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*Offer valid till 31st October 2026
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MODULE 1 : ARTIFICIAL INTELLIGENCE OVERVIEW
• Evolution Of Human Intelligence
• What Is Artificial Intelligence?
• History Of Artificial Intelligence
• Why Artificial Intelligence Now?
• Areas Of Artificial Intelligence
• AI Vs Data Science Vs Machine Learning
MODULE 2 : DEEP LEARNING INTRODUCTION
• Deep Neural Network
• Machine Learning vs Deep Learning
• Feature Learning in Deep Networks
• Applications of Deep Learning Networks
MODULE3 : TENSORFLOW FOUNDATION
• TensorFlow Structure and Modules
• Hands-On:ML modeling with TensorFlow
MODULE 4 : COMPUTER VISION INTRODUCTION
• Image Basics
• Convolution Neural Network (CNN)
• Image Classification with CNN
• Hands-On: Cat vs Dogs Classification with CNN Network
MODULE 5 : NATURAL LANGUAGE PROCESSING (NLP)
• NLP Introduction
• Bag of Words Models
• Word Embedding
• Hands-On:BERT Algorithm
MODULE 6 : AI ETHICAL ISSUES AND CONCERNS
• Issues And Concerns Around Ai
• Ai And Ethical Concerns
• Ai And Bias
• Ai:Ethics, Bias, And Trust
MODULE 1 : PYTHON BASICS
• Introduction of python
• Installation of Python and IDE
• Python Variables
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
MODULE 2 : PYTHON CONTROL STATEMENTS
• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements
MODULE 3 : PYTHON DATA STRUCTURES
• Basic data structure in python
• Basics of List
• List: Object, methods
• Tuple: Object, methods
• Sets: Object, methods
• Dictionary: Object, methods
MODULE 4 : PYTHON FUNCTIONS
• Functions basics
• Function Parameter passing
• Lambda functions
• Map, reduce, filter functions
MODULE 1 : OVERVIEW OF STATISTICS
• Introduction to Statistics
• Descriptive And Inferential Statistics
• Basic Terms Of Statistics
• Types Of Data
MODULE 2 : HARNESSING DATA
• Random Sampling
• Sampling With Replacement And Without Replacement
• Cochran's Minimum Sample Size
• Types of Sampling
• Simple Random Sampling
• Stratified Random Sampling
• Cluster Random Sampling
• Systematic Random Sampling
• Multi stage Sampling
• Sampling Error
• Methods Of Collecting Data
MODULE 3 : EXPLORATORY DATA ANALYSIS
• Exploratory Data Analysis Introduction
• Measures Of Central Tendencies: Mean,Median And Mode
• Measures Of Central Tendencies: Range, Variance And Standard Deviation
• Data Distribution Plot: Histogram
• Normal Distribution & Properties
• Z Value / Standard Value
• Empherical Rule and Outliers
• Central Limit Theorem
• Normality Testing
• Skewness & Kurtosis
• Measures Of Distance: Euclidean, Manhattan And Minkowski Distance
• Covariance & Correlation
MODULE 4 : HYPOTHESIS TESTING
• Hypothesis Testing Introduction
• P- Value, Critical Region
• Types of Hypothesis Testing
• Hypothesis Testing Errors : Type I And Type II
• Two Sample Independent T-test
• Two Sample Relation T-test
• One Way Anova Test
• Application of Hypothesis testing
MODULE 1: MACHINE LEARNING INTRODUCTION
• What Is ML? ML Vs AI
• Clustering, Classification And Regression
• Supervised Vs Unsupervised
MODULE 2: PYTHON NUMPY PACKAGE
• Introduction to Numpy Package
• Array as Data Structure
• Core Numpy functions
• Matrix Operations, Broadcasting in Arrays
MODULE 3: PYTHON PANDAS PACKAGE
• Introduction to Pandas package
• Series in Pandas
• Data Frame in Pandas
• File Reading in Pandas
• Data munging with Pandas
MODULE 4: VISUALIZATION WITH PYTHON - Matplotlib
• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter
MODULE 5: PYTHON VISUALIZATION PACKAGE - SEABORN
• Seaborn: Basic Plot
• Advanced Python Data Visualizations
MODULE 6: ML ALGO: LINEAR REGRESSION
• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Modeling and Evaluation in Python
MODULE 7: ML ALGO: LOGISTIC REGRESSION
• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Modeling and Evaluation in Python
MODULE 8: ML ALGO: K MEANS CLUSTERING
• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Modeling in Python
MODULE 9: ML ALGO: KNN
• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Modeling and Evaluation in Python
MODULE 1: FEATURE ENGINEERING
• Introduction to Feature Engineering
• Feature Engineering Techniques: Encoding, Scaling, Data Transformation
• Handling Missing values, handling outliers
• Creation of Pipeline
• Use case for feature engineering
MODULE 2: ML ALGO: SUPPORT VECTOR MACHINE (SVM)
• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python
MODULE 3: PRINCIPAL COMPONENT ANALYSIS (PCA)
• Building Blocks Of PCA
• How it works: Finding Principal Components
• Modeling PCA in Python
MODULE 4: ML ALGO: DECISION TREE
• Introduction to Decision Tree & Random Forest
• How it works
• Modeling and Evaluation in Python
MODULE 5: ENSEMBLE TECHNIQUES - BAGGING
• Introduction to Ensemble technique
• Bagging and How it works
• Modeling and Evaluation in Python
MODULE 6: ML ALGO: NAÏVE BAYES
• Introduction to Naive Bayes
• How it works: Bayes' Theorem
• Naive Bayes For Text Classification
• Modeling and Evaluation in Python
MODULE 7: GRADIENT BOOSTING, XGBOOST
• Introduction to Boosting and XGBoost
• How it works?
• Modeling and Evaluation of in Python
MODULE 1: TIME SERIES FORECASTING - ARIMA
• What is Time Series?
• Trend, Seasonality, cyclical and random
• Stationarity of Time Series
• Autoregressive Model (AR)
• Moving Average Model (MA)
• ARIMA Model
• Autocorrelation and AIC
• Time Series Analysis in Python
MODULE 2: SENTIMENT ANALYSIS
• Introduction to Sentiment Analysis
• NLTK Package
• Case study: Sentiment Analysis on Movie Reviews
MODULE 3: REGULAR EXPRESSIONS WITH PYTHON
• Regex Introduction
• Regex codes
• Text extraction with Python Regex
MODULE 4: ML MODEL DEPLOYMENT WITH FLASK
• Introduction to Flask
• URL and App routing
• Flask application – ML Model deployment
MODULE 5: ADVANCED DATA ANALYSIS WITH MS EXCEL
• MS Excel core Functions
• Advanced Functions (VLOOKUP, INDIRECT..)
• Linear Regression with EXCEL
• Data Table
• Goal Seek Analysis
• Pivot Table
• Solving Data Equation with EXCEL
MODULE 6: AWS CLOUD FOR DATA SCIENCE
• Introduction of cloud
• Difference between GCC, Azure,AWS
• AWS Service ( EC2 instance)
MODULE 7: AZURE FOR DATA SCIENCE
• Introduction to AZURE ML studio
• Data Pipeline
• ML modeling with Azure
MODULE 8: INTRODUCTION TO DEEP LEARNING
• Introduction to Artificial Neural Network, Architecture
• Artificial Neural Network in Python
• Introduction to Convolutional Neural Network, Architecture
• Convolutional Neural Network in Python
MODULE 1: DATABASE INTRODUCTION
• DATABASE Overview
• Key concepts of database management
• Relational Database Management System
• CRUD operations
MODULE 2: SQL BASICS
• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation
MODULE 3: DATA TYPES AND CONSTRAINTS
• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment
MODULE 4: DATABASES AND TABLES (MySQL)
• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create new table from existing data types
• Insert into, Update records
• Alter table
MODULE 5: SQL JOINS
• Inner join
• Outer join
• Left join
• Right join
• Cross join
• Self join
• Windows functions: Over, Partition , Rank
MODULE 6: SQL COMMANDS AND CLAUSES
• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries
MODULE 7: DOCUMENT DB/NO-SQL DB
• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods
MODULE 1: GIT INTRODUCTION
• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture
MODULE 2: GIT REPOSITORY and GitHub
• Git Repo Introduction
• Create New Repo with Init command
• Git Essentials: Copy & User Setup
• Mastering Git and GitHub
MODULE 3: COMMITS, PULL, FETCH AND PUSH
• Code commits
• Pull, Fetch and conflicts resolution
• Pushing to Remote Repo
MODULE 4: TAGGING, BRANCHING AND MERGING
• Organize code with branches
• Checkout branch
• Merge branches
• Editing Commits
• Commit command Amend flag
• Git reset and revert
MODULE 5: GIT WITH GITHUB AND BITBUCKET
• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers
MODULE 1: BIG DATA INTRODUCTION
MODULE 2: HDFS AND MAP REDUCE
MODULE 3: PYSPARK FOUNDATION
MODULE 4: SPARK SQL and HADOOP HIVE
MODULE 1: TABLEAU FUNDAMENTALS
• Introduction to Business Intelligence & Introduction to Tableau
• Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
• Bar chart, Tree Map, Line Chart
• Area chart, Combination Charts, Map
• Dashboards creation, Quick Filters
• Create Table Calculations
• Create Calculated Fields
• Create Custom Hierarchies
MODULE 2: POWER-BI BASICS
• Power BI Introduction
• Basics Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX FUNCTION
MODULE 3 : DATA TRANSFORMATION TECHNIQUES
• Exploring Query Editor
• Data Cleansing and Manipulation:
• Creating Our Initial Project File
• Connecting to Our Data Source
• Editing Rows
• Changing Data Types
• Replacing Values
MODULE 4 : CONNECTING TO VARIOUS DATA SOURCES
• Connecting to a CSV File
• Connecting to a Webpage
• Extracting Characters
• Splitting and Merging Columns
• Creating Conditional Columns
• Creating Columns from Examples
• Create Data Model
MODULE 1: NEURAL NETWORKS
• Structure of neural networks
• Neural network - core concepts(Weight initialization)
• Neural network - core concepts(Optimizer)
• Neural network - core concepts(Need of activation)
• Neural network - core concepts(MSE & RMSE)
• Feed forward algorithm
• Backpropagation
MODULE 2: IMPLEMENTING DEEP NEURAL NETWORKS
• Introduction to neural networks with tf2.X
• Simple deep learning model in Keras (tf2.X)
• Building neural network model in TF2.0 for MNIST dataset
MODULE 3: DEEP COMPUTER VISION - IMAGE RECOGNITION
• Convolutional neural networks (CNNs)
• CNNs with Keras-part1
• CNNs with Keras-part2
• Transfer learning in CNN
• Flowers dataset with tf2.X(part-1)
• Flowers dataset with tf2.X(part-2)
• Examining x-ray with CNN model
MODULE 4 : DEEP COMPUTER VISION - OBJECT DETECTION
• What is Object detection
• Methods of Object Detections
• Metrics of Object detection
• Bounding Box regression
• labelimg
• RCNN
• Fast RCNN
• Faster RCNN
• SSD
• YOLO Implementation
• Object detection using cv2
MODULE 5: RECURRENT NEURAL NETWORK
• RNN introduction
• Sequences with RNNs
• Long short-term memory networks(part 1)
• Long short-term memory networks(part 2)
• Bi-directional RNN and LSTM
• Examples of RNN applications
MODULE 6: NATURAL LANGUAGE PROCESSING (NLP)
• Introduction to Natural language processing
• Working with Text file
• Working with pdf file
• Introduction to regex
• Regex part 1
• Regex part 2
• Word Embedding
• RNN model creation
• Transformers and BERT
• Introduction to GPT (Generative Pre-trained Transformer)
• State of art NLP and projects
MODULE 7: PROMPT ENGINEERING
• Introduction to Prompt Engineering
• Understanding the Role of Prompts in AI Systems
• Design Principles for Effective Prompts
• Techniques for Generating and Optimizing Prompts
• Applications of Prompt Engineering in Natural Language Processing
MODULE 8: REINFORCEMENT LEARNING
• Markov decision process
• Fundamental equations in RL
• Model-based method
• Dynamic programming model free methods
MODULE 9: DEEP REINFORCEMENT LEARNING
• Architectures of deep Q learning
• Deep Q learning
• Reinforcement Learning Projects with OpenAI Gym
MODULE 10: Gen AI
• Gan introduction, Core Concepts, and Applications
• Core concepts of GAN
• GAN applications
• Building GAN model with TensorFlow 2.X
• Introduction to GPT (Generative Pre-trained Transformer)
• Building a Question answer bot with the models on Hugging Face
MODULE 11: Gen AI
• Introduction to Autoencoder
• Basic Structure and Components of Autoencoders
• Types of Autoencoders: Vanilla, Denoising, Variational, Sparse, and Convolutional Autoencoders
• Training Autoencoders: Loss Functions, Optimization Techniques
• Applications of Autoencoders: Dimensionality Reduction, Anomaly Detection, Image
AI is the future, and DataMites helped me step into it! The online Data Science course was easy to follow, and the tutors explained everything clearly. Even after completing my course, the team supported me with placements and I successfully cracked a job! Grateful to DataMites for shaping my career.
I joined DataMites to learn Data Science and gained strong skills in ML, DL, and Statistics. Through my internship at Rubixe AI Solutions, I worked on real-world projects, which helped me land job interviews and an offer. The trainers were very supportive and explained every concept clearly. Thanks to DataMites, my data science journey was smooth and successful.
My mentor, a DataMites alumnus, suggested I join the Data Science course, and it was one of my best decisions! The offline classes were interactive and full of guidance, and I got the chance to intern at Rubixe AI Solutions. The placement team was very supportive and helped me perfect my resume. Truly thankful to DataMites for this amazing learning journey.
I joined DataMites after completing my engineering in Computer Science and became a Certified Data Scientist. With their support, I completed an internship at Rubix AI Solutions and now work as an AI intern at Inventor Hub. DataMites made learning statistics and data science easy with clear guidance and great materials. I’m truly thankful for their support.
I joined DataMites for the Certified Data Scientist online course, and it was a great experience! The real-time projects helped me apply my learning practically, and the placement support added real value to my career restart after 8 years. DataMites is the perfect platform to learn, grow, and begin your data science journey.
I joined DataMites for the Data Science course and learned Python, SQL, Machine Learning, and Deep Learning through hands-on projects. The trainers are excellent at explaining concepts clearly. The placement team is very supportive and provides great opportunities thanks to DataMites for helping me start my data science journey.
I joined DataMites after my B.Tech when I developed an interest in Data Science and Machine Learning. The training was well-structured, and I even got an internship opportunity that helped me gain real experience. The placement team was super supportive and guided me through every step. Thanks to DataMites, I’m now placed at Horizon Broadband Pvt. Ltd.
I joined DataMites after completing my BCA, and it was the best decision for my data science career! I learned Python, SQL, Machine Learning, Deep Learning, and NLP, and worked on several capstone and client projects that gave me real-world experience. The trainers and team were always friendly and supportive, patiently clearing all my doubts. Thanks to DataMites, I’m confident and job-ready in data science
I started my data science journey with DataMites, and it’s been an amazing experience! The Certified Data Scientist Program helped me learn everything from scratch through hands-on projects and an internship. The placement team was incredibly supportive, always ready to clarify my doubts. Thanks to their guidance, I successfully got placed. Highly recommend DataMites to anyone starting their data science career
Enrolling at DataMites was one of the best decisions I made for my career. Coming from a freelance writing background with no prior technical experience, I was initially unsure about stepping into the field of data analytics. But DataMites made the transition smooth and achievable. I’d highly recommend them to anyone looking for a guided, industry-ready data analytics course. A true DataMites success story!
Eligibility for an artificial intelligence course in the Philippines depends on the course level and curriculum. Beginner programmes typically accept learners with basic computer skills, while advanced courses may require a degree or knowledge of mathematics, statistics, or programming.
An online AI course in the Philippines is suitable for students, fresh graduates, working professionals, entrepreneurs, and career changers interested in artificial intelligence. Beginners can start with AI fundamentals, while learners with technical experience can choose specialised courses in machine learning, data science, or AI development.
An online artificial intelligence course in the Philippines may take anywhere from a few hours to 12 months or longer, depending on its depth and format.
Introductory courses: A few hours to 4 weeks.
Skills-based courses: 1–6 months.
Professional certification programmes: 3–12 months or longer.
As of October 2026, an online artificial intelligence course in the Philippines typically costs around PHP 2,000 to15,000 for short courses and PHP 15,000 to 80,000 or more for comprehensive professional programmes. Fees vary depending on course duration, curriculum, practical projects, instructor support, and certification, while some introductory courses are available free of charge.
An AI course can help learners prepare for roles across technology, finance, healthcare, retail, manufacturing, and business services, depending on their existing qualifications and practical skills. Potential career paths include AI developer, machine learning engineer, data analyst, AI automation specialist, and natural language processing specialist.
According to JobStreet Philippines, AI and Machine Learning Engineers at CGI have an estimated salary range of PHP 80,000 to 120,000 per month, based on past job advertisements and refreshed. For comparison, Indeed reported an average base salary of PHP 42,963 per month for Machine Learning Engineers in the Philippines, updated on 24 September 2026; actual earnings vary by experience, technical skills, location, and employer.
An online AI course can help you develop practical skills in data analysis, machine learning, model evaluation, AI tools, and responsible AI use. Depending on the syllabus, you may also learn Python programming, prompt design, data visualisation, automation, and how to build small AI projects.
Yes, working professionals can enrol in an artificial intelligence course in the Philippines, including flexible online programmes that allow them to study outside regular working hours. Self-paced lessons, recorded lectures, and weekend sessions can make it easier to balance training with existing work commitments.
Prior coding knowledge is not essential for introductory AI courses or roles focused on AI tools, business processes, and prompt design. However, careers in AI engineering and machine learning generally require programming skills, particularly Python, along with mathematics, statistics, and practical project experience.
Artificial intelligence (AI) is the broader field of creating computer systems that perform tasks associated with human intelligence, such as reasoning and problem-solving. Machine learning is a branch of AI that learns patterns from data, while deep learning uses multi-layered neural networks to handle complex tasks such as image recognition, speech processing, and language generation.
Python is one of the most widely used programming languages for AI because it supports data analysis, machine learning, and deep learning through extensive libraries. Other useful languages include R for statistics, SQL for working with databases, Java for enterprise applications, and C++ for performance-intensive systems.
Degrees in computer science, information technology, mathematics, statistics, engineering, and data science can provide a strong foundation for technical AI careers. Professional certificates, practical projects, programming experience, and a portfolio demonstrating AI applications can also strengthen a candidate's profile.
Common AI career roles include AI Engineer, Machine Learning Engineer, Data Scientist, Natural Language Processing Engineer, Computer Vision Engineer, and AI Researcher. Other options include AI Automation Specialist, AI Product Manager, and AI Ethics Specialist, with requirements varying according to the technical and business responsibilities of each position.
Artificial intelligence can be a promising career option in the Philippines for people who enjoy technology, problem-solving, and continuous learning. The Department of Science and Technology's initiatives to strengthen national AI research and capabilities indicate growing institutional interest, although employment opportunities depend on market demand, relevant skills, and practical experience.
Generative AI is helping Philippine workplaces draft content, summarise documents, assist with customer service, analyse information, and automate selected repetitive tasks. Its adoption is particularly relevant to the information technology and business process management sector, although organisations must also address accuracy, data privacy, job redesign, and responsible use.
Yes, people from non-technical backgrounds can build careers involving AI by starting with foundational courses and learning how AI tools support their existing areas of expertise. Opportunities may include AI-assisted marketing, business analysis, content operations, workflow automation, and AI project coordination, while more technical roles require additional training.
Major AI trends in the Philippines include generative AI adoption, business process automation, AI-assisted customer service, data analytics, and the development of local AI research capabilities. Responsible AI governance, workforce reskilling, data privacy, and efforts to expand AI access beyond major urban centres are also important areas of development.
Yes, AI-related skills are becoming increasingly relevant in Philippine industries, including IT and business process management, financial services, telecommunications, and other technology-enabled sectors. However, demand varies by role and experience level, so learners should combine AI knowledge with programming, data skills, domain expertise, and practical project work to improve their employment prospects.
Our DataMites Artificial Intelligence course in the Philippines is suitable for graduates, working professionals, and individuals interested in developing AI skills. A background in programming, mathematics, statistics, or computer science can be helpful, and we recommend contacting our team to confirm the eligibility requirements for your profile.
The fee for our DataMites AI course in the Philippines ranges from PHP 102,080 to PHP 145,560, depending on the selected course option. Contact our DataMites team for a detailed fee breakdown and confirmation of the package that best suits your learning goals.
Our DataMites Artificial Intelligence training in the Philippines runs for 9 months, with 780+ learning hours. This duration gives learners time to build their AI knowledge, develop technical skills, and work on practical learning activities.
Learners may receive a DataMites course completion certificate after meeting the applicable course requirements and certification terms. NASSCOM FutureSkills Certification is available for eligible NRI learners. Please contact our team to confirm eligibility and the certification options available for your enrolment.
Yes, our AI training in the Philippines offers a live online learning format, allowing learners to participate in instructor-led sessions remotely. Contact our team for details about available batches, session schedules, and the online learning arrangements for your chosen course.
To enrol in our DataMites Artificial Intelligence course in the Philippines, contact our team to discuss your learning goals, available batches, course options, and fees. Our team will guide you through the registration process and explain the applicable payment terms before you confirm your enrolment.
We accept online debit and credit card payments through Razorpay, while credit card EMI is available where applicable. A token advance is collected during registration, and the remaining course fee must be paid in full before course completion; contact our team to confirm the terms applicable to your enrolment.
You can contact our team to enquire about demo or trial class availability for our AI training in the Philippines. We will help you confirm the available options so you can learn more about the course structure and online learning format before enrolling.
If you miss a live online session, contact our DataMites team to ask about options for catching up on the missed topics. Recording availability and any revision arrangements depend on the applicable course provisions and should be confirmed with our team.
Our DataMites AI course in the Philippines may be suitable for beginners who want to build their understanding of artificial intelligence. Basic programming and mathematical knowledge can be helpful, so we encourage learners without a technical background to discuss their current skills with our team before choosing a course.
Our DataMites Artificial Intelligence course fee ranges from PHP 102,080 to PHP 145,560, depending on the selected course option. The package may include structured learning, training sessions, and practical activities, but we recommend contacting our team to confirm the exact inclusions, learning resources, and certification provisions.
Internship availability and eligibility depend on the applicable program arrangements and should be confirmed directly with our DataMites team. Contact us to learn whether internship opportunities are included in or available alongside your selected AI training program in the Philippines.
Refund eligibility depends on the applicable cancellation and refund policy for your enrolment. Please contact our DataMites team to confirm the relevant terms, eligibility conditions, and cancellation procedure before making a decision.
Our DataMites AI training in the Philippines offers online learning options, including live online classes. Learners can also enquire about the Flexi Pass, which allows them to attend multiple batches of the same course during its validity period and revisit topics as needed; it remains valid for 3 months from activation.
Our DataMites Artificial Intelligence course covers core AI concepts and relevant technical skills, with topics potentially including Python programming, machine learning, deep learning, data preparation, and AI applications. For the latest module-wise syllabus and the exact tools and technologies covered, please contact our team.
Our AI training in the Philippines is designed to help learners connect theoretical concepts with practical applications through hands-on learning activities and project-based work. Contact our DataMites team to confirm the specific projects, exercises, and practical components included in your chosen course option.
The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -
The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.
No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.
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