Introduction to Data Science (ML and DL ) | IDS
Created By: Training Head
What you'll learn
- Foundations of Data Science
- Python Programming for Data Science
- Data Acquisition and Cleaning
- Exploratory Data Analysis (EDA)
- Statistical Analysis with Python
- Machine Learning Fundamentals
- Supervised Learning Algorithms
- Unsupervised Learning Algorithms
- Advanced Topics
- Capstone Project
Requirements
The basic requirements for the "Introduction to Data Science (ML & DL)" course include:
- Basic Excel skills: Participants should be familiar with basic Excel functions for data manipulation and analysis.
- SQL knowledge: Understanding basic SQL queries for data extraction, joining, and aggregating data sets is essential.
- Python basics: Participants should have a foundational understanding of Python programming, including variables, data types, loops, and conditional statements.
- No prior data science knowledge required: The course is designed for beginners with no previous experience in data science, making it accessible to anyone eager to learn.
These prerequisites ensure that participants have the necessary skills to engage with the course material effectively and derive maximum benefit from the learning experience.
Course Description
Embark on a transformative journey into the world of data with our "Introduction to Data Science (ML & DL)" course. Designed for beginners, this comprehensive program covers essential topics such as Python programming, data manipulation, exploratory data analysis, and machine learning fundamentals. Participants will learn to acquire, clean, and analyze data, gaining practical skills in Python libraries like NumPy, Pandas, and Matplotlib. Advanced topics include feature engineering, deep learning basics, and model deployment using TensorFlow or PyTorch. The course culminates in a Capstone Project where learners apply their skills to solve a real-world problem, ensuring hands-on experience. With additional resources like recommended readings and industry case studies, graduates will be well-prepared for diverse career opportunities in machine learning engineering, data science, and research. Prerequisites include basic Excel, SQL, and Python skills, making it accessible to anyone eager to explore the exciting field of data science.
Course Content
Module 1: Foundations of Data Science (2 weeks)
1 lectures, 01:00:00 hrs
Module 1
Module 2: Data Manipulation and Cleaning (3 weeks)
1 lectures, 01:00:00 hrs
Module 2
Module 3: Exploratory Data Analysis (2 weeks)
1 lectures, 01:00:00 hrs
Module 3
Module 4: Machine Learning Fundamentals (4 weeks)
1 lectures, 01:00:00 hrs
Module 4
Module 5: Advanced Topics (3 weeks)
1 lectures, 01:00:00 hrs
Module 5
Module 6: Capstone Project (4 weeks)
1 lectures, 01:00:00 hrs
Module 6
Live Projects
1 lectures, 01:00:00 hrs
Module 7
Good course for beginners with basic Python knowledge. The instructors took time to explain even the small things, which helped me build confidence. I just wish there were more hands-on coding exercises.
Great content, simple delivery and very practical. I especially liked the parts on unsupervised learning and feature engineering. Live projects added a lot of value.
I was new to data science and wasn't sure I could keep up, but the content is well-explained and supportive. The modules on machine learning made the concepts much easier to understand.
I've done a few free tutorials before, but this course gave me structure and depth. The Capstone Project really tested my learning and gave me a sense of what working on a data science problem feels like.
The best thing about this course was the balance between theory and practical coding. The Python programming part was very well explained. I'm looking forward to using these skills in real-life projects.
This course gave me a solid grip on data manipulation using Pandas and NumPy. The supervised learning module was detailed and the case studies were relevant. Would recommend to anyone starting their data science journey.
Very beginner-friendly and well-structured. The instructors explained statistical analysis clearly. A few more quizzes after each module would have helped, but overall I'm satisfied.
I liked how the course started from the foundations and gradually introduced ML and DL concepts. The use of real datasets and the live projects gave me confidence to apply what I learned.
I joined this course with just basic Python knowledge, and I've learned so much. The EDA and data cleaning sections were particularly useful. Some topics like deep learning felt a bit rushed, but still a great course.
A fantastic starting point for anyone new to data science. The modules were well-paced, and I appreciated how Python was explained with practical examples. The Capstone Project was challenging but rewarding.
₹75,000.00₹106,000.00
This Course Includes:
Duration : 6 Months
Lectures : 72
Offline & Online Mode
