Course Syllabus
This course provides a comprehensive introduction to Python for data science, covering essential concepts and techniques for data analysis, visualization, and machine learning. You'll learn how to use popular Python libraries such as NumPy, pandas, matplotlib, and scikit-learn to solve real-world data problems.
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Module 1: Introduction to Python
- Setting up your Python environment
- Basic Python syntax and data types
- Control flow statements (if, for, while)
- Functions and modules
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Module 2: NumPy for Numerical Computing
- Introduction to NumPy arrays
- Array indexing and slicing
- Array operations and broadcasting
- Linear algebra with NumPy
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Module 3: pandas for Data Analysis
- Introduction to pandas DataFrames and Series
- Data loading and cleaning
- Data manipulation and transformation
- Data aggregation and grouping
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Module 4: Data Visualization with Matplotlib
- Introduction to matplotlib plots
- Creating line plots, scatter plots, and bar charts
- Customizing plot appearance
- Advanced visualization techniques
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Module 5: Introduction to Machine Learning with scikit-learn
- Overview of machine learning concepts
- Supervised learning algorithms (regression, classification)
- Unsupervised learning algorithms (clustering, dimensionality reduction)
- Model evaluation and selection
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Module 6: Real-World Data Projects
- Working with real-world datasets
- Applying data science techniques to solve business problems
- Developing data-driven insights and recommendations
- Presenting your findings effectively
Prerequisites
Basic programming knowledge is recommended but not required. A willingness to learn and a passion for data are essential.
Required Materials
All necessary software and datasets will be provided. A personal computer with internet access is required.
Grading Policy
Your final grade will be based on the following components:
- Assignments (40%)
- Midterm Exam (30%)
- Final Project (30%)
Instructor: Dr. Elena Ramirez
Dr. Elena Ramirez is a data scientist and educator with over 10 years of experience in the field. She holds a Ph.D. in Statistics from Stanford University and has worked on a wide range of data-related projects in both academia and industry. Dr. Ramirez is passionate about teaching and is committed to helping students develop the skills they need to succeed in the data science field.
Her research interests include machine learning, data mining, and statistical modeling. She has published numerous articles in leading data science journals and presented her work at international conferences.
Dr. Ramirez believes that data science is a powerful tool for solving complex problems and improving people's lives. She is excited to share her knowledge and expertise with students at NovaLearn.
Student Reviews
"This course was fantastic! Dr. Ramirez is an excellent instructor who explains complex concepts in a clear and concise manner. I learned so much and feel confident in my ability to apply Python to data science problems."
"I highly recommend this course to anyone who wants to learn Python for data science. The course content is well-structured, and the assignments are challenging but rewarding. Dr. Ramirez provides excellent support and feedback."
"NovaLearn's Python for Data Science course exceeded my expectations. The hands-on projects were invaluable, and I gained a solid understanding of data analysis techniques. Dr. Ramirez is a knowledgeable and engaging instructor."
Enroll Now
Start your data science journey today! Fill out the form below to enroll in the Python for Data Science course.
Course Details
Duration:
8 Weeks
Start Date:
January 15, 2025
Location:
Online
Price:
$999