Certificate in Data Analysis Deep Learning

Certificate in Data Analysis Deep Learning

Job Description

To provide learners with theoretical knowledge of data analysis and deep learning concepts, focusing on principles, structures, and methodologies that explain the foundations of modern artificial intelligence.

Objective: This course is designed to provide comprehensive knowledge of data analysis techniques integrated with deep learning concepts. It covers data preprocessing, neural networks, model training, evaluation, and deployment of deep learning algorithms to extract insights from complex datasets.

Eligibility Criteria

Criteria 1	Criteria 2	Experience	Training Qualification
SSLC	Passed	1 Year	None
12th	Passed	6 Months	None
Degree	Passed	No Experience	None

Sector

Artificial Intelligence

Certifying Bodies

National Education Training And Development

Type of Organisation

Board of Vocational Education

Learning Module In Job Role/Syllabus

Occupation Standards/Syllabus	NCO Code	Mandatory/ Optional	Duration
Introduction to Data Analysis and Deep Learning Data Preprocessing and Cleaning Exploratory Data Analysis (EDA) and Visualization Fundamentals of Machine Learning Introduction to Neural Networks Deep Learning Architectures	N/A	Mandatory	2 Months

Career Opportunities

Data Scientist
Machine Learning Engineer
Deep Learning Engineer
Data Analyst
AI Researcher
Data Engineer
Business Intelligence Analyst
Quantitative Analyst (Quant)
Natural Language Processing (NLP) Engineer
Computer Vision Engineer

Duration Hours

Theory :

75 Practical :

75 Employability Skills :

20 OJT (Mandatory) :

20

Specializations in Course

Proficiency in Programming Languages: Python, R, Java, and C++ are commonly used for data analysis and deep learning.
Statistical Analysis: A strong understanding of statistics is essential for interpreting data trends and creating predictive models.
Machine Learning Frameworks: Familiarity with tools like TensorFlow, PyTorch, Keras, Scikit-learn, and others is crucial for building models.
Data Visualization: Skills in tools like Tableau, Power BI, and Matplotlib to present data insights effectively.
Mathematical and Algorithmic Knowledge: Understanding of linear algebra, calculus, and optimization algorithms.
Knowledge of Big Data Technologies: Familiarity with Hadoop, Spark, and cloud platforms for handling large datasets.

Home

राष्ट्रीय शिक्षा प्रशिक्षण और विकास