{"id":929265,"date":"2024-12-08T18:35:03","date_gmt":"2024-12-08T18:35:03","guid":{"rendered":"https:\/\/www.smartdatacollective.com\/?p=929265"},"modified":"2024-12-08T18:35:05","modified_gmt":"2024-12-08T18:35:05","slug":"empowering-students-with-skills-for-data-driven-careers","status":"publish","type":"post","link":"https:\/\/www.smartdatacollective.com\/empowering-students-with-skills-for-data-driven-careers\/","title":{"rendered":"Empowering Students with Skills for Data-Driven Careers"},"content":{"rendered":"\n

Big data and AI are changing the nature of the workforce. One survey found that 60% of occupations will be affected by AI<\/a> in the coming years. The number of people that will be affected by big data is even higher.<\/p>\n\n\n\n

We talked about some of the data careers that are going to impact the world<\/a>. However, we wanted to go into more detail about some of the ways that people entering these occupations can develop the skills needed to succeed. <\/p>\n\n\n\n

Developing the Right Skills is Key to Thriving in a Data-Driven Career<\/h2>\n\n\n\n

Nobody ever accused a data-driven career of being rife with excitement and intrigue. After all, Superman and James Bond, characters synonymous with thrills and suspense, aren\u2019t number-crunchers. However, it seems society has done data interpreters a disservice. Particularly in today\u2019s world, where data underpins every aspect of our lives, the guidance of skilled educators including those like statistics tutors near me<\/a> can reveal the fascinating and practical applications f this subject.<\/p>\n\n\n\n

Everything we eat, drink, do, and buy comes with data attached. For it to have any value, that data must be organized, analyzed, and interpreted. These days, statisticians and others who work with data are unsung heroes. They help shape our world, and everything in it. This is one of the reasons that there are a lot of careers you can pursue with a data science degree, as we illustrated in this post<\/a>. <\/p>\n\n\n\n

How can teachers turn this seemingly dry-as-toast subject into one that sparks interest and passion? Besides breaking from rote memorization learning patterns, these steps can help teachers guide their students to statistics mastery.<\/p>\n\n\n\n

Understanding Statistics Skills<\/h3>\n\n\n\n

Often, debates over education focus on the subjects we teach; core versus electives, for example. Seldom does anyone talk about the cognitive and intellectual skills students gain from academic learning. We see that split reflected in our classrooms: we teach topics from textbooks, and measure students\u2019 retention through testing.<\/p>\n\n\n\n

Many teachers end up frustrated over this \u2018doing onto students\u2019, when they\u2019d rather empower their learners through active engagement. One way to overcome that is to focus on skills development, and let the subject matter follow.<\/p>\n\n\n\n

Statistics skills range from math abilities to pattern recognition. Of course, one must learn the practical skills statisticians need to work with data, like graphing and database management. Still, across this broad range of talents, teachers have many opportunities to awaken students\u2019 interest in the subject.<\/p>\n\n\n\n

For example, critical thinking is a vital component of pattern recognition. Honing this skill entails turning the gut feeling that \u2018something\u2019s off\u2019 into analyzing why it\u2019s \u2018not right\u2019, and discussing how to interpret data to arrive at correct conclusions.    <\/p>\n\n\n\n

We examine the popular ice cream and drownings scenario to make this point. In the summer, numbers point to more people eating ice cream. Likewise, statistics reveal more water accidents during those months. From there, the class breaks into a lively discussion over causation, correlation, and probability. Presto! You\u2019ve just livened up your statistics lecture, while reinforcing thinking skills.    <\/p>\n\n\n\n

Developing Analytical Skills<\/h3>\n\n\n\n

Statisticians are masters of making numbers reveal any desired conclusion. To analyze anything, you must know what you\u2019re analyzing it for.<\/p>\n\n\n\n

To clarify: a marketing analyst would likely focus on a product\u2019s sales volume, not how much product is produced. Conversely, a productivity analyst would look at production throughput per hour, not how much product is sold.<\/p>\n\n\n\n

To make learning data analysis more engaging, we might assign student groups various analytical roles. One might deal with marketing, another is a consumer, a third could be a manager, and so on. Everyone in the group receives the same data set, but each must analyze it through their assigned role. You may assign grades based on the papers they write, or how they role-play their scenario.   <\/p>\n\n\n\n

The more diverse your students\u2019 experiences are with analyzing data, the broader and better developed their abilities will be. Furthermore, such exercises help them see that data analysis isn\u2019t detached from real life, or something that happens via a computer program. It\u2019s something they can actively pursue \u2013 in class, and in their lives.<\/p>\n\n\n\n

Mastering Excel<\/h3>\n\n\n\n

Databases, spreadsheets, charts and graphs are essential statistics tools. Unfortunately, we have no clever, engaging tactics for you to try on your student groups. There\u2019s no way around having to sit at the computer, inputting data, and turning it into visual representations.<\/p>\n\n\n\n

Still, these exercises build computer literacy, a vital skill in our digital world. Our students qualify as digital natives, meaning they\u2019ve never known life without computers, smartphones, and the connected environment. However, that doesn\u2019t mean they have the skills needed to succeed and thrive in data-driven careers.<\/p>\n\n\n\n

Doing this type of nuts-and-bolts work teaches your students an important lesson. Our digital world is multi-layered, with back ends, front ends, and user interfaces. Parlaying that into statistics terms, we might say building databases is a back-end proposition, graphing and charting data is a front-end application, and spreadsheets are user interfaces.<\/p>\n\n\n\n

Even if your learners don\u2019t plan on a career in statistics, this level of computer literacy arms them with practical tools that apply to virtually any career field. They may grumble and groan over repetitive data entry tasks but, with your encouragement, they will benefit from these skills, in the long run.    <\/p>\n\n\n\n

Interpreting Results<\/h3>\n\n\n\n

We\u2019ve many clever soundbites to describe statistical results. The one about lies, worse lies and statistics, for instance, or \u201cthe numbers are the numbers\u201d. They all hint that interpretation is everything, in statistics.<\/p>\n\n\n\n

The trick to statistical interpretation is objectivity. The eternal struggle lies in what the numbers say, versus what interested parties want the numbers to say. Despite statistics being rather clinical in nature, personal biases can \u2013 and, often do, creep into data interpretation.<\/p>\n\n\n\n

Here is where you find opportunities for student empowerment, and enlightenment. The matter isn\u2019t just about making data prove your point, it\u2019s one of ethics and principle.<\/p>\n\n\n\n

One might grant that data showing red car drivers have more accidents than white car drivers isn\u2019t likely to impact society much. However, drawing false conclusions from medical data, for instance, can have an outsized influence on global health initiatives.<\/p>\n\n\n\n

Perhaps, presenting the ethical and real-world considerations of data interpretation is the greater task when teaching students this skill. Once again, you\u2019ll task them to think critically, to explore new-to-them ideas, and to express themselves.     <\/p>\n\n\n\n

With all this said, we know it\u2019s easy to present these student empowerment initiatives. These strategies work wonders, one-on-one and in small groups. Implementing them in the classroom is much more challenging. Still, if you have a bit of latitude to give your students hands-on learning opportunities, you might transform your classroom by using a few of them.\u00a0\u00a0The market for big data is worth over $348 billion<\/a>, so it is important to make sure that our future employees know how to use it.<\/p>\n\n\n\n

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Big data and AI are changing the nature of the workforce. One survey found that 60% of occupations will be affected by AI in the coming years. The number of people that will be affected by big data is even higher. We talked about some of the data careers that are going to impact the […]<\/p>\n","protected":false},"author":768,"featured_media":933031,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_seopress_robots_primary_cat":"48","_seopress_titles_title":"","_seopress_titles_desc":"Employees in data-driven careers need to develop the right skills to be successful in the coming years.","_seopress_robots_index":"","footnotes":""},"categories":[48,9],"tags":[3983,4900],"class_list":{"0":"post-929265","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-big-data","8":"category-smartdata-collective-exclusive","9":"tag-careers-in-big-data","10":"tag-data-in-business"},"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/www.smartdatacollective.com\/wp-json\/wp\/v2\/posts\/929265","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.smartdatacollective.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.smartdatacollective.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.smartdatacollective.com\/wp-json\/wp\/v2\/users\/768"}],"replies":[{"embeddable":true,"href":"https:\/\/www.smartdatacollective.com\/wp-json\/wp\/v2\/comments?post=929265"}],"version-history":[{"count":3,"href":"https:\/\/www.smartdatacollective.com\/wp-json\/wp\/v2\/posts\/929265\/revisions"}],"predecessor-version":[{"id":934250,"href":"https:\/\/www.smartdatacollective.com\/wp-json\/wp\/v2\/posts\/929265\/revisions\/934250"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.smartdatacollective.com\/wp-json\/wp\/v2\/media\/933031"}],"wp:attachment":[{"href":"https:\/\/www.smartdatacollective.com\/wp-json\/wp\/v2\/media?parent=929265"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.smartdatacollective.com\/wp-json\/wp\/v2\/categories?post=929265"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.smartdatacollective.com\/wp-json\/wp\/v2\/tags?post=929265"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}