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  • 3 Data Analysis Skills To Keep You Competitive in Today's Job Market

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    Data science experts are always in demand, but the best positions can be highly competitive. Augmenting your skills is a great way to stand out in a crowded field; however, the computer science world offers numerous avenues to keep your expertise up to date, so it can be hard to know where to start. If you’re trying to make yourself more attractive to recruiters or improve your performance in your current position, consider these three ideas for new skills to master.

    Master the Art of Examining Data

    Did you know that most analysts spend up to 80% of the time they dedicate to information management just on data cleaning? While it’s an important part of the process—complex data streams, especially when they’re user-generated, need someone to check for and fix duplicated, incorrect, and incomplete fields—it can devour massive amounts of company resources, and it’s not a one-time process, since new data should be coming in all the time.

    You’ll impress recruiters and coworkers alike by streamlining and simplifying company processes for data cleaning. There’s no need to manually pore over a database ever again with newer, friendlier data wrangling software that takes a more visual approach. Some of these tools are even designed to accommodate users by people without a substantial tech background, so you can even mentor junior employees in their use of such software and drastically speed up the cleaning and examination phase of data analysis.

    Enrich Datasets for Smarter Insights

    Even if your company is a big data behemoth that has the opportunity to gather information from users in multiple settings, modern customer relationship management demands the most detailed possible understanding of each consumer, and as industries from subscription meal kits to color cosmetics venture into the world of individual-level personalization, businesses will need to know more and more about their customers to stay competitive. That’s where data enrichment shines.

    You’re probably already familiar with common data enrichment procedures that combine proprietary information your company collects with third-party databases that offer additional basic facts, like geographic location or demographic affiliation. But as long as the company database and third-party database have at least one factor in common, there’s no need to limit yourself to census-level information. Tracking social media information and browsing data and connecting it with proprietary databases will help you demonstrate the usefulness of data analysis and collection by providing your marketing department with the tools they need to create personalized messaging for every stage of the buying process.

    Study Up On Machine Learning 

    No, the robots aren’t coming for your job just yet, but you’ll future-proof your prospects if you can get comfortable with the details of machine learning and artificial intelligence. As author and computer scientist Cal Newport observed in his book Deep Work, among the most successful participants in the modern economy (i.e., you) will be people who are experts in working alongside intelligent machines to develop new insights. The well-groomed datasets you create by cleaning and enriching raw information can become the foundation of AI learning in supervised algorithms, in which the machine uses tagged and sorted data to build the parameters it needs to compare predicted results to actual outcomes.

    You can invest in your own machine learning education by brushing up on the basics of optimizers and loss functions after hours, but to make AI systems a part of your company’s culture, it’s important to get comfortable working with the IT department, since they’ll be the ones responsible for operationalizing predictive models across the company. If you prototype an algorithm in your own favorite language with the libraries and plugins that work for you, it might not be compatible with the architecture the rest of the company uses, so spend some time researching ways to make your new project fit within stability-focused IT paradigms. Developing predictive technology and integrating it into your current position can be a heavy lift, but it is a great project to have on your resume.

    There are more skills than ever that data experts can master to demonstrate proficiency, but these three options are in-demand options you can start learning now.

     

    Create a Great Resume to Stay Competitive

     

    If you want to shift or upgrade your IT career, creating a good resume should be your priority before anything else. This is because your resume is your gateway to landing the dream job you've been aiming for. 

     

    For instance, it's crucial to create an appealing data science resume if you want your profile to stand out for a data scientist post. So, how do you create an impressive resume? 

     

    Check out these tips when writing a great resume for a data scientist job position:

     

    • Resume Design: Use clear fonts for your data scientist's resume, such as Arial or Cambria, delineating them with large-sized headings. It would help using white space to avoid overwhelming info. 

     

    The recommended format for a data scientist’s resume is PDF because MS word tends to get corrupted in tran CVsit.

     

    • Resume Summary: Sell your skills (e.g., data visualization, machine learning, Microsoft certified data scientist) and include your work experience and achievements. Highlight the right parts of your work experience than merely listing them. Adding bullet points is a good idea as it reflects measurable accomplishments. 

     

    For recent graduates, adding the GPA score is applicable, but you don't have to unless it’s impressive.

     

    • Sample to Highlight Data Analysis Skills: Instead of writing your data science skills by enumerating them (such as Collaboration, Database Management, CRM, and Data Visualization), include proof in bullet points. You can say, ‘Decreased wasted email and phone time by 60%’ or ‘collaborated with IT team members and optimized the company's two-year CRM database with cloud migration for a real estate firm.

     

    • Use Keywords: The job listing would include the job description. You can use the keywords on the listing to create an impressive resume. By doing so, you can align your resume to what the company is looking for in a high-potential candidate. Employers want to hire the right people who know their vision or aspirations and principles. 

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