Regression & Forecasting for Data Scientists using Python

Course Description: This course provides comprehensive training in regression analysis and forecasting techniques for data science, emphasizing Python programming. You will master time-series analysis, forecasting, linear regression, and data preprocessing, enabling you to make data-driven decisions across industries. Learning Objectives: • Develop expertise in time series analysis, forecasting, and linear regression. • Gain proficiency in Python programming for data analysis and modeling. • Analyze the techniques for exploratory data analysis, trend identification, and seasonality handling. • Figure out various time-series models and implement them using Python. • Prepare and preprocess data for accurate linear regression modeling. • Predict and interpret linear regression models for informed decision-making. There are Four Modules in this Course: Module 1: Time-Series Analysis and Forecasting Module description: The Time-Series Analysis and Forecasting module provides a comprehensive exploration of techniques to extract insights and predict trends from sequential data. You will master fundamental concepts such as trend identification, seasonality, and model selection. With hands-on experience in leading software, they will learn to build, validate, and interpret forecasting models. By delving into real-world case studies and ethical considerations, participants will be equipped to make strategic decisions across industries using the power of time-series analysis. This module is a valuable asset for professionals seeking to harness the potential of temporal data. You will develop expertise in time series analysis and forecasting. Discover techniques for exploratory data analysis, time series decomposition, trend analysis, and handling seasonality. Acquire the skill to differentiate between different types of patterns and understand their implications in forecasting. Module 2: Time-Series Models Module description: Time-series models are powerful too