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Professional Course in AI and Data Engineering

Design & develop Artificial Intelligence applications for business decision making with the Professional Certification in Data Engineering & AI.
  • Get Trained by Trainers from ISB, IIT & IIM
  • 60 Hours of Interactive Online Sessions
  • 300+ Hours of Practical Assignments
  • 2 Capstone Live Projects
data science & AI course reviews - 360digitmg
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data science & Ai course reviews - 360digitmg
3472 Learners
Academic Partners & International Accreditations
  • Data Engineering Course with Microsoft
  • Data Engineering certification with nasscomm
  • Data Engineering certification innodatatics
  • Data Engineering certification with SUNY
  • Data Engineering certification with NEF

"A recent market survey noted that by 2030 AI is estimated to contribute $16.1 trillion to the global economy, with 133 million jobs. Artificial Intelligence when coupled with Data Engineering is a great combination in the market with potentially high-paying jobs. The same survey also pointed out that 50 percent of human jobs will be taken by AI, leaving a huge demand for AI specialists. In the past decade, AI jobs have exponentially grown. The report also states that requirements for AI skills have drastically doubled in the last three years, with job openings in the domain up to 119%.

Professional program on AI & Data Engineering

Program Cost

INR 55,000/-

(Excluding GST)

AI & Data Engineering Training Overview

Our dual certification program in AI and Data Engineering gives you a good understanding of concepts of mathematics, statistics, calculus, linear algebra, and probability. With a deep knowledge of Data Mining and the use of Regression Analysis methods in Data Mining. How python is designed and is used to enable Data Mining, Machine Learning are also dealt with in detail. The use of NLP libraries and OpenCV to code machine learning algorithms are detailed Prime focus is on Machine Learning, deep learning, and neural networks. Feedforward and backward propagation in neural networks are described at length. The deployment of the Activation function, Loss function, the non-linear activation function is elaborated. A thorough analysis of Convolution Neural Networks (CNNs), Recurrent Neural Networks (RNNs), GANs, Reinforcement Learning, and Q learning is also facilitated in this course.

AI & Data Engineering Learning Outcomes

After the digitization of many organizations data has become a valuable asset. AI and Data Engineering are great career choices with promising career growth. With apt knowledge of cutting edge tools and understanding of where to use them is a pro while looking for a job. With individual attention provided by the experts at 360DigiTMG, the students are trained to handle the Data Engineering and AI challenges they will be facing in the job effectively. Data Engineering and AI are not single industry domains as any industry in need of AI solutions or deals with data needs AI and Data Engineers. Well, the main areas are namely: Medical Science and Artificial Intelligence professionals are in demand are Medicine, Space, Robotics, Automation, Marketing, Information management, Military activities, and many more. The primary objective of Artificial Intelligence and Data Engineering is to deliver skilled professionals by providing quality training, guiding them to implement and gain hands-on experience.

Use data generation sources
Work with tools and techniques used for the analysis of structured and unstructured data
Understand the differences between Descriptive and Predictive Analytics
Perform Text Mining to generate Customer Sentiment Analysis
Apply data-driven, Machine Learning approaches for business decision-making
Build prediction models for day-to-day applications
Perform forecasting to take proactive business decisions
Analyse texts, images and videos
Use Python libraries such as Keras, TensorFlow and OpenCV to create AI and Deep Learning solutions
Apply graphical processing units (GPUs) in Deep Learning Algorithms
Block Your Time
class room duration

60 hours

Live Sessions

assignment duration

300 hours

Assignments

project duration

2

Capstone Projects

Who Should Sign Up?
  • Those aspiring to be Data Scientists, AI experts, Business Analysts, Data Analytics developers
  • Graduates looking for a career in Data Science, Machine Learning, Forecasting, AI
  • Professionals migrating to Data Science
  • Academicians and Researchers
  • Students entering the IT industry

Professional Course on AI and Data Engineering Modules

Artificial Intelligence

This course will be the first stepping stone towards Artificial Intelligence and Deep Learning. In this module, you will be introduced to the analytics programming languages. R is a statistical programming language and Python is a general-purpose programming language. These are the most popular tools currently being employed to churn data for deriving meaningful insights.

 
  • All About 360DigiTMG & Innodatatics Inc., USA
  • Dos and Don'ts as a Participant
  • Introduction to Artificial intelligence and Deep learning
  • Course Outline, Road Map and Takeaways from the Course
  • Cross-Industry Standard Process for Data Mining
  • Artificial Intelligence Applications

Different packages can be used to build Deep Learning and Artificial Intelligence models, such as Tensorflow, Keras, OpenCV, and PyTorch. You will learn more about these packages and their applications in detail.

 

Tensorflow and Keras libraries can be used to build Machine Learning and Deep Learning models. OpenCV is used for image processing and PyTorch is highly useful when you have no idea how much memory will be required for creating a Neural Network Model.

 
  • Introduction to Deep Learning libraries – Torch, Theono, Caffe, Tensorflow, Keras, OpenCV and PyTorch
  • Deep dive into Tensorflow, Keras, OpenCV and PyTorch
  • Introduction to Anaconda, R, R studio, Jupyter and Spyder
  • Environment Setup and Installation Methods of Multiple Packages

Understand the types of Machine Learning Algorithms. Learn about the life cycle and the detailed understanding of each step involved in the project life cycle. The CRISP-DM process is applied in general for Data Analytics /AI projects. Learn about CRISP-DM and the stages of the project life cycle in-depth.

 

You will also learn different types of data, Data Collection, Data Preparation, Data Cleansing, Feature Engineering, EDA, Data Mining and various Error Functions. Understand about imbalanced data handling techniques and algorithms.

 
  • Introduction to Machine Learning
  • Machine Learning and its types - Supervised Learning, Unsupervised Learning, Reinforcement Learning, Semi-supervised Learning, Active Learning, Transfer Learning, Structured Prediction
  • Understand Business Problem – Business Objective & Business Constraints
  • Data Collection - Surveys and Design of Experiments
  • Data Types namely Continuous, Discrete, Categorical, Count, Qualitative, Quantitative and its identification and application
  • Further classification of data in terms of Nominal, Ordinal, Interval & Ratio types
  • Balanced vs Imbalanced datasets
  • Cross-Sectional vs Time Series versus Panel / Longitudinal Data
  • Batch Processing versus Real-Time Processing
  • Structured vs Unstructured vs Semi-Structured Data
  • Big vs Not-Big Data
  • Data Cleaning / Preparation - Outlier Analysis, Missing Values Imputation Techniques, Transformations, Normalization / Standardization, Discretization
  • Sampling Techniques for Handling Balanced vs Imbalanced Datasets
  • Measures of Central Tendency & Dispersion
    • Mean/Average, Median, Mode
    • Variance, Standard Deviation, Range
  • Various Graphical Techniques to Understand Data
    • Bar Plot
    • Histogram
    • Boxplot
    • Scatter Plot
  • Feature Engineering - Feature Extraction & Feature Selection
  • Error Functions - Y is Continuous -  Mean Error, Mean Absolute Deviation, Mean Squared Error, Mean Percentage Error, Root Mean Squared Error, Mean Absolute Percentage Error
  • Error Functions - Y is Discrete - Cross Table, Confusion Matrix, Binary Cross Entropy & Categorical Cross-Entropy
  • Machine Learning Projects Strategy

Maximize or minimize the error rate using Calculus. Learn to find the best fit line using the linear least-squares method. Understand the gradient method to find the minimum value of a function where a closed-form of the solution is not available or not easily obtained.

 

Under Linear Algebra, you will learn sets, function, scalar, vector, matrix, tensor, basic operations and different matrix operations. Under Probability one will learn about Uniform Distribution, Normal Distribution, Binomial Distribution, Discrete Random Variable, Cumulative Distribution Function and Continuous Random Variables.

 
  • Optimizations - Applications
  • Foundations - Slope, Derivatives & Tangent
  • Derivatives in Optimization
  • Maxima & Minima - First Derivative Test, Second Derivative Test, Partial Derivatives, Cross Partial Derivatives, Saddle Point, Determinants, Minor and Cofactor
  • Linear Regression Ordinary Least Squares using Calculus

You will have a high-level understanding of the human brain, importance of multiple layers in the Neural Network, extraction of features layers wise, composition of the data in Deep Learning using an image, speech and text.

 

You will briefly understand feature extraction using SIFT/HOG for images, Speech recognition and feature extraction using MFCC and NLP feature extraction using parse tree syntactic.

 

Introduction to neurons, which are connected to weighted inputs, threshold values, and an output. You will understand the importance of weights, bias, summation and activation functions.

 
  • Human Brain – Introduction to Biological & Artificial Neuron
  • Compositionality in Data – Images, Speech & text
  • Mathematical Notations
  • Introduction to ANN
  • Neuron, Weights, Activation function, Integration function, Bias and Output

Learn about single-layered Perceptrons, Rosenblatt’s perceptron for weights and bias updation. You will understand the importance of learning rate and error. Walkthrough a toy example to understand the perceptron algorithm. Learn about the quadratic and spherical summation functions. Weights updating methods - Windrow-Hoff Learning Rule & Rosenblatt’s Perceptron.

 
  • Introduction to Perceptron
  • Introduction to Multi-Layered Perceptron (MLP)
  • Activation functions – Identity Function, Step Function, Ramp Function, Sigmoid Function, Tanh Function, ReLU, ELU, Leaky ReLU & Maxout
  • Back Propagation Visual Demonstration
  • Network Topology – Key characteristics and Number of layers
  • Weights Calculation in Back Propagation

Understand the difference between perception and MLP or ANN. Learn about error surface, challenges related to gradient descent and the practical issues related to deep learning. You will learn the implementation of MLP on MNIST dataset - multi-class problem, IMDB dataset - binary classification problem, Reuters dataset - single labelled multi-class classification problem and Boston Housing dataset - Regression Problem using Python and Keras.

 
  • Error Surface – Learning Rate & Random Weight Initialization
  • Local Minima issues in Gradient Descent Learning
  • Is DL a Holy Grail? Pros and Cons
  • Practical Implementation of MLP/ANN in Python using Real Life Use Cases
  • Segregation of Dataset - Train, Test & Validation
  • Data Representation in Graphs using Matplotlib
  • Deep Learning Challenges – Gradient Primer, Activation Function, Error Function, Vanishing Gradient, Error Surface challenges, Learning Rate challenges, Decay Parameter, Gradient Descent Algorithmic Approaches, Momentum, Nestrov Momentum, Adam, Adagrad, Adadelta & RMSprop
  • Deep Learning Practical Issues – Avoid Overfitting, DropOut, DropConnect, Noise, Data Augmentation, Parameter Choices, Weights Initialization (Xavier, etc.)

You will learn image processing techniques, noise reduction using moving average methods, different types of filters - smoothing the image by averaging, Gaussian filter and the disadvantages of correlation filters. You will learn about different types of filters, boundary effects, template matching, rate of change in the intensity detection, different types of noise, image sampling and interpolation techniques.

 
  • Introduction to Vision
  • Importance of Image Processing
  • Image Processing
  • Image Transformation, Image Processing Operations & Simple Point Operations
  • Noise Reduction – Moving Average & 2D Moving Average
  • Image Filtering – Linear & Gaussian Filtering
  • Disadvantage of Correlation Filter
  • Introduction to Convolution
  • Boundary Effects – Zero, Wrap, Clamp & Mirror
  • Image Sharpening
  • Template Matching
  • Edge Detection – Image filtering, Origin of Edges, Edges in images as Functions, Sobel Edge Detector
  • Effect of Noise
  • Laplacian Filter
  • Smoothing with Gaussian
  • LOG Filter – Blob Detection
  • Noise – Reduction using Salt & Pepper Noise using Gaussian Filter
  • Nonlinear Filters
  • Bilateral Filters
  • Canny Edge Detector - Non Maximum Suppression, Hysteresis Thresholding
  • Image Sampling & Interpolation – Image Sub Sampling, Image Aliasing, Nyquist Limit, Wagon Wheel Effect, Down Sampling with Gaussian Filter, Image Pyramid, Image Up Sampling
  • Image Interpolation – Nearest Neighbour Interpolation, Linear Interpolation, Bilinear Interpolation & Cubic Interpolation
  • Introduction to the deep neural networks module
  • Modules of Self-driving cars

Convolution Neural Networks are the class of Deep Learning networks which are mostly applied on images. You will learn about ImageNet challenge, an overview on ImageNet winning architectures, applications of CNN, problems of MLP with the huge dataset.

 

You will understand convolution of filter on images, basic structure on the convent, details about Convolution layer, Pooling layer, Fully Connected layer, Case study of AlexNet and few of the practical issues of CNN.

 
  • ImageNet Challenge – Winning Architectures, Difficult Vision Problems & Hierarchical Approach
  • Parameter Explosion with MLPs
  • Convolution Networks - 1D ConvNet, 2D ConvNet, Transposed Convolution
  • Convolution Layers with Filters and Visualizing Convolution Layers
  • Pooling Layer, Padding, Stride
  • Transfer Learning - VGG16, VGG19, Resnet, GoogleNet, LeNet, etc.
  • Practical Issues – Weight decay, Drop Connect, Data Manipulation Techniques & Batch Normalization

You will be able to build the models using Faster RCNN and YOLO. Understand why fast YOLO is a better choice while dealing with object detection. You will also be able to understand the model optimization using OpenVINO

 
  • R-CNN
  • Fast R-CNN
  • Faster R-CNN
  • Mask R-CNN
  • YOLO and Types of YOLO
  • OpenVINO Toolkit
    • Introduction
    • Model Optimization of pre-trained models
    • Inference Engine and Deployment process

Understand the language models for next word prediction, spell check, mobile auto-correct, speech recognition, and machine translation. You will learn the disadvantages of traditional models and MLP. Deep understanding of the architecture of RNN, RNN language model, backpropagation through time, types of RNN - one to one, one to many, many to one and many to many along with different examples for each type.

 
  • Introduction to Adversaries
  • Language Models – Next Word Prediction, Spell Checkers, Mobile Auto-Correction, Speech Recognition & Machine Translation
  • Traditional Language model
  • Disadvantages of MLP
  • Introduction to State & RNN cell
  • Introduction to RNN
  • RNN language Models
  • Back Propagation Through time
  • RNN Loss Computation
  • Types of RNN – One to One, One to Many, Many to One, Many to Many
  • Introduction to the CNN and RNN
  • Combining CNN and RNN for Image Captioning
  • Architecture of CNN and RNN for Image Captioning
  • Bidirectional RNN
  • Deep Bidirectional RNN
  • Disadvantages of RNN

Understand and implement Long Short-Term Memory, which is used to keep the information intact, unless the input makes them forget. You will also learn the components of LSTM - cell state, forget gate, input gate and the output gate along with the steps to process the information. Learn the difference between RNN and LSTM, Deep RNN and Deep LSTM and different terminologies. You will apply LSTM to build models for prediction.

 
  • Introduction to LSTM – Architecture
  • Importance of Cell State, Input Gate, Output Gate, Forget Gate, Sigmoid and Tanh
  • Mathematical Calculations to Process Data in LSTM
  • RNN vs LSTM - Bidirectional vs Deep Bidirectional RNN
  • Deep RNN vs Deep LSTM

Gated Recurrent Unit, a variant of LSTM solves this problem in RNN. You will learn the components of GRU and the steps to process the information.

 
  • Introduction to GRU
  • Architecture & Gates - Update Gate, Reset Gate, Current Memory Content, Final Memory at Current Timestep
  • Applications of GRUs

You will learn something beyond the sequential models, learn to inspect and monitor deep learning models using Keras call-backs and TensorBorad.

 
  • Introduction to the functional API
  • Multi-input Models
  • Multi-output Models
  • Directed acyclic graphs of layers
  • Using callbacks to act on a model during training
  • Introduction to TensorBoard: the TensorFlow Visualization Framework
  • Hyperparameter Optimization
  • Model ensembling

You will understand the advancements in LSTMs, learn about the language translation models, attention mechanism, transformer architecture and its implementation.

 
  • Introduction to LSTMs disadvantages
  • Seq2Seq model and its components
  • Implementation of Seq2Seq
  • Introduction to transformers
  • Architecture of Transformers
  • Types of Transformers

Learn to Build a chatbot using generative models and retrieval models. We will understand RASA open-source and BERT to build chatbots.

 
  • Introduction to Chatbot
  • NLP Implementation in Chatbot
  • Integrating and implementing Neural Networks Chatbot
  • Train Q and A BERT to build a chatbot

Learn to Build a speech to text and text to speech models. You will understand the steps to extract the structured speech data from a speech, convert that into text. Later use the unstructured text data to convert into speech.

 
  • Speech Recognition Pipeline
  • Phonemes
  • Pre-Processing
  • Acoustic Model
  • Deep Learning Models
  • Decoding

You will learn Q-learning which is a type of reinforcement learning, exploiting using the creation of a Q table, randomly selecting an action using exploring and steps involved in learning a task by itself.

 
  • Reinforcement Learning
  • Deep Reinforcement Learning vs Atari Games
  • Maximizing Future Rewards
  • Policy vs Values Learning
  • Balancing Exploration With Exploitation
  • Experience Replay, or the Value of Experience
  • Q-Learning and Deep Q-Network as a Q-Function
  • Improving and Moving Beyond DQN
  • Keras Deep Q-Network

You will learn about the components of Autoencoders, steps used to train the autoencoders to generate spatial vectors, types of autoencoders and generation of data using variational autoencoders. Understanding the architecture of RBM and the process involved in it.

 
  • Intuition
  • Introduction to Architecture of Autoencoders
  • Comparison with other Encoders (MP3 and JPEG)
  • Introduction to Deep Autoencoders
  • Deep Autoencoders
  • Convolutional Autoencoders
  • Variational Autoencoders
  • Implementation of Autoencoders

Understanding the generation of data using GAN, the architecture of the GAN - encoder and decoder, loss calculation and backpropagation, advantages and disadvantages of GAN.

 
  • Introduction to Generative Adversarial Networks (GANS)
  • Building Model
  • Model Inputs and Hyperparameters
  • Defining the Generator and Discriminator
  • Model Training
  • Model Optimizer
  • Discriminator and Generator Losses
  • Implementation of GAN
  • Types of GANs
    • Deep Convolution GAN
    • Conditional GAN
    • Info GAN
    • SRGAN
    • 3D-GAN

Understand the unsupervised neural network models and the process involved in it.

 
  • Introduction to Boltzmann Machines
  • Energy Function
  • Introduction to Restricted Boltzmann Machine
  • Training process of RBM
  • Loss functions
  • Introduction to Deep Belief Networks
  • Applications of DBN

Learn the tools which automatically analyzes your data and generates candidate model pipelines customized for your predictive modeling problem.

 
  • AutoML Methods
    • Meta-Learning
    • Hyperparameter Optimization
    • Neural Architecture Search
    • Network Architecture Search
  • AutoML Systems
    • MLBox
    • Auto-Net 1.0 & 2.0
    • Hyperas
  • AutoML on Cloud - AWS
    • Amazon SageMaker
    • Sagemaker Notebook Instance for Model Development, Training and Deployment
    • XG Boost Classification Model
    • Training Jobs
    • Hyperparameter Tuning Jobs
  • AutoML on Cloud - Azure
    • Workspace
    • Environment
    • Compute Instance
    • Compute Targets
    • Automatic Featurization
    • AutoML and ONNX
  • AutoML on Cloud - GCP
    • AutoML Natural Language
    • Performing Document Classification
    • AutoML Version API's for Image Classification
    • Performing Sentiment Analysis using AutoML Natural Language API
    • Tensor-Flow Models Using Cloud ML Engine
    • Cloud ML Engine and Its Components
    • Training and Deploying Applications on Cloud ML Engine
    • Choosing Right Cloud ML Engine for Training Jobs

Learn the methods and techniques which can explain the results and the solutions obtained by using deep learning algorithms.

 
  • Introduction to XAI - Explainable Artificial Intelligence
  • Why do we need it?
  • Levels of Explainability
    • Direct Explainability
      • Simulatability
      • Decomposability
      • Algorithmic Transparency
    • Post-hoc Explainability
      • Model-Agnostic Algorithms
        • Explanation by simplification (Local Interpretable Model-Agnostic Explanations (LIME))
        • Feature relevance explanation
          • SHAP
          • QII
          • SA
          • ASTRID
          • XAI
        • Visual Explanations
  • General AI vs Symbolic Al vs Deep Learning
Data Engineering
  • Intro to Data Engineering
  • Data Science vs Data Engineering
  • Building Data Engineering Infrastructure
  • Working with Databases and various File formats (Data Lakes)
    • SQL
      • MySQL
      • PostgreSQL
    • NoSQL
      • MongoDB
      • HBase
      • Apache Cassandra
    • Cloud Sources
      • Microsoft Azure SQL Database
      • Amazon Relational Database Service
      • Google Cloud SQL
      • IBM Db2 on Cloud
  • Extra-Load, Extract-Load-Transform, or Extract-Transform-Load paradigms
  • Preprocessing, Cleaning, and Transforming Data
  • Cloud Data Warehouse Service
    • AWS: Amazon Redshift
    • GCP: Google Big Query
    • IBM: Db2 Warehouse
    • Microsoft: Azure SQL Data Warehouse
  • Distributed vs. Single Machine Environments
  • Distributed Framework - Hadoop
    • Various Tools in Distributed Framework to handle BigData
      • HBase
      • Kafka
      • Spark
      • Apache NiFi
    • Distributed Computing on Cloud
      • ML and AI platforms on Cloud
  • Databases and Pipelines
    • Data Pipeline
      • Features of Pipelines
      • Building a pipeline using NiFi
  • Installing and Configuring the NiFi Registry
    • Using the Registry in NiFi
    • Versioning pipelines
    • Monitoring pipelines
    • Monitoring NiFi using GUI
    • Using Pything with the NiFi REST API
  • Building pipelines in Apache Airflow
    • Airflow boilerplate
    • Run the DAG
    • Run the data pipelines
  • Deploy and Monitor Data Pipelines
    • Production Data Pipeline
    • Creating Databases
  • Data Lakes
    • Populating a data lake
    • Reading and Scanning the data lake
    • Insert and Query a staging database
  • Building a Kafka Cluster
  • Setup Zookeeper and Kafka Cluster
  • Configuring and Testing Kafka Cluster
  • Streaming Data with Apache Kafka
  • Data Processing with Apache Spark
  • Real-Time Edge Data with MiNiFi, Kafka, and Spark

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Professional Course on AI and Data Engineering Our Panel of Coaches

Artificial Intelligence & Deep Learning Course Training -360digitmg

Bharani Kumar Depuru

  • Areas of expertise: Data analytics, Digital Transformation, Industrial Revolution 4.0
  • Over 18+ years of professional experience
  • Trained over 2,500 professionals from eight countries
  • Corporate clients include Deloitte, Hewlett Packard Enterprise, Amazon, Tech Mahindra, Cummins, Accenture, IBM
  • Professional certifications - PMP, PMI-ACP, PMI-RMP from Project Management Institute, Lean Six Sigma Master Black Belt, Tableau Certified Associate, Certified Scrum Practitioner, (DSDM Atern)
  • Alumnus of Indian Institute of Technology, Hyderabad and Indian School of Business
Read More >
 
Artificial Intelligence & Deep Learning Course Training -360digitmg

Sharat Chandra Kumar

  • Areas of expertise: Data sciences, Machine learning, Business intelligence and Data
  • Trained over 1,500 professional across 12 countries
  • Worked as a Data scientist for 18+ years across several industry domains
  • Professional certifications: Lean Six Sigma Green and Black Belt, Information Technology Infrastructure Library
  • Experienced in Big Data Hadoop, Spark, NoSQL, NewSQL, MongoDB, Python, Tableau, Cognos
  • Corporate clients include DuPont, All-Scripts, Girnarsoft (College-, Car-) and many more
Read More >
 
Artificial Intelligence & Deep Learning Course Training - 360digitmg

Bhargavi Kandukuri

  • Areas of expertise: Business analytics, Quality management, Data visualisation with Tableau, COBOL, CICS, DB2 and JCL
  • Electronics and communications engineer with over 19+ years of industry experience
  • Senior Tableau developer, with experience in analytics solutions development in domains such as retail, clinical and manufacturing
  • Trained over 750+ professionals across the globe in three years
  • Worked with Infosys Technologies, iGate, Patni Global Solutions as technology analyst
Read More >
 
Data Engineering certification - 360digitmg
data science using python & r certification - 360digitmg

Certificate

Win recognition for your skills with the Data Engineering Certification. Stand out in this emerging yet competitive field with our certification.

Alumni Speak

Pavan Satya

"The training was organised properly, and our instructor was extremely conceptually sound. I enjoyed the interview preparation, and 360DigiTMG is to credit for my successful placement.”

Pavan Satya

Senior Software Engineer

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Chetan Reddy

"Although data sciences is a complex field, the course made it seem quite straightforward to me. This course's readings and tests were fantastic. This teacher was really beneficial. This university offers a wealth of information."

Chetan Reddy

Data Scientist

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Santosh Kumar

"The course's material and infrastructure are reliable. The majority of the time, they keep an eye on us. They actually assisted me in getting a job. I appreciated their help with placement. Excellent institution.”

Santosh Kumar

Business Intelligence Analyst

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Kadar Nagole

"Numerous advantages of the course. Thank you especially to my mentors. It feels wonderful to finally get to work.”

Kadar Nagole

Data Scientist

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Gowtham R

"Excellent team and a good atmosphere. They truly did lead the way for me right away. My mentors are wonderful. The training materials are top-notch.”

Gowtham R

Data Engineer

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Wan Muhamad Taufik

"The instructors improved the sessions' interactivity and communicated well. The course has been fantastic.”

Wan Muhamad Taufik

Associate Data Scientist

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Venu Panjarla

"The instructors went above and beyond to allay our fears. They assigned us an enormous amount of work, including one very difficult live project. great location for studying.”

Venu Panjarla

AVP Technology

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FAQs forProfessional Course on AI and Data Engineering Course

After you have completed the online sessions, you will receive assignments through the online Learning Management System that you can access at your convenience. You will need to complete the assignments in order to obtain your data scientist certificate.

Different organisations use different terms for data professionals. You will sometimes find these terms being used interchangeably. Though there are no hard rules that distinguish one from another, you should get the role descriptions clarified before you join an organisation.

With growing demand, there is a scarcity of Data Science Professionals in the market. If you can demonstrate strong knowledge of Data Science concepts and algorithms, then there is a high chance for you to be able to make a career in this profession.

360DigiTMG provides internship opportunities through Innodatatics, our USA-based consulting partner, for deserving participants to help them gain real-life experience. This greatly helps students to bridge the gap between theory and practical.

While there are a number of roles pertaining to Data Professionals, most of the responsibilities overlap. However, the following are some basic job descriptions for each of these roles.

 

As a Data Analyst, you will be dealing with Data Cleansing, Exploratory Data Analysis and Data Visualisation, among other functions. The functions pertain more to the use and analysis of historical data for understanding the current state.

 

As a Data Scientist, you will be building algorithms to solve business problems using statistical tools such as Python, R, SAS, STATA, Matlab, Minitab, KNIME, Weka etc. A Data Scientist also performs predictive modelling to facilitate proactive decision-making.

 

A Data Engineer primarily does programming using Spark, Python, R etc. It often compliments the role of a Data Scientist.

 

A Data Architect has a much broader role that involves establishing the hardware and software infrastructure needed for an organisation to perform Data Analysis. They help in selecting the right database, servers, network architecture, GPUs, cores, memory, hard disk etc.

In this blended programme, you will be attending 300 hours of online sessions of 6 months. After completion, you will have access to the online Learning Management System for another three months for recorded videos and assignments. The total duration of assignments to be completed online is 300+ hour. Besides this, you will be working on 2+2 live projects.

There are plenty of jobs available for data professionals. Once you complete the training, assignments and the live projects, we will send your resume to the organisations with whom we have formal agreements on job placements.

We also conduct webinars to help you with your resume and job interviews. We cover all aspects of post-training activities that are required to get a successful placement.

If you miss a class, we will arrange for a recording of the session. You can then access it through the online Learning Management System.

We assign mentors to each student in this programme. Additionally, during the mentorship session, if the mentor feels that you require additional assistance, you may be referred to another mentor or trainer.

No, the cost of the certificate is included in the programme package.

Jobs in the field of Artificial Intelligence and Data Engineering

Jobs in the field of Artificial Intelligence and Data Engineering

AI has a great influence in today’s industry, creating a wide spectrum of artificial intelligence career paths. An average growth rate of an AI expert is 74%. Al prevails in education, healthcare, agriculture. For grabbing a job in the AI field, you need to master the skills such as Python, NLP (Neuro-Linguistic Programming), Machine Learning, and a few more. Some big companies like Aditya Birla are giving a 200 % hike if you complete a professional course in AI and Data Engineering.

Salaries in India for Artificial Intelligence and Data Engineering

Salaries in India for Artificial Intelligence and Data Engineering

AI itself has varied job roles and each job role has its pay package. An entry-level AI salary in India for almost 40 percent is Rs6,00,000 per annum while mid and senior-level AI salary is 50,00,000.

Artificial Intelligence and Data Engineering Projects

Artificial Intelligence and Data Engineering Projects

AI is growing multifold along with Data, to manage and untangle the nonsense data and to make data beneficial both government and private sectors have outsourced many projects. The majority of these projects are undertaken by multinational companies, with a lookout for AI and Data Engineering experts.

Role of Open-Source Tools in Artificial Intelligence and Data Engineering

Role of Open-Source Tools in Artificial Intelligence and Data Engineering

 

Modes of training in Artificial Intelligence and Data Engineering course

Modes of training in Artificial Intelligence and Data Engineering course

 

Industry applications of Artificial Intelligence and Data Engineering course

Industry applications of Artificial Intelligence and Data Engineering course

 

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