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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 Singapore depends on the course level and learning objectives. Beginner courses usually require basic computer skills, while advanced programmes may expect knowledge of mathematics, statistics, programming, or a related academic qualification.
An online AI course in Singapore is suitable for students, fresh graduates, working professionals, business owners, and career changers. Beginners can start with AI fundamentals, while learners with technical experience can choose specialised courses in machine learning, data science, or generative AI.
The duration of an online artificial intelligence course in Singapore varies according to its depth and format.
Short introductory courses: 1 day to 4 weeks.
Skills-based courses: 1 to 6 months.
Professional certification programmes: 3 to 12 months or longer.
Online AI course fees in Singapore generally range from around S$ 50 to S$ 500 for short introductory courses and S$ 1,000 to S$ 8,000 or more for comprehensive professional programmes. These are indicative estimates rather than official fees, and the final cost depends on course duration, curriculum, practical projects, instructor support, and certification.
An AI course can help learners prepare for roles in technology, finance, healthcare, manufacturing, retail, and other sectors, depending on their skills and qualifications. Potential career paths include AI engineer, machine learning engineer, data analyst, AI application developer, and AI solutions consultant.
The average salary of an AI Engineer in Singapore is approximately S6,000permonth(S72,000 per year) in base pay, according to Glassdoor's Singapore AI Engineer salary data. Actual earnings vary depending on experience, technical skills, industry, employer, and job responsibilities.
An online AI course can help you develop practical skills for understanding AI systems, working with data, and applying intelligent tools to business problems. Depending on the syllabus, you may learn:
Python programming and data analysis.
Machine learning and basic model development.
Generative AI tools and prompt design.
Model evaluation, responsible AI practices, and problem-solving.
Yes, working professionals can choose part-time, evening, weekend or self-paced online AI courses that fit around their work commitments. These programmes can help learners apply AI tools to existing tasks or prepare for more technical responsibilities without necessarily leaving their current jobs.
No, programming experience is not essential for beginner-level AI courses that focus on AI concepts, generative AI tools, and practical workplace applications. However, learning Python, mathematics, and basic statistics is useful if you plan to build AI models or pursue a technical AI career.
Python is widely used in AI because it offers accessible tools for data analysis, machine learning, and deep learning. Other useful languages include R for statistical computing, SQL for working with databases, and C++ for performance-intensive applications.
A degree or diploma in computer science, data science, mathematics, statistics, or engineering can provide a strong foundation for technical AI roles. Relevant certifications, practical projects, and demonstrable programming skills can also strengthen an application, although specific employers may require particular academic or professional qualifications.
Common AI job roles include AI engineer, machine learning engineer, data scientist, natural language processing engineer, and computer vision engineer. Other opportunities include AI product manager, AI solutions consultant, and AI governance specialist, with the required qualifications depending on the position.
Singaporean businesses are adopting AI for customer service, financial analysis, healthcare support, manufacturing operations, logistics, and business process automation. The Ministry of Manpower's April 2026 report found that AI adoption was more prevalent in digitally intensive sectors, including information and communications, professional services, and financial and insurance services.
AI can be a worthwhile career option in Singapore for people who enjoy technology, data analysis, and solving practical problems. The Infocomm Media Development Authority reported rising demand for AI-related technical skills and increasing business adoption, although career opportunities depend on qualifications, experience, and the ability to apply AI responsibly.
Artificial intelligence (AI) is the broad field of creating systems that perform tasks associated with human intelligence, such as understanding language or making decisions. Machine learning is a part of AI that learns patterns from data, while deep learning is a type of machine learning that uses multiple layers of artificial neural networks to handle complex tasks such as image recognition and speech processing.
Generative AI is helping Singaporean workers draft content, summarise documents, analyse information, generate ideas and automate routine tasks. The Infocomm Media Development Authority's 2025 report found that 73.8% of surveyed workers used AI tools at work, highlighting the growing importance of AI literacy, verification, and responsible data handling.
A successful AI career often combines technical knowledge with analytical thinking and clear communication. Important skills include programming, mathematics, statistics, data preparation, machine learning, problem-solving, collaboration, and an understanding of AI ethics and data privacy.
Yes, people from business, marketing, finance, human resources, and other non-technical fields can build AI-related skills through beginner courses and practical projects. Roles involving AI adoption, workflow improvement, product coordination, and responsible AI use may suit their existing expertise, while more technical positions usually require additional training in programming, mathematics, or data science.
Our DataMites Artificial Intelligence course in Singapore is suitable for learners with an interest in AI, programming, and data-driven problem-solving. Basic knowledge of mathematics and programming can be helpful, but specific eligibility requirements may vary depending on the course track. Contact our team to confirm the requirements for your preferred program.
The fee for our DataMites Artificial Intelligence course in Singapore ranges from S$ 2,310 to S$ 3,300, depending on the selected learning option. For a detailed fee breakdown and available payment arrangements, please contact our DataMites team.
Our DataMites AI course in Singapore has a duration of 9 months, with 780+ learning hours. This learning schedule provides time to build AI knowledge, practise relevant skills, and work on practical learning activities.
To enrol, contact our DataMites team to discuss your learning goals, course options, fees, and eligibility. Once you have confirmed the details, our team can guide you through the registration process and payment requirements.
After meeting the applicable course completion requirements, learners may receive the relevant DataMites course certification. NASSCOM FutureSkills Certification is available for eligible NRI learners, subject to the applicable requirements. Contact our team to confirm the certification options available for your chosen course.
Yes, our AI training in Singapore offers live online learning options, allowing learners to study remotely and interact with instructors during scheduled sessions. This format can be a convenient choice for working professionals and students looking for an online AI course in Singapore.
We accept online debit and credit card payments through Razorpay, and 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. Our team can clarify the applicable payment schedule and EMI availability when you enrol.
Refund eligibility depends on the applicable cancellation and refund policy for your enrolment. Please contact our DataMites team to confirm the terms, eligibility, and procedure before making a cancellation decision.
You can contact our DataMites team to ask about demo or trial class availability for the Artificial Intelligence course in Singapore. Our team can confirm the available sessions and explain how to attend one before you enrol.
If you miss a live online session, contact our team to check the available options for catching up on the missed topics. Access to recordings, alternative batches, or other learning resources depends on the arrangements available for your course.
Our DataMites Artificial Intelligence course in Singapore may suit students, graduates, working professionals, and career changers who want to develop AI skills. Learners from different educational and professional backgrounds can explore the program, subject to the eligibility requirements of their selected course.
Our AI curriculum covers core concepts and practical applications of artificial intelligence, with topics that may include machine learning, deep learning, neural networks, and natural language processing. The course also focuses on applying these concepts to relevant problems through practical exercises and projects. Contact our team for the detailed syllabus of your chosen learning track.
Our AI training program incorporates practical learning activities and project-based work to help learners apply their knowledge to real-world problem scenarios. The specific projects and hands-on components depend on the selected course track and curriculum.
We offer learning options designed to accommodate different schedules and learning preferences, including online learning formats. Depending on the course and availability, learners may also be able to use a Flexi Pass, which allows them to attend multiple batches of the same course during its validity period and revisit topics as needed. The Flexi Pass remains valid for 3 months from activation; contact our team to confirm the options available for your chosen program.
Our AI curriculum may cover Python programming, machine learning libraries, deep learning frameworks, and tools used to develop and evaluate AI solutions. The exact technologies depend on the course track and syllabus, so we recommend checking with our team for the current list of tools included in your program.
At DataMites, we focus on structured AI learning, practical exercises, project-based experience, and flexible online learning options. Our 9-month program includes 780+ learning hours, helping learners build their understanding of AI concepts and their practical applications. Contact our team to explore the course format, inclusions, certification options, and fees before enrolling.
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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