learn to analyze structured and unstructured data with various tools and techniques like Data Preparation, Data Cleansing, Exploratory Data Analysis, Feature Engineering, Feature Extraction, Feature Selection, and Text Mining. At the end of this best Data Science course in Malaysia, they will build prediction models for daily operability. They can also master Black box techniques, Neural Network programming, and Data Visualization methods. Propel yourselves to great heights in your Data Science career with the aid of this program from the best Data Science training institute from Penang.
What is Data Science? Data science is an amalgam of methods derived from statistics, data analysis, and machine learning that are trained to extract and analyze huge volumes of structured and unstructured data.
Who is a Data Scientist? A Data Scientist is a researcher who has to prepare huge volumes of big data for analysis, build complex quantitative algorithms to organize and synthesize the information, and present the findings with compelling visualizations to senior management.
A Data Scientist enhances business decision making by introducing greater speed and better direction to the entire process.
A Data Scientist must be a person who loves playing with numbers and figures. A strong analytical mindset coupled with strong industrial knowledge is the skill set most desired in a Data Scientist. He must possess above the average communication skills and must be adept in communicating the technical concepts to non - technical people.
Data Scientists need a strong foundation in Statistics, Mathematics, Linear Algebra, Computer Programming, Data Warehousing, Mining, and modeling to build winning algorithms.
They must be proficient in tools such as Python, R, R Studio, Hadoop, MapReduce, Apache Spark, Apache Pig, Java, NoSQL database, Cloud Computing, Tableau, and SAS.
Data Science Course Outcomes in Penang
In this data-driven environment a certification in Data Science prepares you for the surging demand of Big Data skills and technology in all the leading industries.There is a huge career prospect available in the field of data science and this Data Science Certification Programme is one of the most comprehensive Data Science courses in the industry today.This training will equip the students with logical and relevant programming abilities to build database models. The students will explore the various stages of the Data Science Lifecycle in the trajectory of this Data Science course. Initially conceptualize Data preparation, Data Cleansing, Exploratory Data Analysis, and Data Mining( Supervised and Unsupervised). Progressively learn the theory behind Feature Engineering, Feature Extraction, and Feature Selection. Perform Predictive modeling using regression analysis. Build machine learning, Deep Learning, and Neural Network algorithms with Python and R language. Apprehend forecasting to take proactive business decisions. Script algorithms for neural networks, time series analysis and forecasting in this Data Science Course in Penang.
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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
Data Science Training Modules in Penang
This Data Science course begins with an introduction to Statistics, Probability, Python and R programming, And Exploratory Data Analysis. Participants learn to perform Data Mining Supervised with Linear regression and Predictive Modelling with Multiple Linear Regression techniques. Data Mining Unsupervised using Clustering, dimension reduction, and association rules is also dealt with in detail. A module is dedicated to scripting machine learning algorithms and enabling Deep Learning and Neural Networks with Black Box techniques and SVM. Learn to perform proactive forecasting and time series analysis with algorithms scripted in Python and R.
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 & 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 & 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 Science Trends in Penang
Data Analytics courses, Data Science courses, and Big Data Analytics courses in Penang are in great demand. 360DigiTMG Malaysia has become the preferred destination for Data Science Certification Training in Malaysia because of the wide spectrum of courses offered. Become a certified data science specialist in Malaysia by learning a plethora of Data Mining concepts at 360DigiTMG. The additional modules in Python and R programming improve employability in Penang and overseas markets. 360DigiTMG also offers Big Data Analytics courses in Penang and has a unique position in the Big Data Analytics training vertical.
Penang is a vibrant Malaysia state located on the north-west coast of Malaysian Peninsula. Penang is recognized worldwide by the sobriquet "Silicon Valley of the East", for its electronics industries. It is one of Malaysia's economic powerhouses. Penang has the highest GDP per capita among Malaysian states. Penang's economic growth has been fuelled by the electronics, engineering, services, tourism and oil refining industries. It is also the jewellery hub of Malaysia. Penang has also opened itself to Data Science and Data Analytics sector. There is a good demand for Data Science professionals in the city. Most Malaysian students are opting for a career in Data Science as it is highly lucrative. A certification program in Data Science at 360DigiTMG Penang Malaysia is a passport to a dream career full of exalted milestones.
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