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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 Australia depends on the course level and curriculum. Beginner courses are generally open to students, graduates, and working professionals, while advanced programs may require knowledge of programming, mathematics, statistics, or computer science.
An online AI course in Australia is suitable for university students, recent graduates, IT professionals, business analysts, and people looking to change careers. Beginners can start with foundational courses, while experienced learners can pursue specialized training in machine learning, data science, or generative AI.
Training by industry experts.
Internship Opportunities(10 Capstone Projects and 1 Client Project)
An online artificial intelligence course in Australia can take anywhere from a few days to 12 months or longer, depending on the content and learning format.
Introductory courses: 1 to 4 weeks
Skills-based courses: 2 to 6 months
Professional certification programs: 6 to 12 months or longer
As a general budgeting guide, short online AI courses in Australia may cost around AUD 50 to 500, while structured training programs can range from AUD 500 to 5,000, and comprehensive professional programs may cost AUD 5,000 to 15,000 or more. These are indicative estimates rather than official market averages, as fees depend on course duration, curriculum, practical projects, instructor support, and certification.
Completing an AI course can help prepare learners for roles involving data analysis, intelligent software development, automation, and AI implementation. Potential career paths include AI engineer, machine learning engineer, data scientist, AI developer, data analyst, and AI solutions consultant, depending on qualifications and practical experience.
According to Indeed Australia, the average base salary for an AI/ML Engineer is approximately AUD 142,578 per year. For additional reference, Robert Half Australia’s salary guide lists an annual salary range of AUD 150,000–195,000 for AI Engineers, with a median of AUD 170,000; actual earnings vary according to experience, location, technical skills, industry, and job responsibilities.
An online AI course can help you develop technical and practical skills for building, testing, and applying AI solutions. Common learning areas include:
Python programming and data preparation
Machine learning and model evaluation
Generative AI tools and prompt engineering
Data visualization, responsible AI, and project development
Yes, working professionals can enrol in artificial intelligence courses in Australia, including flexible online programmes with recorded lessons, evening classes, or weekend sessions. These options allow learners to build relevant skills while balancing work and personal commitments.
No, programming experience is not essential for beginner-level AI courses, as many introduce the fundamental concepts before moving into practical exercises. However, learning Python, basic mathematics, and statistics will help you progress towards technical areas such as machine learning and deep learning.
Qualifications in computer science, information technology, engineering, mathematics, statistics, or data science can provide a useful foundation for an AI career. Professional certificates, practical projects, and a portfolio demonstrating your skills can also support job applications, although requirements differ across employers and positions.
Python is one of the most widely used languages in AI because it offers accessible syntax and extensive libraries for machine learning and data analysis. Other useful languages include R for statistical computing, SQL for database queries, Java for software applications, and C++ for performance-intensive systems.
Common AI-related roles in Australia include AI engineer, machine learning engineer, data scientist, AI developer, NLP engineer, computer vision engineer, and AI solutions architect. Opportunities are also available in AI product management, data analytics, and AI governance, depending on an individual's experience and skills.
AI can be a worthwhile career path in Australia for people interested in technology, data, automation, and problem-solving. The National AI Centre's 2025 ecosystem report found that 1,532 organisations sought workers with AI-related skills in 2024, up from 483 in 2015, although employment opportunities depend on skills, industry demand, and experience.
Artificial intelligence (AI) is the broad field of creating computer systems that perform tasks associated with human intelligence, such as understanding language and making predictions. Machine learning is a branch of AI that learns patterns from data, while deep learning is a type of machine learning that uses multi-layered neural networks to handle complex tasks such as image recognition and language processing.
Generative AI is helping Australian workplaces draft documents, summarize information, support customer service, generate code, and automate routine tasks. Jobs and Skills Australia's Generative AI Capacity study found that generative AI is more likely to change or support many work activities than replace entire jobs, increasing the importance of digital skills, human judgment, and responsible AI use.
Yes, people from non-technical backgrounds can work in AI by learning to use AI tools, understand their limitations, and apply them to business or industry-specific problems. Roles in AI adoption, product coordination, business analysis, content development, and governance may be accessible with relevant experience and additional training.
Skills in machine learning, Python programming, data science, generative AI, cloud computing, and AI system integration are relevant to Australia's developing AI ecosystem. Analytical thinking, communication, cybersecurity awareness, and responsible AI practices are also valuable as organizations expand their use of AI technologies.
Yes, demand for AI-related skills is growing across Australian industries, including finance, healthcare, professional services, technology, and business operations. The Australian Government's National AI Plan identifies AI capability, workforce training, infrastructure investment, and responsible adoption as priorities, although demand varies by occupation and location.
Our DataMites AI Engineer course in Australia is suitable for learners with an interest in artificial intelligence, programming, and data-driven problem-solving. A background in IT, computer science, mathematics, or a related field can be helpful, but we recommend contacting our team to confirm the eligibility requirements for your preferred course.
Our DataMites Artificial Intelligence course in Australia has a duration of 9 months, with 780+ learning hours covering concepts, practical exercises, and project-based learning. This structure helps learners build their AI knowledge progressively while balancing their other commitments.
The fee for our DataMites AI course in Australia ranges from AU$ 3,160 to AU$ 4,510, depending on the selected program and applicable course options. For an accurate fee breakdown and confirmation of available payment arrangements, please contact our DataMites team.
Our AI training in Australia provides access to course-related certification options, subject to the requirements of the selected program and successful completion of the applicable assessments. NASSCOM FutureSkills Certification is available for eligible NRI learners, subject to the relevant eligibility criteria; please contact our team to confirm the certification applicable to your enrollment.
Yes, we offer online learning options for our DataMites Artificial Intelligence course in Australia, including live online classes where available. Our online AI course in Australia helps learners access instructor-led learning and practical training without needing to attend sessions in person.
You can contact our DataMites team through our official website to discuss the course, confirm eligibility, and explore available batches and learning options. Once you have confirmed the details, our team can guide you through registration, the token advance payment, and the remaining enrollment steps.
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; please contact our team to confirm the applicable payment schedule and options.
Refund eligibility depends on the applicable cancellation and refund policy for your DataMites AI course enrollment. Please contact our team to confirm the relevant terms, eligibility requirements, and any applicable conditions before making a cancellation decision.
You can contact our DataMites team to ask whether a demo or trial class is available for the Artificial Intelligence course in Australia. Our team can confirm the available sessions and explain how to access one before you enroll.
If you miss a live online session, please contact our DataMites team to confirm whether a recording, make-up session, or alternative learning support is available for your batch. Availability and access arrangements may vary, so we recommend checking the applicable policy before enrolling.
Our DataMites Artificial Intelligence course in Australia can be suitable for students, graduates, working professionals, and career changers who want to develop AI skills. Prior programming or mathematical knowledge may be helpful, but we encourage prospective learners to confirm the specific entry requirements with our team.
Our AI training program is designed to develop practical knowledge of artificial intelligence, machine learning, data preparation, model development, and related AI concepts. The exact curriculum may vary by program, so contact our DataMites team for the detailed syllabus, module list, and project requirements.
Our DataMites AI training focuses on developing practical skills through project-based learning, with the specific projects and internship components depending on the selected program. Please contact our team to confirm whether an internship is included and which real-world or industry-oriented projects form part of your course.
Our AI training may cover programming tools, data analysis techniques, machine learning frameworks, and technologies used to build and evaluate AI models. The exact tools depend on the selected course and curriculum, so we recommend requesting the current technology list from our DataMites team.
We offer online learning options, including live online training where available, to help learners pursue AI education with greater flexibility. Depending on the program, learners may also be able to use a Flexi Pass, which allows attendance across multiple batches of the same course during its validity period and provides opportunities to revisit topics; the pass remains valid for 3 months from activation.
At DataMites, we focus on structured AI learning, practical skill development, and course options designed to help learners build their understanding of artificial intelligence. Our DataMites Artificial Intelligence course in Australia combines a defined learning duration, project-oriented training, and online learning options, while our team can help you confirm the curriculum, fees, and certification details before enrollment.
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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