Time Management And Goal Planning: The Productivity Combo

Course Feature

Cost

Paid
Provider

Udemy
Certificate

Paid Certification
Language

English
Start Date

On-Demand
Learners

No Information
Duration

1.00
Instructor

Claudia Simonetto

Add to Favorites

4.5

2 Ratings

Learn the best time management strategies to get more work done in less time and achieve your goals. Master the art of goal planning and time management to maximize your results and become more productive.

Show All

Time Management And Goal Planning: The Productivity Combo

Go to class

Course Overview

❗The content presented here is sourced directly from Udemy platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [July 12th, 2023]

This course, Time Management and Goal Planning: The Productivity Combo, is designed to help students master their time and focus on the high-value actions that will lead to long-term success. Students will learn how to prioritize tasks, create a success-based routine, set a daily, weekly and monthly action plan, and get rid of bad habits that hold them back from maximizing results. They will also learn powerful time management techniques to stop procrastinating and boost their productivity. The course will also cover how to set 48-Hour Productivity from a 24-hour day, and how to create a concrete and strategic goal setting plan based on personal and professional long term goals. Additionally, the course will include exercises designed to put into practice what has been learned, as well as downloadable resources to use as a reference.

Show All

Recommended Courses

Policy Analysis Using Interrupted Time Series

5.0

Edx 288 learners

Learn More

This course, Policy Analysis Using Interrupted Time Series, provides a comprehensive introduction to the use of interrupted time series analysis and regression discontinuity designs to evaluate policies with routinely collected data. Students will gain the skills to become a go-to person in their company, government department, or academic department as the technical expert on this topic. Through the course, students will learn how to select and set up data sources, conduct statistical analysis, interpret and present results, and identify potential pitfalls. Examples from the social sciences will be used to illustrate the application of these techniques.

Sequences Time Series and Prediction

5.0

Coursera 274 learners

Learn More

This course, Sequences Time Series and Prediction, is part of the deeplearning.ai TensorFlow Specialization. It will teach you how to use TensorFlow to build time series models and apply them to real-world data. You will learn best practices for preparing time series data, and how to use RNNs and 1D ConvNets for prediction. With this course, you will gain the skills to build AI-powered algorithms that are scalable and can be applied to real-world problems.

Practical Time Series Analysis

1.5

Coursera 0 learners

Learn More

This course, Practical Time Series Analysis, is designed for people with some technical competencies who need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of their professional topics. It covers data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more. It also looks at mathematical models and graphical representations that provide insights into the data, as well as how to make forecasts. The language used is R, a free implementation of the S language. With video lectures, supporting written materials, quizzes, and discussion with fellow learners, this course is a great way to learn Time Series Analysis.

The Econometrics of Time Series Data

1.5

Coursera 127 learners

Learn More

This course will provide you with the knowledge and skills to understand and apply econometric models to time series data. You will learn about stationary and non-stationary models, and how to test for non-stationarity. You will also explore the applications of time series models, forecasting exercises, and models for changing volatility. With the help of real financial market data, you will be able to estimate and interpret the empirical autocorrelation function, cointegration equations, and ARCH(p) and GARCH(p,q) models. By the end of this course, you will be able to confidently manipulate and plot data, and perform in-sample and out-of-sample forecasting exercises.