Business Intelligence Developer Income Kentucky – If you buy stocks, distribute and sell products, or develop marketing strategies, you can understand the power of data. Today’s data shapes every decision we make and can determine a company’s path to success.
We’re swimming in data (2.5 quintillion bytes are created every day, according to some estimates) and we’re constantly developing faster and more automated ways to collect, organize, store, clean, retrieve, and analyze it all. Therefore, keeping all the data analysis languages, frameworks and tools up to date can be very difficult.
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Data analytics requires an ever-expanding vocabulary and resume to stay competitive, and it’s a field that needs more data professionals. According to the World Economic Forum, nearly all 20 new jobs are data-related. In fact, the top three jobs are data analyst/scientist, AI and machine learning specialist, and big data specialist. Additionally, careers in information can be lucrative. For example, data scientists earn a median salary of $103,930 in the U.S. According to the Bureau of Labor Statistics, several states (including New York) offer a median salary of over $120,000.
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According to the World Economic Forum’s 2020 Future of Jobs report, more than 90 percent of organizations in the digital communications, education, financial services, transportation, and mining and minerals sectors will adopt big data analytics technologies by 2025. Food production, automobile production, healthcare, and oil and gas production are doing the same.
These employers are often looking for professionals skilled in big data technology, but where do they start? Maybe you work for a business, have a strong product to sell, and understand your customer base well. You can work with data every day, feel good about using it to make business decisions, and learn how it builds success.
But there’s a catch: The sheer volume of data — and the ways to store, analyze and visualize it — can be overwhelming. Data analysis tools change and adapt. Developers make them faster, add more features, and build to handle ever-growing amounts of data. Choosing the right equipment is like choosing the right way to your destination quickly and easily. But which one?
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That’s where you have to be selective. You can’t (and probably shouldn’t) know how to use every programming language, framework, or tool for data analysis. The key is to acquire the skills you need to make yourself more competitive in the job market.
Consider which data analysis skills you need for specific career paths. For example, the Analytics and Data Science Standards Initiative analyzed LinkedIn job listings to determine the skills most in demand among employers. Here are some of the findings:
By combining these valuable skills with your business experience and knowledge, your research and communication skills, and your professional network, you can put yourself at the forefront of the job market. Which data analysis skills should you consider learning? Here are our picks.
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The major tools, frameworks and languages used in data analytics today cover a wide range of business needs and technology approaches. Some require little programming experience to operate, while others take time, study, and practice to master.
Again, don’t feel compelled to add each of these to your resume. Even veteran programmers don’t have the time or energy to become fluent in every data analysis tool. Consider the best fit for your industry, professional interests, and job title.
Python 3 is the latest version of one of the most popular programming languages in the world. For the fifth year in a row, Python is one of the most used languages and the language developers most want to learn, according to the latest Stack Overflow survey.
Python is considered powerful, easy to learn and used for a variety of programming purposes. Developers build games and websites in Python, businesses use it for data mining and analysis, and AI programmers train computers to learn in Python. One of the biggest film companies in the world also works on Python.
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Python is essential for data analytics and fields that use machine learning because it comes with a robust set of libraries. Users use Python to transform large amounts of data into real-time analytics and meaningful predictions. For example, Forecastwatch.com uses Python to help weather services use data to provide more accurate forecasts.
There are many online resources for learning Python that count millions of users in the community. Someone is usually available to help with tips and troubleshooting. Python’s website is a great place to start, even for coding beginners. And if you’re starting from square one, consider the online resource “Python From the Very Beginning.”
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According to Mozilla’s developers website, Hypertext Markup Language, or HTML, is “the most basic building block of the Web.” HTML is one of the primary languages used to create the look and feel of websites.
Developers use HTML on the front-end of web pages, which means their code sets the structure of the elements displayed on the page: text, images, links – all content flows through HTML.
HTML remains the fundamental tool of web development. According to Stack Overflow, it ranks number 2 among the most used technologies for developers. The main reason is the need to create websites, all written in the language.
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Visualization is an essential tool for data analysts, who want their data and analysis to be easy and clean for team members to read. Colleagues who don’t understand code can read the web presentation of your data. This makes HTML a useful skill for analysts who want to present actionable information.
Mozilla offers a helpful step-by-step introduction to HTML, which includes getting started, keywords, and how to build your first website.
CSS, or Cascading Style Sheets, HTML’s companion, gives your web pages a spark. Think of HTML as the language that builds the content of your page, and CSS brings the style to that element.
With CSS, developers define the look and feel of their pages. Sizes and fonts, color, column width and spacing, page layout and design are all controlled by CSS. This language works in conjunction with HTML, so if you’re comfortable with the basics of HTML, you can add CSS to your show. Knowing CSS allows you to add style and flexibility to your data visualizations.
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Getting started is easy. Mozilla provides another easy-to-follow introduction to CSS. In addition, Freecode Camp has built a series of courses that explain how to build a website using HTML and CSS.
Microsoft Spreadsheet remains one of the most powerful and useful tools when working with data. Excel industry result is confirmed. Do you want proof? The first module of the Columbia Engineering Data Analytics Boot Camp includes Excel.
Excel can do more than create simple spreadsheets and pie charts. It can be used for statistical modeling and forecasting. Excel also offers a number of advanced tools that data analysts, financial analysts, and market analysts can learn to differentiate themselves from employers. Many businesses, large and small, rely on Excel. An example of Microsoft highlights includes an ice cream maker that runs many aspects of its business using Excel.
TestGorilla’s job site offers the kind of expert perspective employers are looking for. Microsoft also offers a certification exam in Excel to demonstrate your proficiency.
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“Data culture” is a key phrase at Tableau, which built its platform to empower people and businesses to use their data.
Tableau makes free and paid versions of the software, which can import, analyze and visualize data from almost any source. Users can import data from spreadsheets, databases, or data warehouses. You can combine data from multiple sources, build visualizations, and discover trends.
Tableau is an option for analysts who need to work with data but don’t have the time or interest to learn to code. The Tableau community counts over one million users;
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