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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 UAE depends on the course level and curriculum. Beginners can usually start with basic computer knowledge, while advanced programs may require a degree or experience in computer science, mathematics, statistics, or programming.
An online AI course in the UAE is suitable for university students, fresh graduates, working professionals, career changers, and entrepreneurs interested in artificial intelligence. Beginners can start with introductory topics, while IT professionals can choose advanced training in machine learning, deep learning, or generative AI.
The duration of an online artificial intelligence course in the UAE varies according to the depth of the syllabus and learning format.
Practical projects, assessments, and self-paced learning can affect the total completion time.
Course fees vary by provider, duration, curriculum, practical projects, and certification options. As a general budgeting estimate, short online courses may cost around AED 300 to 2,000, while comprehensive professional AI programs may range from AED 3,000 to 20,000 or more; these are indicative ranges, not official or universal fees.
An AI course can help learners prepare for roles that match their technical knowledge, qualifications, and practical experience. Common career options include:
Building a project portfolio and developing relevant programming and analytical skills can improve readiness for these positions.
According to Naukrigulf, an artificial intelligence engineer in the UAE earns an average of approximately AED 7,524 per month, based reported salaries. Monthly earnings range from AED 2,222 to AED 18,518, depending on experience and other employment factors. These figures represent AI engineer salaries across the UAE and may differ by location, technical expertise, and employer.
Artificial intelligence is being applied across the UAE to improve services, analyze information, and automate routine tasks. Examples include healthcare diagnostics, fraud detection in finance, predictive maintenance in energy, route optimization in transport, personalized customer service in retail, and automated government services. Adoption is supported by national AI initiatives and sector-specific digital transformation programs.
Yes, professionals from finance, marketing, healthcare, education, human resources, and other fields can move towards AI-related work by learning relevant tools and concepts. Starting with AI fundamentals, data literacy, and practical applications in their existing industry can help them identify suitable roles, including AI product coordination, business analysis, and AI operations. Engineering positions generally require stronger programming and mathematical skills.
Skills related to AI development, business adoption, and responsible technology use are likely to remain important as organizations expand their AI capabilities. Useful areas to study include
Communication, problem-solving, and knowledge of industry-specific requirements are also valuable.
Python is widely used in AI because it supports machine learning libraries, data analysis, and deep learning frameworks. Other useful languages include R for statistical analysis, Java for enterprise applications, C++ for performance-intensive systems, and SQL for working with databases. The best choice depends on the project and job requirements.
No, coding is not essential for every AI-related role. Business analysts, AI project coordinators, product managers, and AI adoption specialists may focus more on business knowledge, communication, and understanding AI capabilities. However, AI developers, machine learning engineers, and research specialists generally need programming skills and a stronger technical foundation.
AI-related job titles vary across employers and industries, but several common roles focus on developing, deploying, or managing AI systems. These include AI Engineer, Machine Learning Engineer, Data Scientist, NLP Engineer, Computer Vision Engineer, AI Research Scientist, and AI Solutions Architect. Required qualifications differ depending on whether the position involves research, software development, data analysis, or business implementation.
AI can be a well-paid career in the UAE, particularly for experienced professionals with specialized technical skills and the ability to deliver business results. However, salaries differ significantly by role, seniority, employer, and location, so AI-related positions should not be assumed to offer the same pay. Beginners may need practical experience and a strong portfolio before qualifying for more advanced roles.
Major AI trends in the UAE include generative AI, AI agents, smart government services, autonomous systems, and wider use of AI in healthcare, finance, transport, and energy. Organizations are also paying greater attention to AI governance, data protection, cybersecurity, and human oversight. These developments align with the country's broader plans to expand responsible AI adoption across public and private sectors.
You can begin by learning the fundamentals of AI, mathematics, statistics, and programming before progressing to more specialized topics. A practical starting plan is to:
An online AI course in the UAE can provide a structured learning path, but employment depends on your skills, qualifications, experience, and the requirements of individual employers.
Artificial intelligence (AI) is the broader field of creating computer systems that perform tasks associated with human intelligence. Machine learning (ML) is a branch of AI in which systems learn patterns from data, while deep learning is a specialized type of machine learning that uses multi-layered neural networks to handle complex tasks such as image recognition, speech processing, and language generation.
Generative AI is helping organizations draft content, summarize documents, support customer service, analyze information, and assist with software development. In the UAE, its growing use can improve productivity and support new services, but organizations must also address data privacy, accuracy, intellectual property, and human review. Employees who learn to use these tools responsibly may be better prepared for changing workplace requirements.
Yes, AI adoption is an important part of the UAE's digital development, with initiatives supporting AI use in government services, healthcare, transport, finance, and other sectors. The UAE's National Strategy for Artificial Intelligence 2031 and newer government initiatives demonstrate continued institutional interest in the technology. However, demand varies by role and experience, so candidates should develop practical skills that match current employer requirements.
At DataMites, learners interested in an artificial intelligence course in the UAE can explore AI training based on their existing knowledge and learning goals. Those with basic computer knowledge can start with AI fundamentals, while programming and mathematical knowledge can be helpful for understanding advanced concepts.
The DataMites Artificial Intelligence course in the UAE costs between AED 7,940 and AED 11,320, depending on the applicable program and fee option. For the latest fee details and available payment arrangements, learners can contact the DataMites team.
DataMites provides online AI training, allowing learners in the UAE to study without travelling to a training location. Learners can contact the team to confirm the available learning format, session schedule, and course access details before enrolling.
Learners who meet the applicable course and assessment requirements can receive IABAC certification after completing the program. NASSCOM FutureSkills Certification is also available for eligible NRI learners, subject to the applicable requirements.
The DataMites Artificial Intelligence course in the UAE has a duration of 8 months, with 160 Live training hours.This provides time to study AI concepts and develop a stronger understanding of relevant technologies through structured learning.
To enroll, visit the DataMites website, navigate to the Artificial Intelligence course page, and submit an inquiry or registration request. The team can guide you through the available course options, registration steps, fee details, and payment process.
Demo or trial class availability may depend on the current course schedule and applicable arrangements. Contact the DataMites team to confirm whether a demo session is available before registering for the AI engineer course.
DataMites accepts online debit and credit card payments through Razorpay, and credit card EMI may be available for eligible transactions. A token advance is collected during registration, and the remaining course fee must be paid in full before course completion.
Yes, beginners interested in AI can explore the course even if they have not previously studied artificial intelligence. Learning basic programming and mathematical concepts can help them understand topics such as machine learning, deep learning, and generative AI more comfortably.
If you miss a live online AI training session, contact the DataMites team to confirm the available options for revisiting the missed topics. The DataMites Flexi Pass allows learners to attend multiple batches of the same course during its validity period, helping them review topics as needed; it remains valid for 3 months from the date of activation.
Internship inclusion depends on the specific program terms and should be confirmed before enrollment. Contact the DataMites team to check whether an internship is included in the Artificial Intelligence course in the UAE.
AI training generally covers the core concepts and technologies used to develop intelligent applications. Depending on the course syllabus, topics may include Python programming, machine learning, deep learning, neural networks, natural language processing, computer vision, and generative AI; contact DataMites for the confirmed syllabus.
Refund eligibility and cancellation conditions depend on the applicable enrollment terms. Before cancelling, contact the DataMites team to confirm the current refund policy, any applicable conditions, and the process for submitting a request.
Practical exercises and projects can help learners apply AI concepts to realistic problems and understand how intelligent applications are developed. Contact the DataMites team to confirm the specific hands-on activities, project work, and practical components included in the current course syllabus.
Learners can inquire about the available online learning arrangements and session schedules to find an option that fits their study commitments. The DataMites Flexi Pass also allows learners to attend multiple batches of the same course within 3 months of activation, making it possible to revisit topics during its validity period.
DataMites offers a structured artificial intelligence course with 8 months of training and 440 learning hours, along with IABAC certification for eligible learners and NASSCOM FutureSkills Certification availability for eligible NRI learners. The course fee ranges from AED 7,940 to AED 11,320, and learners can inquire about the syllabus, learning arrangements, payment options, and Flexi Pass before making a decision.
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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