ARTIFICIAL INTELLIGENCE CERTIFICATION AUTHORITIES

Artificial Intelligence Course Features

ARTIFICIAL INTELLIGENCE COURSE LEAD MENTORS

ARTIFICIAL INTELLIGENCE COURSE FEE IN MARATHAHALLI, BANGALORE

Live Virtual

Instructor Led Live Online

154,000
123,672

  • IABAC® & DMC Certification
  • 9-Month | 780 Learning Hours
  • 100-Hour Live Online Training
  • 10 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

92,000
73,897

  • Self Learning + Live Mentoring
  • IABAC® & DMC Certification
  • 1 Year Access To Elearning
  • 10 Capstone & 1 Client Project
  • Job Assistance
  • 24*7 Learner assistance and support

Classroom

In - Person Classroom Training

154,000
141,540

  • IABAC® & DMC Certification
  • 9-Month | 780 Learning Hours
  • 100-Hour Classroom Sessions
  • 10 Capstone & 1 Client Project
  • Cloud Lab Access
  • Internship + Job Assistance

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UPCOMING ARTIFICIAL INTELLIGENCE ONLINE CLASSES IN MARATHAHALLI

UPCOMING ARTIFICIAL INTELLIGENCE OFFLINE CLASSES IN MARATHAHALLI

BEST ARTIFICIAL INTELLIGENCE CERTIFICATIONS

The entire training includes real-world projects and highly valuable case studies.

IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.

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WHY DATAMITES FOR ARTIFICIAL INTELLIGENCE TRAINING

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SYLLABUS OF ARTIFICIAL INTELLIGENCE CERTIFICATION COURSE

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 

  • Big Data Overview
  • Five Vs of Big Data
  • What is Big Data and Hadoop
  • Introduction to Hadoop
  • Components of Hadoop Ecosystem
  • Big Data Analytics Introduction

MODULE 2: HDFS AND MAP REDUCE 

  • HDFS – Big Data Storage
  • Distributed Processing with Map Reduce
  • Mapping and reducing  stages concepts
  • Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort

MODULE 3: PYSPARK FOUNDATION 

  • PySpark Introduction
  • Spark Configuration
  • Resilient distributed datasets (RDD)
  • Working with RDDs in PySpark
  • Aggregating Data with Pair RDDs

MODULE 4: SPARK SQL and HADOOP HIVE 

  • Introducing Spark SQL
  • Spark SQL vs 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

OFFERED ARTIFICIAL INTELLIGENCE COURSES IN MARATHAHALLI

ARTIFICIAL INTELLIGENCE TRAINING COURSE REVIEWS

ABOUT ARTIFICIAL INTELLIGENCE COURSE IN MARATHAHALLI

According to a recent report, the global AI market is expected to reach $267 billion by 2027. As AI becomes an integral part of various industries, the demand for skilled professionals in this field is increasing rapidly. If you want to stay ahead of the curve, join the best artificial intelligence courses in Marathahalli at DataMites.

DataMites is a global institute for Data Science and Artificial Intelligence Courses in Bangalore. The institute is accredited by IABAC, making it one of the most reputed institutes in the city. Our faculty comprises experienced industry professionals who have a passion for teaching. With our cutting-edge curriculum and state-of-the-art facilities, you can be assured of getting the best education in AI.

At DataMites, we offer a variety of Artificial Intelligence Training in Marathahalli to suit your needs. Whether you prefer offline training in Marathahalli, online training in Marathahalli, or a combination of both, we have got you covered. We also offer AI courses with an internship in Marathahalli, giving you practical experience in the field. And for those looking for a job in AI, we offer AI courses with placement in Marathahalli, so you can jumpstart your career.

Upon completion of our Artificial Intelligence courses, you will receive an Artificial Intelligence certification in Marathahalli that will enhance your profile and give you a competitive edge in the job market. Our courses are designed to make you proficient in the latest AI technologies such as Machine Learning, Deep Learning, and Natural Language Processing. With our experienced faculty, comprehensive curriculum, and practical training, you can be assured of getting the best education in this field. Get in touch with us today to learn more about our courses and start your journey in AI.

ABOUT DATAMITES ARTIFICIAL INTELLIGENCE COURSE IN MARATHAHALLI

Artificial Intelligence can be described as the ability of machines to learn from experience, adjust to new inputs, and perform human-like tasks. It involves the development of algorithms and models that can perform tasks without explicit instructions, relying instead on patterns and inference. AI is a rapidly growing field that has the potential to transform many industries and aspects of our daily lives.

Artificial Intelligence is used in various aspects of everyday life such as social media algorithms, email filtering, virtual assistants, smart home devices, personalized advertising, navigation systems, and online customer service. AI is also used in healthcare, finance, education, and other industries to improve efficiency, accuracy, and decision-making.

Artificial Intelligence can improve decision-making by providing faster and more intelligent processing of data, enabling better analysis and insights, and facilitating predictive modeling. AI algorithms can also help identify patterns and trends that may not be apparent to humans, leading to more informed and effective decision-making.

Learning Artificial Intelligence offers several benefits such as increased career opportunities and earning potential, improved problem-solving and decision-making skills, the ability to create innovative solutions and products, and the opportunity to contribute to the development of cutting-edge technologies that can have a positive impact on society. Additionally, learning AI can be intellectually stimulating and personally fulfilling.

The target audience for Artificial Intelligence Training in Marathahalli includes individuals who are interested in pursuing a career in data science, machine learning, or AI, as well as professionals looking to upskill or reskill in these fields. The training is also suitable for entrepreneurs and business owners who want to understand how AI can help their business grow and innovate.

Completing Artificial Intelligence Training can open up several career opportunities such as AI engineer, data scientist, machine learning engineer, research scientist, business intelligence developer, and AI project manager. These roles are in high demand and offer competitive salaries and growth opportunities.

As per Glassdoor, the average AI Engineer Salary in Bangalore is INR ₹9,64,199 per year.

Learning Artificial Intelligence may not be easy, but it is definitely worth the effort. It requires time, commitment, and a willingness to learn and adapt to new concepts and technologies. However, with the availability of online resources and training programs, anyone can start learning AI at their own pace and gradually build up their skills and knowledge. Additionally, AI has a promising future, with a high demand for AI professionals in various industries, making it a worthwhile investment in one's career.

Artificial Intelligence Certification in Marathahalli can benefit professionals and students by enhancing their employability and making them more competitive in the job market. It can also provide opportunities for career advancement, as AI is becoming increasingly important across various industries. Additionally, the certification can showcase one's proficiency in AI concepts and applications, which can be valuable in securing job opportunities and building a strong professional network.

Artificial Intelligence has the potential to transform the future of technology by enabling machines to learn from data, simulate human reasoning, and make decisions. With the increasing availability of data and advancements in computing power, AI is becoming more sophisticated, leading to new possibilities in fields such as natural language processing, computer vision, and robotics. AI has the potential to revolutionize the way we live and work, making our lives easier and more efficient.

FAQ'S OF ARTIFICIAL INTELLIGENCE TRAINING IN MARATHAHALLI

Enrolling in DataMites for Artificial Intelligence training comes with several benefits, including access to a comprehensive syllabus covering topics such as machine learning, deep learning, natural language processing, and computer vision. 

Additionally, DataMites has a reputation for providing high-quality training, with experienced industry experts as instructors. Learners also have the option to choose between online and offline training, as well as courses with internships and placement opportunities. Overall, enrolling in DataMites for Artificial Intelligence training can set you on the path to a successful career in AI.

DataMites renders Artificial Intelligence Training in:

  • Artificial Intelligence Foundation
  • Artificial Intelligence Engineer
  • Artificial Intelligence Expert
  • Certified NLP Expert
  • Artificial Intelligence for Managers

Yes, you can choose the duration of your Artificial Intelligence course in Marathahalli based on your preferences and needs. DataMites offers a range of courses with different durations, ranging from 1 month to 11 months. Additionally, learners have the option to choose between online and offline training, as well as courses with internships and placement opportunities. This flexibility allows learners to customize their learning experience and achieve their career goals.

The AI Expert Course offered in Marathahalli focuses on the integration of data science into human resource management, providing a unique opportunity to leverage AI for operational excellence. With the increasing demand for AI, the course aims to equip learners with the knowledge and skills required to become an AI expert. The course curriculum emphasizes the practical application of AI, covering topics such as machine learning, deep learning, computer vision, and natural language processing, all of which are essential for understanding the integration of AI into organisational operations.

The objective of the AI Foundation Training in Marathahalli is to equip beginners with the fundamental knowledge and understanding of artificial intelligence. The course aims to provide a strong foundation in AI concepts, theories, and applications, which can then be applied in various industries. Students will learn about machine learning, deep learning, and neural networks, among other topics, and will gain practical skills that can be used in real-world scenarios.

The Artificial Intelligence Training Fee in Marathahalli varies depending on the course and mode of training chosen. The fees range from 14,000 INR to 99,000 INR.

Yes, DataMites provides artificial intelligence offline training in Marathahalli. They offer a range of courses, including AI foundation, AI engineer, and AI expert, to cater to the needs of different learners. The classroom training is designed to provide hands-on experience with real-world AI applications and technologies.

The Flexi-Pass option for Artificial Intelligence training at DataMites provides students with a three-month period to attend training sessions related to AI. This pass can be used for any queries or revisions related to previously covered topics.

Yes, DataMites provides Offline Artificial Intelligence Training in three locations in Bangalore - Kudlu Gate, BTM, and Marathahalli.

Yes, upon completing the Artificial Intelligence training from DataMites, you will be awarded an IABAC® certification, which is recognized worldwide and attests to your expertise in the field.

Yes, DataMites offers Artificial Intelligence courses with placement in Marathahalli graduates through its dedicated Placement Assistance Team (PAT), which helps them find suitable job opportunities after completing the course.

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: -

  • 1. Job connect
  • 2. Resume Building
  • 3. Mock interview with industry experts
  • 4. Interview questions

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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