4.9 (22,700) reviews NGN 1,925,000
NGN 1,402,172*
*Offer valid till 31st October 2026
Instructor Led Live Online
Self Learning + Live Mentoring
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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!
The eligibility requirements for an artificial intelligence course in Abuja depend on the course level and curriculum. Beginner programs generally accept students, graduates, working professionals, and career changers, while advanced courses may require a degree or knowledge of mathematics, statistics, or programming.
An online AI course in Abuja can suit university students, recent graduates, IT professionals, business owners, analysts, and people exploring a new career path. Beginners can start with AI fundamentals, while learners with technical experience can choose specialized training in machine learning, deep learning, or AI application development.
An online artificial intelligence course in Abuja may take anywhere from a few days to 12 months, depending on its depth and learning format.
Self-paced courses may take longer if you study alongside work or other commitments.
The cost of an artificial intelligence course in Abuja generally ranges from NGN 10,000 to NGN 120,000 for short online courses and approximately NGN 200,000 to NGN 600,000 or more for comprehensive professional training programs. The actual fees depend on the course duration, curriculum, practical projects, and certification included.
AI training can prepare learners for technical and business-focused roles across finance, healthcare, education, telecommunications, consulting, and public services. Relevant skills may support applications for roles such as AI developer, machine learning engineer, data analyst, AI automation specialist, or generative AI specialist, depending on the learner's experience and project portfolio.
According to PBridge's 2026 AI Engineer salary guide for Abuja, mid-level AI engineers with 2 to 5 years of experience can earn approximately NGN 665,000 to NGN 1,140,000 per month. Salaries vary depending on experience, technical expertise, and employer, while entry-level and senior professionals may earn different amounts.
A structured online AI course in Abuja may help you develop the following skills, depending on the syllabus:
Advanced programs may also cover model evaluation, deployment, data privacy, and responsible AI practices.
Yes, working professionals in Abuja can study AI through flexible online classes, evening sessions, weekend programs, or self-paced learning. Choosing a course with practical assignments and a manageable weekly schedule can help learners build skills without interrupting their existing work commitments.
No, prior programming knowledge is not essential for a beginner-level artificial intelligence course in Abuja. Introductory programs usually explain core concepts before progressing to Python, algorithms, and model development, while advanced technical courses may expect previous coding experience.
Artificial intelligence uses algorithms and data to recognize patterns, make predictions, understand language, or generate content, with different systems performing different tasks. In Nigeria, potential applications include fraud detection in banking, medical data analysis, agricultural forecasting, customer support, logistics, and document processing; the National Artificial Intelligence Strategy discusses AI adoption across several of these sectors.
Python is widely used because it offers libraries for data analysis, machine learning, and deep learning, including scikit-learn, PyTorch, and TensorFlow. Other useful languages include R for statistical analysis, SQL for working with databases, and JavaScript for integrating AI features into web applications.
Yes, people from backgrounds such as business, finance, education, marketing, and administration can begin with AI fundamentals and gradually develop technical or applied skills. Depending on their interests, they may explore AI-assisted business analysis, workflow automation, prompt design, or technical development after learning the relevant tools and building practical projects.
AI-related roles in Abuja include positions in software development, data analysis, automation, research, and business technology. Depending on qualifications and experience, possible roles include:
Current vacancies can be explored through Indeed's AI job listings for Abuja.
AI development in Abuja may expand as organizations explore automation, data-driven decisions, digital public services, and locally relevant AI applications. The National Centre for Artificial Intelligence and Robotics (NCAIR) is based in Abuja and supports AI research, robotics, and innovation, while Nigeria's National AI Strategy outlines wider plans for responsible AI adoption and talent development.
AI can be a worthwhile career option in Abuja for people who enjoy problem-solving, technology, data, or improving business processes. Opportunities depend on employers' needs, practical experience, and the ability to apply AI responsibly, so learners should build demonstrable projects and continue updating their skills rather than relying on a certificate alone.
Artificial intelligence (AI) is the broad field of creating computer systems that perform tasks associated with human intelligence, while machine learning (ML) is a branch of AI that enables systems to learn patterns from data. Deep learning is a specialized form of machine learning that uses multi-layered neural networks to handle complex tasks such as speech recognition, image analysis, and language generation.
Generative AI tools can help Abuja-based organizations draft documents, summarize reports, answer customer queries, analyze information, and create content more efficiently. Their usefulness depends on the task and the quality of human review, particularly when handling confidential information or making decisions that require accuracy and accountability; Nigeria's National AI Strategy highlights responsible adoption and data protection as important considerations.
Demand will depend on how businesses and public institutions adopt AI, but several skills are likely to remain useful as applications develop:
Nigeria's National Artificial Intelligence Strategy supports AI talent development, innovation, and responsible deployment, although specific future job demand in Abuja cannot be guaranteed.
The DataMites AI Engineer course is designed for learners interested in developing skills in artificial intelligence and related technologies. If you are unsure whether your educational background or current skill level is suitable, contact the DataMites team to confirm the eligibility requirements before enrolling.
The DataMites Artificial Intelligence course runs for 9 months with 780+ learning hours. This provides learners with an extended period to study AI concepts and develop their understanding of the subject.
You can visit the official DataMites website and navigate to the Artificial Intelligence course page to review the program details and registration options. Submit your enrollment details and contact the DataMites team if you need help confirming the next steps.
Yes, learners can explore the online learning options available through DataMites for AI training. Contact the team to confirm the current live-class schedule, batch timings, and delivery details for the Artificial Intelligence course.
The DataMites Artificial Intelligence course fee ranges from NGN 1,150,000 to NGN 1,925,000. The applicable fee may depend on the selected program option, so contact the DataMites team to confirm the current fee and what is included before registering.
After completing the applicable course and certification requirements, learners can pursue IABAC certification. NASSCOM FutureSkills Certification is also available for eligible NRI learners, subject to the relevant eligibility criteria and certification requirements.
The DataMites AI course may interest students, graduates, working professionals, and individuals looking to build knowledge of artificial intelligence. For confirmation of any specific educational prerequisites or other eligibility conditions, contact the DataMites team before enrolling.
Internship opportunities and real-time project components should be confirmed with the DataMites team, as their availability may depend on the specific program. Before enrolling, ask which practical assignments, projects, or internship options are included in your selected AI Engineer course.
You can contact the DataMites team to ask whether a demo session or trial class is currently available for the Artificial Intelligence course. They can confirm the available options and explain how to arrange a session before you make an enrollment decision.
DataMites accepts online debit and credit card payments through Razorpay, and credit card EMI may be available. A token advance is collected during registration, and the remaining course fee must be paid in full before course completion; contact the team to confirm the applicable payment and EMI options.
An AI Engineer course generally covers core concepts and technologies used to develop intelligent systems, such as machine learning, deep learning, neural networks, and AI programming tools. For the exact DataMites syllabus, including the technologies, modules, and practical components currently offered, refer to the official course curriculum or contact the team.
If you miss a live online class, contact the DataMites team to ask about available options for catching up on the missed material. Class recordings, alternative batch attendance, and other make-up arrangements should be confirmed before enrollment rather than assumed to be included.
DataMites offers learning options that prospective students can discuss with the team to find a suitable way to follow their AI training. Ask about available online batches, schedules, and applicable flexibility options before choosing your program.
The DataMites Flexi Pass allows learners to attend multiple batches of the same course during its validity period, helping them revisit topics and strengthen their understanding as needed.The pass remains valid for 3 months from the date of activation. Contact the DataMites team to confirm the applicable terms for your Artificial Intelligence course.
Beginners and professionals from non-technical backgrounds who want to explore artificial intelligence can contact DataMites to discuss whether the program suits their current knowledge and learning goals. Before enrolling, confirm whether prior programming, mathematics, or other technical knowledge is recommended or required for the selected AI Engineer course.
Refund eligibility and cancellation terms depend on the applicable DataMites enrollment policy and the circumstances of the cancellation. Contact the DataMites team before making a payment or cancelling your registration to obtain the current refund policy and confirm any applicable conditions.
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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