Time Series Forecasting and Predictive Analytics Virtual Internship
In this virtual internship, students will develop expertise in time series analysis and forecasting techniques, including the application of machine learning models to predict future trends and make data-driven decisions. Through a series of hands-on projects, students will learn to preprocess and analyze time series data, build and evaluate forecasting models, and communicate their findings to stakeholders. Upon completion, students will be equipped with the skills to pursue careers in data science, business analytics, and predictive modeling.
Track Overview
Tasks & Milestones
Exploratory Data Analysis of Time Series
AdvancedIn this task, students will perform exploratory data analysis on a given time series dataset, identifying patterns, trends, and seasonality.
Implementing Basic Forecasting Techniques
AdvancedIn this task, students will apply basic time series forecasting techniques, such as moving averages and exponential smoothing, to make predictions on a given dataset.
Regression-based Time Series Forecasting
AdvancedIn this task, students will implement and evaluate regression-based time series forecasting models, such as linear regression and polynomial regression.
Decision Tree-based Time Series Forecasting
AdvancedIn this task, students will apply decision tree-based models, such as random forests, for time series forecasting and evaluate their performance.
Implementing RNN and LSTM for Time Series Forecasting
AdvancedIn this task, students will develop RNN and LSTM models for time series forecasting and evaluate their performance on a real-world dataset.
Optimizing Deep Learning Models for Time Series Forecasting
AdvancedIn this task, students will explore techniques for optimizing deep learning models for time series forecasting, such as hyperparameter tuning and feature engineering.
Capstone Project: Time Series Forecasting
AdvancedIn this capstone project, students will apply their knowledge of time series forecasting to a real-world problem, demonstrating their ability to analyze data, build and evaluate forecasting models, and communicate their findings.
Prerequisites
- • Proficiency in Python programming
- • Familiarity with data manipulation and analysis using libraries like Pandas
- • Basic understanding of machine learning concepts
Certificate
Certificate of Completion
Earn a certificate upon successful completion