Best data science course syllabus 2025.pdfmayra0232020
The Best Data Science Course Syllabus 2025 offers a comprehensive curriculum designed to equip students with the latest skills in data analysis, machine learning, and AI. This course provides hands-on experience with real-world data sets and tools, preparing students for a career in data science.
Key Topics Include:
Introduction to Data Science and Python Programming
Data Wrangling and Cleaning with Pandas and NumPy
Statistical Analysis and Hypothesis Testing
Machine Learning Algorithms (Supervised and Unsupervised)
Deep Learning and Neural Networks
Data Visualization with Matplotlib, Seaborn, and Tableau
Working with Big Data and Cloud Platforms (AWS, Google Cloud)
Real-World Projects and Case Studies
Discover the comprehensive data science course curriculum at Quality Thought. Our curriculum is designed to equip you with the latest skills and knowledge needed to excel in the field of data science. Enroll now and start your journey towards becoming a successful data scientist.
Best Artificial Intelligence Course | Online program | certification course Learn and Build
Learn Understand and solve complex machine learning problems with programming language skills and become AI experts, explore opportunities for data engineering, AI engineering, Software engineering and a lot more. Get enrolled now, learn anywhere and get an online certification Artificial Intelligence course.
Machine Learning with Data Science Online Course | Learn and Build Learn and Build
You are just one step away from becoming a Data Scientist Engineer. Learn a foundational understanding of Machine Learning techniques at one place. Get Online Machine Learning Certification at Learn and Build.
This document provides an introductory seminar on using deep learning and LSTM methods for stock price prediction. It discusses using machine learning to analyze stock market data and predict future prices. The objectives are to accurately predict stock prices and provide a mobile app interface for visualization. Literature on existing prediction systems is reviewed and a proposed system is described using tools like Pandas, NumPy, and Scikit-learn. The system architecture involves data processing, dimension reduction, model training with LSTM, and price prediction. Potential advantages include automation and continuous improvement, while disadvantages include time/resources and error-prone models.
This document provides an introductory seminar presentation on using deep learning and LSTM methods for stock price prediction. The presentation was given by students at the Manoharbhai Patel Institute of Engineering & Technology under the guidance of Prof. OMKAR DUDBHURE. The presentation covers the objectives of stock price prediction using machine learning techniques, a literature review on existing approaches, a description of the proposed system including modules and tools, an overview of the system architecture and flowchart, advantages and disadvantages, and references. The goal is to accurately predict future stock prices using past stock market data and deep learning models like LSTM to help investors.
Experienced Machine Learning Engineer with a demonstrated history of working in the sports industry. Skilled in Data Science, Neural Networks, OpenCV, Computer Vision, and Scikit-Learn. Strong engineering professional with a Master of Technology (M.Tech.) focused in Computer Science from International Institute of Information Technology, Bhubaneswar.
Everything you need to Data Science Data Science Deep Learning In Python in Bangalore.For those looking for an in-depth experience Data Science in Python. Join myTectra
Best data analyst course syllabus 2025.pdfmayra0232020
This Data Analyst Syllabus provides a comprehensive guide for students and professionals looking to master the skills required for data analysis. This course covers essential topics in data analysis, statistics, and tools used to process, visualize, and interpret data. Perfect for beginners and those looking to enhance their analytical capabilities, the syllabus is designed to help learners understand data-driven decision-making.
Transition From Mechanical Engineering to Data Science | Tutort AcademyTutort Academy
At first look, data science and mechanical engineering appear to be separate professions, although they are interconnected in various ways. In the age of digital transformation, incorporating data science concepts and approaches into mechanical engineering has become increasingly important.
Building machine learning muscle in your team & transitioning to make them do machine learning at scale. We also discuss about Spark & other relevant technologies.
Data Science With Python | Python For Data Science | Python Data Science Cour...Simplilearn
This Data Science with Python presentation will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python. The aim of this video is to provide a comprehensive knowledge to beginners who are new to Python for data analysis. This video provides a comprehensive overview of basic concepts that you need to learn to use Python for data analysis. Now, let us understand how Python is used in Data Science for data analysis.
This Data Science with Python presentation will cover the following topics:
1. What is Data Science?
2. Basics of Python for data analysis
- Why learn Python?
- How to install Python?
3. Python libraries for data analysis
4. Exploratory analysis using Pandas
- Introduction to series and dataframe
- Loan prediction problem
5. Data wrangling using Pandas
6. Building a predictive model using Scikit-learn
- Logistic regression
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you'll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques.
Learn more at: https://www.simplilearn.com
Kubernetes and AI - Beauty and the Beast - Tobias Schneck - DOAG 24 NUE - 20....Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Keywords: AI, Container, Kubernetes, Cloud Native
This candidate has a Masters in Computer Science from Indiana University and a Bachelors in Computer Engineering from University of Pune, India. They have experience in research, development and data analysis roles using technologies like Java, Python, SQL, machine learning algorithms and data visualization tools. Their academic projects involved building classifiers and models to solve multi-label classification, recommendation and malware detection problems using techniques like feature engineering, cross-validation and hyperparameter tuning.
This document provides an introduction to a seminar on using deep learning and LSTM methods for stock price prediction. It discusses using machine learning algorithms to analyze stock market data and predict future prices. The proposed system would use tools like Pandas, NumPy, and Scikit-learn to get stock data and predict prices using an LSTM model. It presents the modules, architecture, advantages, and requirements of the system. The conclusion states that both machine learning techniques showed improved prediction accuracy compared to traditional methods, with LSTM proving more efficient.
Nitin Agrawal has over 6 years of experience working with databases including SQL Server, MySQL, PostgreSQL, and Oracle. He has experience extracting data from websites using Perl and regular expressions, monitoring sensor data with Raspberry Pi, and showcasing bibliographic data using Tableau, PHP, and SQL Server. Nitin holds an OCA Oracle certification and a B.Tech. in computer science with experience in roles including database administration, system analysis, and software engineering.
Aadit Agarwal is a student pursuing an Integrated Post Graduation in Information Technology from Indian Institute of Information Technology and Management, Gwalior. He has work experience as a Machine Learning Intern at Caliche Pvt. Ltd. and as an Embedded Systems Intern at Saccidanand U-Tech. His skills include Python, C/C++, SQL, R, TensorFlow, Keras and Scikit-Learn. He is currently working on projects involving classification of organic compounds and caffeine products using machine learning algorithms.
Containers & AI - Beauty and the Beast !?! @MLCon - 27.6.2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Keywords: AI, Containeres, Kubernetes, Cloud Native
Event Link: https://mlconference.ai/tools-apis-frameworks/containers-ai-infrastructure/
Data Science decoded- author: Rohit DubeyRohit Dubey
his book is designed for aspiring professionals
who have mastered the tools and
technologies of data science—like Python,
Machine Learning, Tableau, and more—but
sometimes struggle to articulate their
knowledge during interviews.
- Rohit Dubey (Author)
Why This Book
This book is your ultimate companion to
cracking data science interviews. It combines
technical mastery with strategic insights to
help you:
• Master Core Skills: Learn Python, SQL,
machine learning, and data visualization
tailored for interview success.
• Outsmart Interviewers: Get cunning, smart
answers to tackle tricky questions with
confidence.
• Build Your Edge: Understand behavioral
tactics and communication hacks that
make you stand out.
• Be Job-Ready: With case studies, practice
scenarios, and post-interview strategies, it’s
all you need to land your dream role.Contents:
Topic of Interview | Page no.
Python Core | 2
Machine Learning | 17
Numpy | 28
Pandas | 38
Scikit | 47
Tesorflow | 60
Machine Learning Project-1 I 72
Machine Learning Project-2 I 89
Data Analytics | 103
Data Analytics project | 116
SQL | 125
SQL PROJECT | 137
MySQL | 150
MS Excel | 163
MS Excel Project | 175Contents
Topic of Interview | Page no.
R | 186
R Project | 193
Power BI | 202
Power BI Project | 213
Tableau | 226
Tableau Project | 235
mongo DB | 246
mongo DB Project | 256
BIG DATA | 263
BIG DATA Project | 271
Data Science | 281
Data Science Project | 291
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Keywords: AI, Containeres, Kubernetes, Cloud Native
Event Link: https://meine.doag.org/events/cloudland/2024/agenda/#agendaId.4211
The document outlines an introduction to Python guest lecture covering setting up a Python development environment, Python basics syntax including variables, data types, functions and flow control, sample Python programs, and continuing your Python learning journey with additional concepts and a quiz. The lecture agenda includes explaining Python basics, demonstrating sample programs, taking questions, and clearing doubts. The speaker has 17 years of IT industry experience and is sharing their Python expertise in this lecture.
This document provides information about a Data Scientist Master's Program offered in collaboration between Simplilearn and IBM. The program aims to accelerate careers in data science through world-class training on in-demand data science and machine learning skills. It offers extensive training on Python, R, Tableau, machine learning concepts and hands-on experience with tools and technologies. The program includes courses, electives, projects, certificates and support from IBM experts to help students gain expertise in data science.
This document provides information about a Data Scientist Master's Program offered in collaboration between Simplilearn and IBM. The program aims to accelerate careers in data science through world-class training on in-demand data science and machine learning skills like Python, R, Tableau, and machine learning concepts. It consists of 5 core courses covering topics like Python, data science with Python, machine learning, Tableau, and a capstone project. It also offers electives and provides certificates, projects, mentorship, and other resources to help students learn. The program is suitable for professionals of all backgrounds looking to enter or advance in a data science career.
CSDT IT Solution is leading Computer Programming training Institute in Patna, CSDT provides best Python programming class in patna, CSDT Python computer Programming training in patna is fully based on corporate/IT industry for job purpose. CSDT is one of the best programming institute in patna. CSDT IT Solution provides live project training on Python Programming for BCA, MCA, B.Tech students and other courses students.
http://www.csdt.co.in/Python_institute_patna.htm
pulse ppt.pptx Types of pulse , characteristics of pulse , Alteration of pulsesushreesangita003
what is pulse ?
Purpose
physiology and Regulation of pulse
Characteristics of pulse
factors affecting pulse
Sites of pulse
Alteration of pulse
for BSC Nursing 1st semester
for Gnm Nursing 1st year
Students .
vitalsign
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This document provides an introductory seminar presentation on using deep learning and LSTM methods for stock price prediction. The presentation was given by students at the Manoharbhai Patel Institute of Engineering & Technology under the guidance of Prof. OMKAR DUDBHURE. The presentation covers the objectives of stock price prediction using machine learning techniques, a literature review on existing approaches, a description of the proposed system including modules and tools, an overview of the system architecture and flowchart, advantages and disadvantages, and references. The goal is to accurately predict future stock prices using past stock market data and deep learning models like LSTM to help investors.
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Best data analyst course syllabus 2025.pdfmayra0232020
This Data Analyst Syllabus provides a comprehensive guide for students and professionals looking to master the skills required for data analysis. This course covers essential topics in data analysis, statistics, and tools used to process, visualize, and interpret data. Perfect for beginners and those looking to enhance their analytical capabilities, the syllabus is designed to help learners understand data-driven decision-making.
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Data Science With Python | Python For Data Science | Python Data Science Cour...Simplilearn
This Data Science with Python presentation will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python. The aim of this video is to provide a comprehensive knowledge to beginners who are new to Python for data analysis. This video provides a comprehensive overview of basic concepts that you need to learn to use Python for data analysis. Now, let us understand how Python is used in Data Science for data analysis.
This Data Science with Python presentation will cover the following topics:
1. What is Data Science?
2. Basics of Python for data analysis
- Why learn Python?
- How to install Python?
3. Python libraries for data analysis
4. Exploratory analysis using Pandas
- Introduction to series and dataframe
- Loan prediction problem
5. Data wrangling using Pandas
6. Building a predictive model using Scikit-learn
- Logistic regression
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you'll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
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Kubernetes and AI - Beauty and the Beast - Tobias Schneck - DOAG 24 NUE - 20....Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Keywords: AI, Container, Kubernetes, Cloud Native
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This document provides an introduction to a seminar on using deep learning and LSTM methods for stock price prediction. It discusses using machine learning algorithms to analyze stock market data and predict future prices. The proposed system would use tools like Pandas, NumPy, and Scikit-learn to get stock data and predict prices using an LSTM model. It presents the modules, architecture, advantages, and requirements of the system. The conclusion states that both machine learning techniques showed improved prediction accuracy compared to traditional methods, with LSTM proving more efficient.
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As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Keywords: AI, Containeres, Kubernetes, Cloud Native
Event Link: https://mlconference.ai/tools-apis-frameworks/containers-ai-infrastructure/
Data Science decoded- author: Rohit DubeyRohit Dubey
his book is designed for aspiring professionals
who have mastered the tools and
technologies of data science—like Python,
Machine Learning, Tableau, and more—but
sometimes struggle to articulate their
knowledge during interviews.
- Rohit Dubey (Author)
Why This Book
This book is your ultimate companion to
cracking data science interviews. It combines
technical mastery with strategic insights to
help you:
• Master Core Skills: Learn Python, SQL,
machine learning, and data visualization
tailored for interview success.
• Outsmart Interviewers: Get cunning, smart
answers to tackle tricky questions with
confidence.
• Build Your Edge: Understand behavioral
tactics and communication hacks that
make you stand out.
• Be Job-Ready: With case studies, practice
scenarios, and post-interview strategies, it’s
all you need to land your dream role.Contents:
Topic of Interview | Page no.
Python Core | 2
Machine Learning | 17
Numpy | 28
Pandas | 38
Scikit | 47
Tesorflow | 60
Machine Learning Project-1 I 72
Machine Learning Project-2 I 89
Data Analytics | 103
Data Analytics project | 116
SQL | 125
SQL PROJECT | 137
MySQL | 150
MS Excel | 163
MS Excel Project | 175Contents
Topic of Interview | Page no.
R | 186
R Project | 193
Power BI | 202
Power BI Project | 213
Tableau | 226
Tableau Project | 235
mongo DB | 246
mongo DB Project | 256
BIG DATA | 263
BIG DATA Project | 271
Data Science | 281
Data Science Project | 291
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
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CSDT IT Solution is leading Computer Programming training Institute in Patna, CSDT provides best Python programming class in patna, CSDT Python computer Programming training in patna is fully based on corporate/IT industry for job purpose. CSDT is one of the best programming institute in patna. CSDT IT Solution provides live project training on Python Programming for BCA, MCA, B.Tech students and other courses students.
http://www.csdt.co.in/Python_institute_patna.htm
pulse ppt.pptx Types of pulse , characteristics of pulse , Alteration of pulsesushreesangita003
what is pulse ?
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physiology and Regulation of pulse
Characteristics of pulse
factors affecting pulse
Sites of pulse
Alteration of pulse
for BSC Nursing 1st semester
for Gnm Nursing 1st year
Students .
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What Is a P–N Junction? Learn how P-type and N-type materials join to create a diode.
Depletion Region & Biasing: See how forward and reverse bias shape the voltage–current behavior.
V–I Characteristics: Understand the curve that defines diode operation.
Real-World Uses: Discover common applications in rectifiers, signal clipping, and more.
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*Metamorphosis* is a biological process where an animal undergoes a dramatic transformation from a juvenile or larval stage to a adult stage, often involving significant changes in form and structure. This process is commonly seen in insects, amphibians, and some other animals.
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The *nervous system of insects* is a complex network of nerve cells (neurons) and supporting cells that process and transmit information. Here's an overview:
Structure
1. *Brain*: The insect brain is a complex structure that processes sensory information, controls behavior, and integrates information.
2. *Ventral nerve cord*: A chain of ganglia (nerve clusters) that runs along the insect's body, controlling movement and sensory processing.
3. *Peripheral nervous system*: Nerves that connect the central nervous system to sensory organs and muscles.
Functions
1. *Sensory processing*: Insects can detect and respond to various stimuli, such as light, sound, touch, taste, and smell.
2. *Motor control*: The nervous system controls movement, including walking, flying, and feeding.
3. *Behavioral responThe *nervous system of insects* is a complex network of nerve cells (neurons) and supporting cells that process and transmit information. Here's an overview:
Structure
1. *Brain*: The insect brain is a complex structure that processes sensory information, controls behavior, and integrates information.
2. *Ventral nerve cord*: A chain of ganglia (nerve clusters) that runs along the insect's body, controlling movement and sensory processing.
3. *Peripheral nervous system*: Nerves that connect the central nervous system to sensory organs and muscles.
Functions
1. *Sensory processing*: Insects can detect and respond to various stimuli, such as light, sound, touch, taste, and smell.
2. *Motor control*: The nervous system controls movement, including walking, flying, and feeding.
3. *Behavioral responses*: Insects can exhibit complex behaviors, such as mating, foraging, and social interactions.
Characteristics
1. *Decentralized*: Insect nervous systems have some autonomy in different body parts.
2. *Specialized*: Different parts of the nervous system are specialized for specific functions.
3. *Efficient*: Insect nervous systems are highly efficient, allowing for rapid processing and response to stimuli.
The insect nervous system is a remarkable example of evolutionary adaptation, enabling insects to thrive in diverse environments.
The insect nervous system is a remarkable example of evolutionary adaptation, enabling insects to thrive
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Classifiers
ML Algorithms
Prediction Models
Data Modeling algorithms
Project :- Developing a Prediction Modeling using
algorithms of Machine Learning.
5. Seekho Digital
India Academy
Deep Learning
Introduction to Deep Learning
Linear Classifiers
Optimization Techniques
Gradient Descent, Batch Optimization
Introduction to Neural Network
Multilayer Perceptron, Back Propagation Learning
Unsupervised Learning with Deep Network
Convolutional Neural Network, Building blocks of CNN
Tensorflow
Revisiting Gradient Descent, Momentum Optimizer
Classical Supervised Tasks with Deep Learning, Image Denoising
Generative Modeling with DL
Project :- Developing a Recommendation , Detection
System using algorithms of Deep Learning.
6. Seekho Digital
India Academy
100% Job Placement
Course Duration
Course Completion
Project Development
6 Months
4.5 Months
1.5 Months
We worked on real-Time Projects
+91 98055-81734
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