Program profile · official sources
Advanced Time Series Analysis with Python
Advanced Time Series Analysis with Python is a technical progression for readers who already understand basic forecasting and machine learning. It compares statistical, machine-learning and deep-learning approaches in a forecasting setting. The current title differs from the retained forecasting URL, which identifies the same course rather than a second program.
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Who it is for
Python users extending time-series forecasting through statistical, machine-learning and deep-learning methods. Required preparation and access: Basic understanding of Python Fundamentals of time series forecasting Basic understanding of machine learning Familiarity with the PyData stack: Numpy, Pandas, Plotly, Sci-Kit Learn Recommended advance preparation: None.
Who should skip it
Skip this if you require complete mathematical derivations of every model. The source explicitly limits dynamic-regression mathematical detail, analytical Fourier-coefficient calculation and Fourier decomposition, the mathematical Fourier-transform breakdown, and a full VECM derivation. Those limits concern depth of explanation; the corresponding practical methods remain taught.
Facts checked 20 Oct 2026
- Price
- See pricing
As shown in the United States on 20 October 2026, the Self-study card displays USD 29/month as a monthly equivalent with annual billing, alongside a USD 36/month reference price. This is a displayed annual-billing observation, not a verified charged annual total or a one-course completion price. The captured card does not establish an offer end date. The captured course metadata also labels a paid offer in USD, but does not establish a one-course completion total. The Free plan provides previews and limited features; it does not establish full-course or certificate access. Self-study lists accredited certificates. Lifetime is a separate pay-once route with no automatic renewals and no numeric price established.
- Provider link
See price and enroll at 365 Data Science (official site) ↗
We may earn a commission. It does not change our facts.
- Time
- 6 hours headline; Lessons (6 hours). Curriculum lists 37 lessons, 43 exercises, 1 exam.
- Level
- Advanced
- Credential
- 365 Data Science course completion certificate
As checked on 20 October 2026, the provider says all acquired 365 Data Science certificates remain valid and have no expiration date. Purchased course access has a separate duration.
- Prerequisites
- Required preparation and access: Basic understanding of Python Fundamentals of time series forecasting Basic understanding of machine learning Familiarity with the PyData stack: Numpy, Pandas, Plotly, Sci-Kit Learn Recommended advance preparation: None. Platform registration and purchase require an account with accurate identity information. The terms prohibit minors from registering or purchasing and require legal capacity. Activity setup in the 20 October 2026 capture: installing Git and setting up a GitHub account are required to access some course materials. Coding tutorials use Jupyter Notebook, with Anaconda and Jupyter installation guidance for unfamiliar learners. The Git/GitHub materials-access requirement is distinct from the tutorial setup guidance; these are not additional universal admission conditions and do not establish complete software costs.
- Format
- Online self-paced course; provider lists QAS Self Study
Sources ↗
- Duration : https://365datascience.com/courses/forecasting-with-advanced-techniques-and-machine-learning/
- Official program page : https://365datascience.com/courses/forecasting-with-advanced-techniques-and-machine-learning/
- Provider subscription terms : https://365datascience.com/pricing/
- Credential details : https://365datascience.com/support/is-my-certificate-of-program-completion-still-valid/
- Credential details : https://365datascience.com/terms-of-use/
- Credential details : https://365datascience.com/certificates/
- Credential details : https://365datascience.com/support/what-is-the-exam-retake-policy/
- Credential details : https://365datascience.com/support/how-do-i-get-a-course-certificate/
- Credential details : https://365datascience.com/support/i-ve-signed-up-for-the-free-plan-do-i-get-a-certificate/
- Credential details : https://365datascience.com/support/are-the-career-track-certificate-and-course-certificate-accredited/
Course and activity access
The pricing page includes courses and accredited certificates in Self-study; the terms give paid subscription plans access to all platform study materials.. Official source Official source
Dated course facts
| Provider / format | Level / language | Individual credential | Course source date |
|---|---|---|---|
| 365 Data Science / Online self-paced course; provider lists QAS Self Study | Advanced / Not stated | Course Certificate | 2026-10-20 |
| Dated activity inventory | Course award condition |
|---|---|
| 37 lessons, 43 exercises, 1 exam; 2026-10-20 | Paid access and at least 60% on the course exam; Free previews do not include exams or certificates. |
What you actually do
The course moves among model families that a forecasting learner may need to compare, rather than using machine learning as a replacement label for every time-series method. Dynamic and statistical approaches precede machine-learning and deep-learning material. The stated Python, time-series and PyData preparation helps distinguish using the examples from starting programming from scratch. Practical harmonic-regression and Fourier use remain in scope even where detailed mathematics is excluded. Similarly, VECM is taught without a full derivation. A buyer should judge that balance against the need to apply a method or to derive it mathematically, without treating the published depth boundary as absence of the model itself.
Curriculum and workload
| Provider workload labels | Sum of listed sections | Assessment allocation |
|---|---|---|
| 6 hours headline; Lessons (6 hours). Curriculum lists 37 lessons, 43 exercises, 1 exam. | 453.0 minutes | Separately named assessment entries total 60 minutes, 13.2% of 453 listed minutes (60/453 x 100, rounded to one decimal). |
| Section | Published duration | Share of section-time sum | Source date |
|---|---|---|---|
| Intro | 6 min | 1.3% | 2026-10-20 |
| Coding Environment | 7 min | 1.5% | 2026-10-20 |
| Dynamic Models | 55 min | 12.1% | 2026-10-20 |
| Advanced Statistical Models | 106 min | 23.4% | 2026-10-20 |
| Machine Learning Models | 109 min | 24.1% | 2026-10-20 |
| Deep Learning | 85 min | 18.8% | 2026-10-20 |
| Further Notes | 25 min | 5.5% | 2026-10-20 |
| Course exam | 60 min | 13.2% | 2026-10-20 |
Preparation and access
Required preparation and access: Basic understanding of Python Fundamentals of time series forecasting Basic understanding of machine learning Familiarity with the PyData stack: Numpy, Pandas, Plotly, Sci-Kit Learn Recommended advance preparation: None. Registration or purchase on the platform requires an account containing accurate identity details. These access rules also require legal capacity and exclude minors from both registration and purchasing, independently of the course’s technical preparation. Activity setup in the 20 October 2026 capture: installing Git and setting up a GitHub account are required to access some course materials. Coding tutorials use Jupyter Notebook, with Anaconda and Jupyter installation guidance for unfamiliar learners. The Git/GitHub materials-access requirement is distinct from the tutorial setup guidance; these are not additional universal admission conditions and do not establish complete software costs.
US access prices
| Route | Amount at original source date | Billing scope | Included / limits |
|---|---|---|---|
| Platform annual-billing figure observed 2026-10-20 | USD 29/month billed annually | Platform subscription; monthly equivalent with annual billing and reference figure remain separate. No annual charge or completion total is inferred. | The dated USD 29/month annual-billing equivalent and USD 36/month reference are retained. A fixed course-completion total, verified annual charged amount, complete software/API cost and tax breakdown are not established. Distinct pricing-FAQ and terms refund/access conditions appear in the credential note. |
| Platform reference figure observed 2026-10-20 | USD 36/month reference | Platform subscription; monthly equivalent with annual billing and reference figure remain separate. No annual charge or completion total is inferred. | The dated USD 29/month annual-billing equivalent and USD 36/month reference are retained. A fixed course-completion total, verified annual charged amount, complete software/API cost and tax breakdown are not established. Distinct pricing-FAQ and terms refund/access conditions appear in the credential note. |
Observed 20 October 2026: the pricing FAQ measures refunds from the last payment and promises progress retention after cancellation with a refund; the terms use access after payment and email confirmation and permit account/data removal after paid access ends. These statements remain unresolved, with no universal renewal-refund entitlement or guaranteed retention period. A subscription display does not establish a verified annual charge, fixed completion cost or full external software/API budget.
Official course · Source 2 · Source 3 · Source 4 · Source 5 · Source 6 · Source 7 · Source 8 · Source 9 · Policy profile
Alternatives in the registry
| Program | Level / format | Dated workload | Credential | Dated price |
|---|---|---|---|---|
| Machine Learning Specialization / DeepLearning.AI and Stanford Online | Beginner; Online, self-paced three-course specialization with video lessons, code examples and graded assignments. | Publisher content: 94h58m. Publisher pacing: about 10 weeks at 5 hours/week; Coursera pacing: 2 months at 10 hours/week, with course estimates 33/34/28 hours. Different provider estimates, not a completion guarantee.; 2026-10-01 | Course completion certificate | Pro membership provides access and certificate route. Prices explicitly USD 30 per month billed monthly / USD 25 per month equivalent billed annually from program page. As shown in the United States on 1 October 2026. No fixed completion total or explicit regular/reference price is verified.; 2026-10-01 |
| Machine Learning (XCS229) / Stanford Online | Unstated; 100% online, on-demand, live: prerecorded lectures with coursework on a set schedule and live online sessions with the teaching team; instructor-paced cohort | Nov 16 - Feb 7, 2027 session listed; 10-15 hours per week (provider-listed); 10 CEU-equivalent units; 2026-10-05 | Certificate of Achievement | USD 2045. As shown in the United States on 5 October 2026, the official course page displays Tuition $2,045.00 for an individual enrollment in the Nov 16 - Feb 7, 2027 session; group pricing for five or more is by request. Taxes are not stated.; 2026-10-05 |
Before you enroll
Does this course award university credit?
The retained course record does not establish academic degree credit or a transfer arrangement with a university. Its provider certificate and professional-accreditation statements have different scopes. State boards determine acceptance of any listed CPE designation; that does not make the course a university qualification or establish acceptance by every employer or professional body.
Can I repeat the course exam after a pass?
The dated course-exam policy permits unlimited retakes, with thirty minutes of course-specific video required before every repeat attempt, whether the earlier result passed or failed. Watching every lesson is unnecessary before the first attempt. Separate career-track final-exam waiting periods do not apply here. A separate course-retake fee and academic degree credit are not established.