Data-Science Advanced Premium

Time Series Forecasting Virtual Internship

The Time Series Forecasting Virtual Internship is an advanced data science program designed to equip participants with the skills to build sophisticated forecasting models and extract valuable insights from historical data. Through a series of hands-on projects, learners will gain expertise in applying time series analysis techniques to predict future values, identify seasonal and cyclical patterns, and make data-driven decisions. This internship will cover a wide range of topics, including data preprocessing, feature engineering, model selection, and performance evaluation. By the end of the program, participants will have a strong portfolio of forecasting projects and the ability to tackle complex real-world time series problems.

weeks
5 tasks
0 enrolled
Sign In to Purchase - $49
Track price: $49.00

Track Overview

This track provides hands-on experience and real-world projects to build your skills.

Tasks & Milestones

Exploratory Data Analysis of Time Series

Advanced

Perform a comprehensive exploratory data analysis on a real-world time series dataset, identifying key characteristics and patterns.

8 hours

Implement and Evaluate ARIMA Models

Advanced

Build and optimize ARIMA models to forecast future values for a given time series dataset, and assess the model's performance.

12 hours

Implement and Evaluate Exponential Smoothing Models

Advanced

Build and optimize Exponential Smoothing models to forecast future values for a given time series dataset, and compare their performance with ARIMA models.

12 hours

Implement and Customize Prophet Models

Advanced

Build and optimize Prophet models to forecast future values for a given time series dataset, incorporating custom features and evaluating the model's performance.

12 hours

Implement and Evaluate Advanced Forecasting Techniques

Advanced

Build and optimize Neural Network and Ensemble-based forecasting models, and compare their performance with traditional time series techniques.

16 hours

Prerequisites

  • • Proficiency in Python programming
  • • Strong foundation in data analysis and machine learning concepts
  • • Experience with libraries like Pandas, NumPy, and Scikit-learn

Certificate

Certificate of Completion

Earn a certificate upon successful completion