Hacks For Efficient Time Management

January 13, 20232 Minutes Read
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Why are some people more productive than others? How can some finish so much in 24 hours? How do some people find the time to pursue their hobbies every day? When everyone is given the same number of hours, how do some accomplish more than others? The answer lies in effective time management.

What is time management?

The ability to utilise time efficiently is referred to as time management. Although time is limited, time management may make it seem like there is more of it in a day, depending on how you plan your calendar. Time management is a skill it needs to be learned and developed.

Time management to improve productivity at work:

Time is of the essence when you are dealing with multiple projects or chasing a deadline. Working smarter, not harder, should be the goal in such a scenario. It takes a lot of planning, patience, and dedication to master the art of time management . The following methods help you manage your time effectively.

  1. Set reminders: Be clear about the task and the deadline given to you. Set progress reminders so that you always focus on your goals.
  2. Daily planner : Begin your day by making a list of jobs that need to be completed. At the end of the day, tick off the tasks you have completed.
  3. Keep distractions at bay: When you are focused on working on your project, stay away from all other distractions, like avoiding the urge to check social media, discouraging unnecessary chit-chat with peers, etc.
  4. Prioritise: Prioritising your tasks helps in accomplishing them. Start with the most urgent or the toughest one, and then work your way to the least important and the easiest one.

Time management for students:

Effective time management is all about achieving a balance between your school/college work and leisure time. It is about taking control of your time and optimising it for focus and productivity. Here are some time management tips and strategies to help you get maximum time out of your day.

Plan your day : Planning your day could help you manage and control your time. It would enable you to organise your life and focus on what matters the most.

Understand your time-spending patterns : Once you understand your schedule, you can analyse how you spend your time. This will help you reduce unproductive activities and focus on the important ones.

Break down larger tasks : When you have many things to do, it's best to write them down in order of its priority. If you have a large assignment, break it down into smaller parts. Doing so helps you stay motivated and makes the task easier.

Take Breaks : Small breaks can do big wonders. Your body will let you know when these breaks are needed. It will send signals by turning sleepy, fidgeting, hungry or losing focus.Glow & Lovely Careers (formerly known as Fair & Lovely Career Foundation) can provide you with tips, courses, and guidance on effective time management.

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Career In Machine Learning

Machine Learning is the use of Artificial Intelligence to let machines learn how to do a task without being programmed to do that task specifically. (In short, machines learn on their own without any help from people!) This process starts with giving them good-quality data. The data is then used to build different machine-learning models and algorithms, which are used to train the machines. The type of data we have and the type of task we want to automate will determine which algorithms we use. Now that we know the basics of Machine Learning, let's look at how this knowledge can be used to build a career.

Machine Learning is very popular because it saves people a lot of work and makes machines work better by letting them learn independently. Because of this, there are many popular and well-paying jobs in Machine Learning, like Machine Learning Engineer, Data Scientist, NLP Scientist, etc.

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  1. ** Data Scientist:** A Data Scientist uses advanced analytics tools, such as Machine Learning and Predictive Modeling, to collect, analyse, and make sense of a lot of data in order to get insights that can be used to make decisions. The executives of the company then use these to decide how to run the business.
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  1. **Machine Learning Engineer: **A Machine Learning Engineer is an engineer who uses programming languages like Python, Java, Scala, etc., along with the right machine learning libraries to run different machine learning experiments.
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  1. ** NLP Scientist:** "NLP" stands for "natural language processing," which is the process of making machines understand how people talk. This means that one day, machines will be able to talk to people in their own language. An NLP Scientist's job is to help make a machine that can learn how people talk and translate what they say into other languages.
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  1. Business Intelligence Developer: A Business Intelligence Developer uses Data Analytics and Machine Learning to collect, analyse, and make sense of large amounts of data. They then turn this information into actionable insights that company executives can use to make business decisions. In simpler terms, it means using data to help businesses make better decisions.
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  1. **Human-Centered Machine Learning Designer: **Human-Centered Machine Learning is about Machine Learning algorithms based on people. Video rental services like Netflix are a good example of this. They let their customers choose movies based on what they like, giving them a "smart" viewing experience. This means that a Human-Centered Machine Learning Designer builds different systems that can use information processing and pattern recognition to do Human-Centered Machine Learning. This lets the machine "learn" each person's preferences without having to use complicated programmes that have to be adjusted by hand for every possible user scenario.

    All of the above five careers require some serious skills. These include Programming, Probability, Statistics, Data Modeling, Machine Learning Algorithms, System Design, etc. You can find free coursesabout many of them at Glow & Lovely Careers (formerly known as Fair & Lovely Career Foundation).