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Best Data Science Course Training in Philippines

Fast-track your career with the Certificate Course in Data Science Training in Philippines.
  • 48 Hours Classroom & Online Sessions
  • 80+ Hours Assignments & eLearning
  • 100% Job Assistance
  • 2 Capstone Projects
  • Blockchain enabled tamper-proof security certificate
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"The Philippines Data Science Market will be worth 6 million dollars in 2025 and the Data Analytics Outsourcing market in Philippines is worth $26 Billion", Philippines will undoubtedly witness around three lakh job openings in Data Science by 2021.  Philippines is second to the United States in terms of the number of job openings in Data Science. In 2019, 97,000 positions in data science and analytics were vacant due to the lack of qualified candidates. The top sectors creating the most Data Science jobs are BFSI, Energy, Pharmaceutical, HealthCare, E-commerce, Media, and Retail. Today large companies, medium-sized companies and even startups are willing to hire data scientists in Philippines. The five most sought after digital skills are Big Data, Software and User Testing, Mobile Development, Cloud Computing, and Software Engineering Management.

Data Science

data science course duration in Philippines

Total Duration

3 Months

pre-requisites - data science course duration in Philippines


  • Computer Skills
  • Basic Mathematical Concepts
  • Analytical Mindset

Data Science Training in Philippines

360DigiTMG has introduced the most comprehensive Data Science course in Philippines. The various stages of the Data Science Lifecycle are explored in the trajectory of this Data Science program. This Data Science training in Philippines begins with an introduction to Statistics, Probability, Python, and R programming. The student will then conceptualize Data Preparation, Data Cleansing, Exploratory Data Analysis, and Data Mining (Supervised and Unsupervised). Comprehend the theory behind Feature Engineering, Feature Extraction, and Feature Selection. Participants will also learn to perform Data Mining (Supervised) with Linear Regression and Predictive Modeling with Multiple Linear Regression Techniques. Data Mining Unsupervised using Clustering, Dimension Reduction, and Association Rules are 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. All the stages delineated in the CRISP-DMM framework for a Data Science Project are dealt with in great depth and clarity in this course. Undoubtedly this emerges as one of the best Data Science in Philippines due to the live project exposure in INNODATATICS. This gives a golden opportunity for students to apply the various concepts studies to a real-time situation.

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 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. Having proficiency in tools such as Python, R, R Studio, Hadoop, MapReduce, Apache Spark, Apache Pig, Java, NoSQL database, Cloud Computing, Tableau, and SAS is beneficial, but not mandatory.

Data Science Course Outcomes in Philippines

In this data-driven environment 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 is one of the most comprehensive courses in the industry today. This course in Philippines is specially designed to suit both data professionals and beginners who want to make a career in this fast-growing profession. This training will equip the students with logical and relevant programming abilities to build database models. They will be able to create simple machine learning algorithms like K-Means Clustering, Decision Trees, and Random Forest to solve problems and communicate the solutions effectively. In three months, students will also explore the key techniques such as Statistical Analysis, Regression Analysis, Data Mining, Machine Learning, Forecasting and Text Mining, and scripting algorithms for the same with Python and R programming. Understand the key concepts of Neural Networks and study Deep Learning Black Box techniques like SVM.

Work with various data generation sources
Perform Text Mining to generate Customer Sentiment Analysis
Analyse structured and unstructured data using different tools and techniques
Develop an understanding of Descriptive and Predictive Analytics
Apply Data-driven, Machine Learning approaches for business decisions
Build models for day-to-day applicability
Perform Forecasting to take proactive business decisions
Use Data Concepts to represent data for easy understanding

Block Your Time

data science course - 360digitmg

48 hours

Classroom & Online Sessions

data science course - 360digitmg

80+ hours

Assignments & eLearning

data science course duration - 360digitmg


Capstone Projects

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 Course Modules in Philippines

This program follows the CRISP-DM Methodology. The premier modules are devoted to a foundational perspective of Statistics, Mathematics, Business Intelligence, and Exploratory Data Analysis. The successive modules deal with Probability Distribution, Hypothesis Testing, Data Mining Supervised, Predictive Modelling - Multiple Linear Regression, Lasso And Ridge Regression, Logistic Regression, Multinomial Regression, and Ordinal Regression. Later modules deal with Data Mining Unsupervised Learning, Recommendation Engines, Network Analytics, Machine Learning, Decision Tree and Random Forest, Text Mining, and Natural Language Processing. The final modules deal with Machine Learning - classifier techniques, Perceptron, Multilayer Perceptron, Neural Networks, Deep Learning Black-Box Techniques, SVM, Forecasting, and Time Series algorithms. This is the most enriching course in Australia  in terms of the array of topics covered.

Get about, CRISP - ML(Q) the perfect Project Management Methodology used for handling Data Mining projects. Understand the entire process flow including Business Problem definition, Data Collection, Data Cleansing, Feature Engineering, Feature Selection, Model Building, Deployment and Maintenance. Get introduced to the principles of big data and learn about the opportunities being created. Understand about how Data is generation and explosion of data, Innovations in the space of analytics. Learn how to distinguish between data types, Exploratory data analysis, the Various moments of Business decisions and various Graphical techniques. Learn about probability and probability distribution namely Z distribution and Student's t-distribution.

Learn about Hypothesis testing, the many Hypothesis testing Statistics, work with the Null Hypothesis & Alternative hypothesis and Types of hypothesis testing. Interpret the results of Hypothesis test and probabilities of Alpha error, understand Type I and Type II errors. Get introduced to Linear regression, various components of Linear regression viz regression line, Linear regression equation, the concept of Ordinary Least Square. Get introduced to Linear regression analysis, and Linear regression examples.

Understand the Linear regression in a multivariate scenario, understand collinearity and how to deal with it. Get introduced to the analysis of Attribute Data, understand the principles of Logistic regression, Binary Logistic regression analysis. Learn about the Multiple Logistic regression, Probability measures, and its interpretation. Get clarity on the confusion matrix and its elements. Get introduced to “Cut off value” estimation using AUC and ROC curve, understand False Positive Rate, False Negative Rate, Sensitivity, Specificity. Gain a birds-eye view to various advanced regression techniques and analysis of count data namely Poisson regression, Negative binomial regression. Learn when to use Poisson regression and negative binomial regression for predicting count data.

Learn about modeling using KNN, the K nearest neighbour algorithm using KNN algorithm examples. The KNN classifier is one of the most popular classifier algorithms. Decision tree & Random forest are one of the most powerful classifier algorithms today. Under this tutorial learn about Decision Tree analysis, Decision Tree examples and Random Forest algorithms. Also learn about the various ensemble machine learning algorithms. Text Mining or Text Data Mining are the most widely used analyzing tools for unstructured data. As part of the session, learn about Text analytics and the various text mining techniques in the text mining application, text mining algorithms, and sentiment analysis. Gain a ‘hands-on’ on how to extract data from Social Media, download user reviews from E-commerce sites and travel sites. Generate various visualizations using the downloaded data.

Under the Naïve Bayes classifier tutorial, learn how the classification modelling is done using Bayesian classification, understand the same using Naïve Bayes example. Learn about Naïve Bayes through the example of text mining. Artificial Neural Network and Support Vector Machines are the 2 powerful Deep learning algorithms. Get introduced to Perceptron Algorithms, Artificial Neural Networks, Multilayer Perceptron (MLP). Learn how to work with Support Vector Machine, SVM classifiers, and SVM regression. Get introduced to Association rules in data mining to decode the relationship between entities, understand how the Apriori algorithm works, and the association rule mining algorithm works.

Description: As part of data mining unsupervised, get introduced to various clustering algorithms, learn about Hierarchical clustering, K-means clustering using clustering examples, know what clustering machine learning is all about. Learn about K-means Clustering, Clustering ratio, and various clustering metrics. Get introduced to methods of making optimum clusters. Learn the need for data reduction in data mining using dimensionality reduction techniques. Learn about the advantages of dimensionality reduction using PCA. Get introduced to the difference between cross-sectional data and Time-series data. Various stages of forecasting projects, components of Time-series, visualization techniques, model-based techniques and learning how to evaluate the forecasting models accuracy.

How we prepare you
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    Additional Assignments of over 150+ hours
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    Live Free Webinars
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    Resume and LinkedIn Review Sessions
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    Lifetime LMS Access
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    24/7 support
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    Job Assistance in Data Science fields
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    Complimentary Courses
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    Unlimited Mock Interview and Quiz Session
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    Hands-on experience in a live project
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