Python Timeseries Forecasting Practice Exam

Python Timeseries Forecasting Practice Exam

About Python Timeseries Forecasting Exam

Python Timeseries Forecasting involves using statistical and machine learning techniques to predict future values of a variable based on historical data points that are ordered chronologically. This is crucial for various applications, such as predicting stock prices, forecasting sales trends, analyzing weather patterns, and monitoring website traffic. Python offers a rich ecosystem of libraries like Pandas, NumPy, Scikit-learn, and Statsmodels, which provide powerful tools for data manipulation, visualization, and model building, making it an ideal environment for time series forecasting.

Skills Required

Key skills required include:

• A strong foundation in Python programming, including data manipulation and handling with libraries like Pandas and NumPy. 
• A solid understanding of statistical concepts like time series decomposition, stationarity, and autocorrelation is crucial. 
• Familiarity with various forecasting methods, including classical methods like ARIMA and machine learning techniques like Prophet and neural networks, is also essential.
• Additionally, effective data visualization skills using libraries like Matplotlib and Seaborn are vital for exploring data patterns. 
• Domain knowledge in the specific area of application can provide valuable insights. 
• Problem-solving and critical thinking abilities are essential for analyzing time series data, identifying patterns, and selecting appropriate forecasting models.

Who Should Take the Exam?

The Python Time Series Forecasting exam would be suitable for:

• Those interested in pursuing a career in data science or data analysis and want to demonstrate their proficiency in time series forecasting techniques.
• Professionals working in fields such as finance, economics, supply chain management, meteorology, or marketing, who deal with time series data regularly, can benefit from this exam to validate their skills.
• Students who are learning time series analysis and forecasting as part of their academic curriculum.
• Professionals looking to enhance their career prospects in data science or related fields can demonstrate their expertise in time series forecasting through this exam.

Course Outline

The Python Timeseries Forecasting Exam covers the following topics - 

Domain 1. Introduction

• Introduction to Time Series Forecasting
• Meet the Instructor
• Course Overview

Domain 2. Motivation and Overview of Time Series Analysis

• Introduction to Time Series Forecasting
• Key Features of Time Series
• Types of Time Series Data
• Stages in Time Series Forecasting
• Data Manipulation Techniques for Time Series
• Data Processing for Time Series Forecasting
• Machine Learning Approaches to Forecasting
• RNN for Forecasting
• Projects to Be Covered

Domain 3. Basics of Data Manipulation in Time Series

• Module Overview
• Required Packages for Error-Free Code Execution
• Introduction to Basic Plotting and Visualization
• Overview of Time Series Parameters
• Installing Dependencies and Dataset Overview
• Data Manipulation in Python
• Data Slicing and Indexing
• Basic Visualization of a Single Time Series Feature
• Visualizing Multiple Time Series Features
• Customizing Feature Selection in Data Visualization

Domain 4. Data Processing for Time Series Forecasting

• Module Overview
• Significance of the Dataset
• Overview and Manipulation of the Dataset
• Data Preprocessing Steps
• RVT Models Overview
• Automatic Time Series Decomposition
• Trend Analysis Using Moving Average Filter
• Seasonality Comparison
• Resampling Techniques

Domain 5. Machine Learning in Time Series Forecasting

• Section Overview
• Data Preparation Techniques
• Auto-Correlation and Partial Correlation Analysis
• Splitting Data for Model Training
• Autoregression Techniques
• Implementing Autoregression in Python
• Moving Average and ARMA Models
• ARIMA Model Overview
• Implementing ARIMA in Python
• Auto ARIMA in Python

Domain 6. Recurrent Neural Networks in Time Series Forecasting

• Module Overview
• Important Model Parameters
• LSTM Models Overview
• BiLSTM Models Overview
• GRU Models Overview
• Understanding Underfitting and Overfitting
• Models for Underfitting and Overfitting
• Evaluating Models for Underfitting and Overfitting
• Dataset Preparation and Scaling Techniques
• Reshaping Datasets for Model Input

Domain 7. Project 1: COVID-19 Positive Cases Prediction Using Machine Learning Algorithms

• Project Overview
• Overview of the COVID-19 Dataset
• Correlation Analysis of the Dataset
• Checking for Missing Values and Dataset Shape
• Visualizing the Data (Area Plot)
• Analyzing Autocorrelation, Standard Deviation, and Mean
• Stationarity Check
• Implementing ARIMA

Domain 8. Project 2: Microsoft Corporation Stock Prediction Using RNNs

• Project Overview
• Data Analysis for Stock Prediction
• Data Visualization Techniques (Line and Area Plots)
• Analyzing Autocorrelation, Standard Deviation, and Mean
• Stationarity Check
• Data Preparation for Deep Learning Models
• Dividing the Dataset for Training and Testing
• Implementing and Evaluating LSTM Models
• Stock Prediction Using LSTM

Domain 9. Project 3: Birth Rate Forecasting Using RNNs with Advanced Data Analysis

• Project Overview
• Overview of the Birth Rate Dataset
• Visualizing Yearly Birth Distribution and Birth Rate Trends
• Monthly and Day-Wise Birth Distribution and Birth Rate Plots
• Visualizing Birth Rate Range Trends
• Data Manipulation for Forecasting
• Stationarity Check
• Preparing Data for Forecasting
• Scaling Data for Model Input