4.9 (15,640) reviews 154,000
90,295*
NO COST EMI : 15,049 p.m for 6 Months
*Offer valid till 11th January 2026
Instructor Led Live Online
Self Learning + Live Mentoring
In - Person Classroom Training
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
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!
Artificial Intelligence is a technology that enables machines to perform tasks requiring human-like intelligence, such as learning, reasoning, and decision-making. It operates by analyzing large amounts of data through algorithms and machine learning models to recognize patterns, make predictions, and automate processes efficiently.
The scope of Artificial Intelligence careers in India is growing rapidly, driven by the adoption of AI across sectors like healthcare, finance, and technology. Professionals can pursue roles in machine learning, data analysis, and automation, making it a promising path for future-ready talent.
The artificial intelligence course fees in India generally range from INR 50,000 to INR 2,00,000, depending on the institute, course duration, and curriculum. These programs provide comprehensive training in AI concepts, machine learning, and data-driven technologies.
The demand for artificial intelligence experts in India is increasing steadily, driven by widespread adoption of AI technologies across industries like finance, healthcare, and e‑commerce. This growth is opening up numerous career opportunities for skilled professionals in the AI field.
To choose the best Artificial Intelligence Institute in India, evaluate the curriculum quality, industry‑aligned projects, and expert faculty to ensure practical learning and career relevance. Check student reviews, placement support, and certification value to make a confident, future‑focused decision.
The field of Artificial Intelligence in India offers a wide range of career opportunities due to the growing adoption of AI technologies across industries. AI job roles include:
These roles provide opportunities to work on intelligent systems, automation, and data-driven solutions across sectors like healthcare, finance, and technology.
The salary of an artificial intelligence engineer in India, according to Glassdoor, varies by experience level:
The duration of an Artificial Intelligence course in India typically ranges from 3 months to 12 months, depending on the program type and depth of the curriculum. Short-term courses focus on fundamentals, while advanced programs cover machine learning, deep learning, and real-world AI projects.
To start an AI course in India, learners should have a basic understanding of programming languages like Python, mathematics (especially linear algebra, calculus, and statistics), and logical thinking. Familiarity with data analysis, algorithms, and problem-solving skills also helps in grasping AI concepts effectively.
Artificial intelligence courses in India are open to everyone, including those from non-technical backgrounds, as long as they have a willingness to learn programming basics and analytical concepts. Many programs offer foundational training to help beginners transition into AI, machine learning, and data-driven technologies.
The best Artificial Intelligence course to choose in India is one that offers a balanced mix of theory, hands-on projects, industry‑aligned curriculum, and strong placement support. Look for programs that cover machine learning, deep learning, and real‑world AI applications to build job‑ready skills and future growth potential.
P.G degree is not a mandatory requirement to pursue an Artificial Intelligence certification. However, a sound knowledge of Technology, Engineering, and Management domains will be an added advantage.
Machine Learning is a subset of Artificial Intelligence that focuses on teaching machines to learn from data and improve performance over time without explicit programming. While AI is the broader concept of machines performing intelligent tasks, Machine Learning specifically uses algorithms and data to enable predictive and adaptive capabilities.
Yes, Artificial Intelligence is considered a high-paying career in India, with salaries increasing significantly as professionals gain experience and expertise. Roles in AI, machine learning, and data science offer lucrative opportunities across industries like technology, healthcare, and finance
To become an Artificial Intelligence Engineer in India, start by gaining a strong foundation in programming languages like Python and R, along with mathematics, statistics, and data analysis. Next, pursue specialized AI courses, gain hands-on experience through projects, and stay updated with machine learning, deep learning, and AI frameworks to build a successful career.
Enrolling in an artificial intelligence course in India is highly beneficial as the AI sector is expanding rapidly, creating numerous career opportunities. The demand for AI professionals in India is growing at an estimated 33 % annually, highlighting a significant increase in job openings for skilled talent.
With AI adoption rising across industries like healthcare, finance, and technology, India’s AI market is set to become a multi‑billion‑dollar industry, offering strong career growth, higher earning potential, and long-term job security for trained professionals.
Yes, working professionals can learn Artificial Intelligence online in India through flexible programs that offer self-paced learning, live sessions, and project-based training. Online AI courses allow professionals to balance work and study while gaining practical skills in machine learning, deep learning, and data-driven technologies.
The market for Artificial Intelligence in India is booming and is expected to grow in the future. As AI requires the mastering of various disciplines and there are only a few who are good at all of them, the one who canto the disciplines is at a greater advantage. Career Opportunities in AI are plenty but there is a shortage of skilled AI professionals, therefore there is also a rising demand for the same. Some of the top industries in India for AI are- Banking and Finance, Information and Communication, Administration, and Support Services.
Yes, non-technical learners can join Artificial Intelligence courses in India. Many programs offer foundational training in programming, mathematics, and data concepts, enabling beginners from any background to develop skills in AI, machine learning, and intelligent systems.
A typical Artificial Intelligence course in India covers topics such as:
These topics equip learners with both theoretical knowledge and practical skills to work on intelligent systems and AI solutions.
Essential programming languages and tools for learning AI include:
These languages and tools help learners implement AI models, perform data analysis, and develop intelligent applications efficiently.
Industries in India adopting Artificial Intelligence the most include banking and finance, technology, healthcare, retail, and manufacturing. AI is driving automation, predictive analytics, and smarter decision-making, creating significant opportunities for skilled professionals across these sectors.
Common challenges faced when learning AI include:
Overcoming these challenges requires consistent practice, hands-on projects, and continuous learning.
Leading companies hiring AI professionals in India include TCS, Infosys, Wipro, HCLTech, Accenture, IBM, and global tech giants like Google, Microsoft, and Amazon. These organizations focus on AI research, machine learning, and automation, offering abundant career opportunities for skilled professionals.
The Artificial Intelligence course fees at DataMites in India are:
These options allow learners to choose the mode of training that best fits their schedule and learning style.
DataMites is a global institute that offers comprehensive courses in Artificial Intelligence. The syllabus is designed in tune with the current industry trends and helps to cater to the needs of fresh AI aspirants and experienced professionals. The Artificial Intelligence course offered by DataMites is unique in the following ways.
The Artificial Intelligence course at DataMites spans 9 months with 780 learning hours, including 100 hours of live online training. This structure ensures comprehensive coverage of AI concepts, practical applications, and hands-on experience.
The eligibility criteria for DataMites Artificial Intelligence courses are flexible, allowing learners from both technical and non-technical backgrounds. With a basic understanding of Python or analytical concepts, anyone interested can start building a career in AI and data science.
DataMites offers a prestigious Artificial Intelligence certification accredited by IABAC and NASSCOM FutureSkills, enhancing your credibility in the AI and machine learning industry. These certificates help boost your resume and career prospects in artificial intelligence roles.
The DataMites Artificial Intelligence course includes hands-on internship opportunities, allowing learners to gain practical experience with real-world AI projects. This exposure helps build a strong portfolio and prepares students for professional roles in artificial intelligence and machine learning.
Yes, DataMites offers placement assistance after completing the AI course, supporting learners with interview preparation, resume building, and job referrals. This AI course placement support helps students transition into roles in artificial intelligence and data science.
Yes, DataMites offers demo classes before enrolling in the AI course, so you can experience the teaching style and curriculum first-hand. These Artificial Intelligence demo sessions help you make an informed decision before starting your AI training.
DataMites offers multiple payment options, including debit/credit cards (Visa, MasterCard, American Express) and PayPal. After completing the payment, students receive their course materials and enrollment confirmation, along with guidance from an educational counselor to ensure a smooth start to their AI learning journey.
Yes, DataMites offers EMI options for the Artificial Intelligence course. DataMites have partnered with ShopSe, TEPL, and Bajaj Finserv to provide competitive finance options at as low as 0% interest rates with no hidden costs, allowing learners to pay the course fee conveniently in installments.
Students at DataMites get access to study materials for 6 months to 1 year, providing ample time to learn and revise Artificial Intelligence concepts effectively. This flexible access ensures learners can progress at their own pace while mastering key AI skills.
Yes, DataMites provides hands-on real-time projects in its Artificial Intelligence course, allowing students to gain practical experience. These projects help learners apply AI concepts to real-world scenarios, enhancing skills and boosting career readiness.
DataMites India offers a full refund if a cancellation is requested within one week of the course start, provided the student has attended no more than two sessions. Refunds are usually processed within 5–7 business days, and requests made after six months from enrollment are not eligible. For complete details, visit the DataMites Refund Policy page.
DataMites India’s privacy policy ensures that all personal information collected during course registration and transactions is securely stored and used only for legitimate purposes. It covers data protection, consent, third-party sharing, payment security, and cookies, giving students a safe learning experience. To know full details, Click here DataMites Privacy Policy.
At DataMites India, students can reschedule their training depending on the notice period: requests made over 7 days before the course start have a 20% fee, 3 to 7 days incur a 50% fee, and less than 3 days are not eligible. DataMites may also provide a full refund or allow rescheduling at their discretion. To know full details, Click here DataMites Rescheduling Policy.
DataMites operates several offline training centers across India, including major cities like Bangalore, Pune, Hyderabad, Chennai, Coimbatore, Mumbai, Delhi, Ahmedabad, Chandigarh, and Bhubaneswar. These centers offer classroom-based learning with hands-on practical experience and expert mentorship for courses like Artificial Intelligence and Data Science.
DataMites Bangalore headquarters is situated in Kudlu Gate at Bajrang House, 7th Mile, C-25, Bengaluru-Chennai Highway, Garvebhavi Palya, Bengaluru, Karnataka 560068.
And DataMites also have another 2 branches in Bangalore:
DataMites Pune headquarters is situated in Kharadi at Office Number 16, Second Floor, B Wing, City Vista, Downtown Road, Ashoka Nagar, Kharadi, Pune, Maharashtra 411014.
And DataMites also have another branch in Pune:
DataMites Hyderabad has two locations:
DataMites Chennai headquarters is situated in Anna Nagar at A.J. COMPLEX, 1/1, Anna Arch Road, AG Block, River View Colony, Anna Nagar, Chennai, Tamil Nadu 600040.
Chennai also has two additional branches:
The DataMites Mumbai branch is located in Andheri East at 10th Floor, Crescent Plaza, Teli Gali, Bima Nagar, Andheri East, Mumbai, Maharashtra 400069.
DataMites Patna is located at 2nd Floor, My Branch Services, Plot No. 382, Bailey Road, near RPS More, RPS Nagar, Kaliket Nagar, Patna, Bihar 801503.
The DataMites Madurai branch is situated on the 1st Floor of MyBranch, Vikashni, 760 West, 80 Feet Road, Anna Nagar, Madurai, Tamil Nadu 625020.
The DataMites Noida offline training center is located at MyBranch Services, Chandra Heights, Khasra No. 694M, 695M, 696M, Dadri Main Road, Salarpur Khadar, Sector 107, Noida, Uttar Pradesh 201304.
DataMites provides Flexi Pass, which gives you the privilege to attend unlimited batches in a year. The Flexi Pass is specific to one particular course. Therefore if you have a Flexi pass for a particular course of your choice, you will be able to attend any number of sessions of that course. It is to be noted that a Flexi pass is valid for a particular period.
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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