PYTHON TIME SERIES ANALYSIS
Learning Path | 16 Course Series
This Time Series Analysis and Forecasting with Python includes 16 courses with 57+ hours of video tutorials and One year access. You will learn about how to use Python programming in time series analysis and forecasting of data from scratch.
Offer ends in:
What you'll get
- 57+ Hours
- 16 Courses
- Course Completion Certificates
- One year access
- Self-paced Courses
- Technical Support
- Mobile App Access
- Case Studies
Synopsis
- Courses: You get access to all 16 courses in the Projects bundle. You do not need to purchase each course separately.
- Hours: 57++ Video Hours
- Core Coverage: This course's main aim is to teach students how to use Python for real forecasting and time series analysis.
- Course Validity: One year access
- Eligibility: Anyone serious about learning Time Series Analysis and Forecasting with Python
- Pre-Requisites: Basic knowledge of Python programming, Data Science, and Machine learning
- What do you get? Certificate of Completion for each of the 16 courses, Projects
- Certification Type: Course Completion Certificates
- Verifiable Certificates? Yes, you get verifiable certificates for each course with a unique link. These links can be included in your resume/LinkedIn profile to showcase your enhanced skills.
- Type of Training: Video Course – Self-Paced Learning
Content
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MODULE 1: Statistics Essentials Training
Courses No. of Hours Certificates Details Python Bootcamp 10h 33m ✔ Advanced Python for Data analysis 6h 29m ✔ Statistics Essentials with Python 3h 23m ✔ Data Science with Python 4h 14m ✔ Machine Learning using Python 3h 26m ✔ -
MODULE 2: Logistic Regression & Predictive Modeling
Courses No. of Hours Certificates Details Sales Forecasting using Time Series Analysis in Python 2h 13m ✔ House Price Prediction using Linear Regression in Python 3h 2m ✔ Predictive Analytics and Modeling with Python 8h 26m ✔ Machine Learning Python Case Study - Diabetes Prediction 1h 02m ✔ Logistic Regression & Supervised Machine Learning in Python 2h 6m ✔ -
MODULE 3: Learning from Practicals & Case Studies
Courses No. of Hours Certificates Details Linear Regression & Supervised Learning in Python 2h 28m ✔ Predicting Credit Default using Logistic Regression in Python 3h 3m ✔ Financial Analytics with Python 1h 6m ✔ -
MODULE 4: Advanced Projects based Learning
Courses No. of Hours Certificates Details AI Artificial Intelligence & Predictive Analysis with Python 6h 15m ✔ Python Case Study - Sentiment Analysis 57m ✔ Project on Tensorflow - Implementing Linear Model with Python 1h 46m ✔
Description
Time Series Analysis and Forecasting can be considered the approach concerned with leveraging past data to implement the analysis and forecasting. It can also be defined as the process or procedures implemented using programming language to help businesses determine the trend of upcoming events. To implement the concept of Time series analysis, there should be some past data available that could be leveraged to draft the solution for prediction. There is the logic associated with these concepts, which must be implemented precisely to let it work accurately. It helps businesses understand trends, which eventually helps them make critical business decisions.
Sample Certificate
Requirements
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- A few technologies are partially dependent on this course, and we will consider them as the prerequisite.
- The very first thing is a good understanding of Data Science. Since this course is focused on a topic related to data science, trainees are required to possess a good understanding of this technology. If you have good exposure to data science, you will find it very easy to work with the advanced aspects of this course.
- The next important thing is Machine learning. We have included units and projects in this course solely based on Machine learning. If you have a good understanding of the concepts that fall under the court of machine learning, then it will be very easy for you to understand the related concepts.
We have included all the topics considered prerequisites in this course to make it easy for trainees to find everything in a single place.
Target Audience
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- This course has been developed to welcome all sorts of trainees who are willing to master Time Series Analysis and Forecasting with Python.
Professionals, trainers, and students could be this course's target audience.
Developers or professionals who have good experience in Python or any other programming language and want to learn how to implement Time Series Analysis and Forecasting with Python can be the best target audience for this course. They will learn everything from an advanced perspective that will help them gain a deep understanding of the subject.
Students working in Python who want to learn about analysis and forecasting can also be the best target audience for this training. They will be able to learn things from scratch. Educators can also leverage this course to learn more so that they can best serve their trainees.
Course Ratings
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I found this course very helpful. The course was highly informative and very well presented. It was very easier to follow. Many complicated concepts were clearly explained. It improved my confidence with respect to programming skills
JIYEON CHOI
Very Informative and Well Organized Course Contents. High Quality Videos. I will recommend this course to anyone I know who interested to learn about Data Analytics.
JOSEPH WONG
I recently completed a data analytics course and found it to be an incredibly valuable learning experience. The course provided a comprehensive introduction to data analytics, covering everything from data collection and cleaning to advanced statistical analysis and data visualization. One thing I appreciated about the course was the hands-on approach to learning. Throughout the course, we worked with real datasets and used industry-standard tools such as Python, R, and Tableau to analyze and visualize the data. This gave me the practical skills and experience I needed to feel confident in my ability to work with data in a professional setting. The course instructors were knowledgeable and engaging, and they were always available to answer questions and provide feedback. The course also had a supportive and active online community, where I was able to connect with other learners and share my experiences and insights. Overall, I would highly recommend this data analytics course to an
Akram Ahmed
The Data Science Fundamentals online course that I recently completed. Overall, I found the course to be highly valuable and informative. The content was well-structured and provided a solid foundation for understanding key concepts in data science.
Priti Gajanan Patole