ANA5005X - Advanced Data Science Techniques
Course Description
Take your data science skills to the next level with Advanced Data Science Techniques—a course designed for professionals ready to move beyond the basics and explore the powerful tools and methods that drive real-world analytics and decision-making.
This course offers a deep dive into time series analysis and forecasting, providing you with the knowledge to identify patterns and make predictions based on historical data—an essential skill in finance, operations, and strategic planning. You’ll also be introduced to core machine learning concepts, including linear regression and classification, while using Scikit-learn, one of Python’s most widely used libraries for implementing machine learning models.
In addition, you’ll learn how to harness advanced Python libraries for data processing and manipulation, equipping you to tackle complex, large-scale datasets with confidence and precision.
This course is ideal for aspiring data scientists, analysts, and technical professionals, this course is your gateway to mastering the techniques that power modern data science careers. Whether you’re upskilling for a promotion or transitioning into a data-focused role, this course delivers the hands-on experience employers are actively seeking.
Key Benefits
- 100% online self-paced course
- Learn entry-level job skills for a career in data analytics
- No prior experience required.
- No textbooks and other materials required for purchase.
Learning Modules Include
- Unit 1 - Time series analysis and forecasting
- Unit 2 - Introduction to machine learning concepts (e.g. linear regression, classification)
- Unit 3 - Using Scikit-learn for basic machine learning tasks
- Unit 4 - Data processing with advanced Python libraries
Course Learning Outcomes
- Implement more advanced data analysis techniques, including time series analysis and data forecasting.
- Explore machine learning basics and its applications in data science.
- Utilize Python libraries for more complex data processing tasks.
Requirements to Enroll
No application is needed to enroll. All learners are eligible to enroll in the course. There are no admission requirements. It is recommended that learners have a high school diploma or GED, and have basic computer and internet knowledge.
Estimated Time to Completion
This course can be completed in 16 hours
Access Time
Learners will have access to their course learning modules for up to 12 months from the date of enrollment.
Certificate of Completion
After successful completion of all 4 course units and earning a 100% class progress, learners will be awarded an official National University Professional and Continuing Education Certificate of Completion.
Refund Policy
We at National University want all of our students to have a positive and rewarding learning experience. In the event that a student is not completely satisfied with a course, we offer a refund policy to ensure satisfaction.
To be eligible for a refund, the student must submit a written request, including a valid reason, within 9 calendar days of registering for the course. Additionally, the student must not have attempted more than 25% of the course.
To request a refund, email PACE@nu.edu and include your full name, student ID, and course name. Your reason for requesting a refund will be carefully reviewed, and we reserve the right to refuse a refund if we determine the request to be invalid.
If you meet the eligibility criteria and are approved for a refund, we will issue a refund to your original method of payment within 60 days of the refund approval.
How to Access My Course
Allow up to 24 hours after enrollment for the course to appear in Brightspace. (An error will appear if accessed before 24 hours)
Follow the steps below:
- Log in to your student portal at westraining.nu.edu
- Click “Login”, then “Student Login”
- Log in using User Name (email address) and Password
- In your PACE student portal, click on “Brightspace LMS” on the left-hand navigation panel.
- Under My Courses, click the course title to get started.
Applies Towards the Following Certificates
- Python for Data Science : Python for Data Analytics