
Python for Data Science & Machine Learning
This comprehensive course is designed to introduce students to the powerful capabilities of Python for data science and machine learning. Throughout the course, participants will gain a solid foundation in Python programming, explore essential data science libraries, and learn to implement various machine learning algorithms. By combining theoretical knowledge with practical applications, this course aims to equip students with the skills necessary to analyze data, build predictive models, and make data-driven decisions.
Efficient Python Coding
Demonstrate proficiency in writing Python code for data science applications.
Data Visualization
Create insightful data visualizations using Matplotlib and Seaborn to effectively communicate findings.
Machine Learning Models
Build and implement various machine learning models, including linear regression, decision trees, and clustering algorithms.
Objective
Duration
Application Form
Documents
Important Notes
Objective
- Introduction to Python Programming: Understand the basics of Python programming, including data types, control structures, functions, and modules.
- Data Manipulation and Analysis: Learn to manipulate and analyze data using libraries such as NumPy, Pandas, and Matplotlib.
- Data Visualization: Gain proficiency in creating meaningful visualizations to effectively communicate data insights using libraries like Matplotlib and Seaborn.
- Statistical Analysis: Apply statistical techniques to interpret and analyze data, including descriptive statistics and hypothesis testing.
- Machine Learning Fundamentals: Understand the principles of machine learning and explore different types of machine learning algorithms, including supervised and unsupervised learning.
- Model Evaluation and Tuning: Learn techniques to evaluate, validate, and tune machine learning models to improve their performance.
- Practical Applications: Work on real-world projects and case studies to apply the concepts learned and gain hands-on experience in solving data science problems.
- Ethics and Best Practices: Discuss the ethical considerations and best practices in data science and machine learning.
Duration
- The duration of the course shall be of 3 months.
- Five months classroom teaching and practicles ( Saturday and Sunday )
Application Form
Obtain the application form:
- Collect the application form from the institute’s admission office.
- Fill Out the Application Form
Complete the application form with accurate and relevant details:
- Personal information (name, contact details, etc.)
- Educational background
- Program/course applied for
- Work experience (if applicable)
- Any other required information
Documents
Documents:
- Gather and submit the necessary documents along with the application form
- Photocopies of educational certificates and mark sheets
- Proof of identity (Aadhar card, passport, etc.)
- Passport-sized photographs
- Work experience certificates (if applicable)
Acceptance of Admission Offer:
- Confirm your acceptance by paying the admission fee within the stipulated time.
- Submit the necessary original documents for verification.
Enrollment and Orientation:
- Complete the enrollment process by filling out any additional forms required.
- Attend the orientation program to familiarize yourself with the institute, faculty, and curriculum.
Important Notes
- Deadlines: Adhere to all application deadlines and submission dates.
- Contact Information: For any queries, contact the admission office via phone, email, or in
person. - Updates: Regularly check the institute’s website and your email for updates regarding the
admission process.