The Everything Guide To Study Skills Pdf

[Rachelle Kun]

This handout offers several tips on effective studying. Implementing these tips into your regular study routine will help you to efficiently and effectively learn course material. Experiment with them and find some that work for you.

Think of reading as an important part of pre-studying, but learning information requires actively engaging in the material (Edwards, 2014). Active engagement is the process of constructing meaning from text that involves making connections to lectures, forming examples, and regulating your own learning (Davis, 2007). Active studying does not mean highlighting or underlining text, re-reading, or rote memorization. Though these activities may help to keep you engaged in the task, they are not considered active studying techniques and are weakly related to improved learning (Mackenzie, 1994).

Organization and planning will help you to actively study for your courses. When studying for a test, organize your materials first and then begin your active reviewing by topic (Newport, 2007). Often professors provide subtopics on the syllabi. Use them as a guide to help organize your materials. For example, gather all of the materials for one topic (e.g., PowerPoint notes, text book notes, articles, homework, etc.) and put them together in a pile. Label each pile with the topic and study by topics.

For example, you may do a few problems per day in math rather than all of them the hour before class. In history, you can spend 15-20 minutes each day actively studying your class notes. Thus, your studying time may still be the same length, but rather than only preparing for one class, you will be preparing for all of your classes in short stretches. This will help focus, stay on top of your work, and retain information.

In addition to learning the material more deeply, spacing out your work helps stave off procrastination. Rather than having to face the dreaded project for four hours on Monday, you can face the dreaded project for 30 minutes each day. The shorter, more consistent time to work on a dreaded project is likely to be more acceptable and less likely to be delayed to the last minute. Finally, if you have to memorize material for class (names, dates, formulas), it is best to make flashcards for this material and review periodically throughout the day rather than one long, memorization session (Wissman and Rawson, 2012). See our handout on memorization strategies to learn more.

Not all studying is equal. You will accomplish more if you study intensively. Intensive study sessions are short and will allow you to get work done with minimal wasted effort. Shorter, intensive study times are more effective than drawn out studying.

In fact, one of the most impactful study strategies is distributing studying over multiple sessions (Newport, 2007). Intensive study sessions can last 30 or 45-minute sessions and include active studying strategies. For example, self-testing is an active study strategy that improves the intensity of studying and efficiency of learning. However, planning to spend hours on end self-testing is likely to cause you to become distracted and lose your attention.

On the other hand, if you plan to quiz yourself on the course material for 45 minutes and then take a break, you are much more likely to maintain your attention and retain the information. Furthermore, the shorter, more intense sessions will likely put the pressure on that is needed to prevent procrastination.

In technical courses, it is usually more important to work problems than read the text (Newport, 2007). In class, write down in detail the practice problems demonstrated by the professor. Annotate each step and ask questions if you are confused. At the very least, record the question and the answer (even if you miss the steps).

In order to study smarter, not harder, you will need to eliminate distractions during your study sessions. Social media, web browsing, game playing, texting, etc. will severely affect the intensity of your study sessions if you allow them! Research is clear that multi-tasking (e.g., responding to texts, while studying), increases the amount of time needed to learn material and decreases the quality of the learning (Junco, 2012).

Know when and where you study best. It may be that your focus at 10:00 PM. is not as sharp as at 10:00 AM. Perhaps you are more productive at a coffee shop with background noise, or in the study lounge in your residence hall. Perhaps when you study on your bed, you fall asleep.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License.
You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Learning Center, University of North Carolina at Chapel Hill

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As a data analytics instructor for over 2 years, I am often asked 2 questions: "what skills should I learn to become a data analyst?" and "what resources or courses are the best for learning those skills?". Here's the short answer for them:

1. The key skills to become a data analyst are: Excel, Power BI, Tableau, SQL, and Python.
2. In this article, I will be providing some of my favorite resources that I used on my data journey and use with my students. However, there are millions of courses online (free or paid) the specific course you decide to take does not matter instead focus on completing at least 2-3 projects utilizing each skill.

Warning: This guide to does not include any certifications. There has been a wave of misleading information that certifications are needed or required to transition to data roles. My guide in this article focuses on skill building, projects, and networking.

Disclaimer: This guide has been broken down into 100 days because that is how long I instruct a full Data Analytics course at technology schools and boot-camps. Feel free to modify the timeline as needed.

Three major advantages formal education provides over self-learning is structure, networking, and accountability. I'm providing the structure in this article but networking and accountability are on you.

1. Setup a LinkedIn and Twitter account before starting their tech learning journey. The profiles don't need anything fancy, just a clear photo and headline of who you are. I recommend using a headline/bio such as "Student Data Analyst". You will use these accounts during the next 100 Days to share your projects and thoughts along your journey. After you complete this guide, you will use these accounts to find a job!

• LinkedIn: set a daily connections goal with the main goal to reach 500 connections. I don't know what it is, but you unlock some next level LinkedIn at 500 connections. Search "data analyst", "business intelligence analyst", etc. to find connections. You can send a quick sentence explaining why you want to connect, but to be honest I don't always do that. I also recommend joining a tech or data group where you can post questions, ask for project feedback, and connect with people.
• Twitter: follow tech content creators and hashtags such as #blacktechtwitter, #blackindata, etc.
• Optional: There are also virtual tech communities on Facebook, Clubhouse, and Discord. You can find in-person networking events in your area on Meetup.

Whoever said "a goal without a plan is just a wish" had to be a Virgo. I think all goals require monthly, weekly, and daily planning but that's a whole other article. In order for you to learn the skills at the end of the 100 days (or even the end of the year), you need a plan.

1. Evaluate your current life schedule and identify when you have time for learning. I created this weekly template to help with this exercise. I recommend 10-12 hours per week in order to complete in 100 days.

During my data science boot-camp, we were given assignments in DataCamp to learn the topics and really used in-class time to work on projects and ask questions. I highly recommend DataCamp to anyone interested in any data career. The courses provide reading, lecture videos, and hands-on exercises to help learn and apply the skills to real world problems.

The Basic plan is free and offers the first chapter of each course and 6 full courses. The Premium plan is $300/yearly or $39/month and offers Tableau and PowerBI (cannot access with Basic plan). You can cancel at any time. I am not affiliated with DataCamp or paid for this message.

Do you have to be good in math to work in tech? No. Do you have to be "okay" in statistics to work in data? Yes. My data journey includes a Udacity Nanodegree, local boot-camp, and two master's degree programs and they all began with course(s) in statistics. If any of you are interested in data science, it is even more critical for you to learn and grasp concepts.

Most of you are probably familiar with Excel and may have even played with it for school or personal use. Excel can be a very powerful tool and is still used very commonly (if not daily) by data analysts.

Power BI is another Microsoft product so it will look and feel similar to Excel which hopefully help you ease into it. Power BI is a very common tool for data visualizations and can also be used for data cleaning.

1. Create a dashboard at least 4 visualization (always have a title) and utilizing slicer filters. Tell a complete story and/or answer business question(s).
2. Create a dashboard with a map with drill-down features.
3. Create a dashboard with time series analysis and cards to display KPIs.
4. Connect a web data source to the dashboard and visualize.
5. Use Query Editor to rename columns and join tables then create dashboard.

Tableau is another data visualization software with similar functionalities as Power BI. You can find debates of which one is better on Youtube and Twitter. I personally prefer Tableau but solely for aesthetic reasons, it feels more Apple/iOS like with its sleekness. And I can also download it on my Mac.

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