Full Stack DATA SCIENCE & AI with Gen AI and Agentic AI

Enroll in our Full Stack DATA SCIENCE & AI with Gen AI and Agentic AI course to unlock the power of data-driven decision-making and artificial intelligence. Gain expertise in Gen AI & Agentic AI, data analysis, machine learning, and AI algorithms using tools like Python, TensorFlow, and SQL. Learn from industry professionals and work on real-world projects to enhance your skills. Join now to propel your career in Data Science and AI

Curriculum

9 Sections
0 Lessons
6 Weeks

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Section 1
Start with the Basics: Understand what Data Science is and its importance.
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Section 2
Learn Programming: Get proficient in Python (and optionally R).
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Section 3
Handle and Manipulate Data: Master Pandas and NumPy.
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Section 4
Visualize Data: Learn data visualization tools and techniques.
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Section 5
Dive into Machine Learning: Explore supervised and unsupervised learning.
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Section 6
Understand Deep Learning: Learn neural networks and advanced deep learning topics.
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Section 7
Explore NLP: Work with text data and NLP tasks.
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Section 8
Learn Data Engineering: Build ETL pipelines and manage databases.
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Section 9
Deploy and Monitor Models: Learn model deployment and maintenance.
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Real Time Expert

What is Data Science?

Data Science is a multidisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

What topics are covered in a Data Science course?

Data Collection and Cleaning

Exploratory Data Analysis (EDA)

Statistical Analysis and Hypothesis Testing

Machine Learning Algorithms (Supervised and Unsupervised)

Deep Learning and Neural Networks

Big Data Technologies (Hadoop, Spark)

Data Visualization

Natural Language Processing (NLP)

Model Deployment and Evaluation

Can I take this course if I have no prior programming experience?

Yes, many courses are designed to start from the basics and build up to advanced topics. Look for beginner-friendly courses that include introductory modules.

What is the difference between Data Science and Machine Learning?

Data Science is a broader field that encompasses data collection, cleaning, analysis, and visualization, while Machine Learning focuses specifically on developing algorithms that allow computers to learn from data and make predictions.

What tools and technologies will I learn in a Data Science course?

Programming Languages: Python, R

Data Analysis and Visualization: Pandas, Matplotlib, Seaborn

Machine Learning Libraries: Scikit-learn, TensorFlow, PyTorch

Big Data Tools: Hadoop, Spark

Database Systems: SQL, NoSQL

Data Processing: Apache Kafka, Apache Airflow

Cloud Platforms: AWS, Azure, Google Cloud Platform