What you'll get
- 2+ Hours
- 1 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Forecasting involves using various models and methods to accurately predict future trends.
- The forecasting process involves specific steps and often faces challenges that can undermine prediction reliability.
- Time series decomposition helps analyze data by breaking it into trend, seasonal, and irregular components.
- Simple forecasting methods provide straightforward techniques to estimate future values based on historical data.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Forecasting the Sales of the Store Using Time Series Analysis | 2h 13m | ✔ | View Curriculum |
Description
The Sales Forecasting using Time Series Analysis in Python course introduces learners to the concept of time-series data, which consists of data points recorded sequentially over a specific period. Time series analysis is a statistical method that studies sequential data to identify trends, patterns, and valuable insights. A primary goal of this analysis is to forecast future values, a process that involves extrapolation and can be complex. However, when combined with measures of uncertainty, these forecasts become highly valuable for informed decision-making.
Time series analysis involves analyzing past data within a defined timeframe to predict future trends. It relies on ordered sequences of data collected at consistent intervals. For example, airline passenger data, which records passenger counts over time, can be analyzed to identify trends and seasonality and to predict future demand.
Time series analysis holds significance across multiple domains due to the large volume of time-dependent data generated in various fields:
- Economics: It aids in budget planning, census studies, and the analysis of economic trends over time.
- Finance: It is widely used to monitor stock market fluctuations, assess market volatility, and support yield management.
- Social Science: It helps track demographic changes, such as birth and death rates, to inform social policies and programs.
- Healthcare: Epidemiologists and researchers can use it to study infection rates, track disease progression, or evaluate treatment outcomes over time. For instance, monitoring blood pressure trends can assess the effectiveness of a new drug.
- Environmental Science: It supports the analysis of environmental trends, such as rising temperatures or climate change, by examining historical data to understand long-term patterns.
Through this course, learners gain hands-on experience analyzing time-series data with Python and applying statistical techniques to produce accurate, actionable sales forecasts.
Requirements
- Learners should have a foundational understanding of statistics, mathematics, and programming concepts.
- Familiarity with basic data analysis techniques and coding practices will help in effectively following the course content.
Target Audience
- Professionals aiming to enhance their expertise and capabilities in sales analytics.
- Professionals or students looking to apply data-driven techniques to improve sales forecasting and business decisions.
