
{"id":10564,"date":"2021-11-15T16:54:12","date_gmt":"2021-11-15T15:54:12","guid":{"rendered":"https:\/\/careerfoundry.inbearbeitung.de\/en\/?p=10564"},"modified":"2023-07-12T11:31:50","modified_gmt":"2023-07-12T09:31:50","slug":"data-analytics-for-beginners","status":"publish","type":"post","link":"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/data-analytics-for-beginners\/","title":{"rendered":"Data Analytics for Beginners"},"content":{"rendered":"<p><span style=\"font-weight: 400;\"><strong>In an age where the collection and storage of data is more prevalent than ever, understanding how best to analyze and extract information from this data is the key to success for many businesses and organizations.<\/strong> <\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Enter data analytics:<\/strong> a field that <\/span><a href=\"https:\/\/www.dataversity.net\/brief-history-analytics\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">emerged way back in the 1960s<\/span><\/a><span style=\"font-weight: 400;\">. Since the advent of big data, cloud computing, machine learning and other various software and hardware, data analytics has evolved significantly, becoming an integral part of modern-day business decision-making.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">As a result of this industry growth, data analytics has become a popular field for those seeking career change. But the uninitiated may have many questions about the field, such as: what exactly is data analytics, anyway, and how do I become one? We\u2019ll answer these questions and more in this data analytics for beginners guide, but you can also get straight in with this <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/short-courses\/become-a-data-analyst\/\"><strong>free data short course for beginners<\/strong><\/a>.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In this guide, we\u2019ll address the following topics and questions. If you\u2019d like to skip ahead to a specific section, just use the clickable menu:<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"#what-is-data-analytics\"><span style=\"font-weight: 400;\">What is data analytics?<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"#types-of-data-analytics\"><span style=\"font-weight: 400;\">Types of data analytics<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"#data-analysis-process\"><span style=\"font-weight: 400;\">The data analysis process<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"#data-analytics-skills\"><span style=\"font-weight: 400;\">What skills do I need to become a data analyst?<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"#become-data-analyst\"><span style=\"font-weight: 400;\">How do I become a data analyst?<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"#data-bootcamps-courses\"><span style=\"font-weight: 400;\">Data analytics for beginners: Recommended bootcamps and courses<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"#data-analytics-projects\"><span style=\"font-weight: 400;\">Data analytics projects for beginners<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"#data-analytics-books\"><span style=\"font-weight: 400;\">Best data analytics books for beginners<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"#summary\"><span style=\"font-weight: 400;\">Summary and further reading<\/span><\/a><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">Ready to get into data analytics for beginners? Let\u2019s get started!<\/span><\/p>\n<h2 id=\"what-is-data-analytics\"><span style=\"font-weight: 400;\">1. What is data analytics?<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Every time we open an app, buy something at the supermarket, answer a survey, or fill out a CAPTCHA to log into our email\u2014we\u2019re creating data that is collected by businesses and organizations. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">As you can imagine just from these activities alone, colossal volumes of data are being collected every day! So what happens with this data? Well, for a large proportion of it\u2014up to 99.5%\u2014the answer is: nothing. And the other 0.5%? That gets used for data analytics purposes.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In the field of data analytics, data analysts aim to extract meaningful insights from the swathes of raw data presented to them. By doing this, businesses and organizations are able to unleash their predictive power, giving them the ability to make informed business decisions. With data analytics, businesses are able to answer the following questions: what\u2019s happened in the past? What\u2019s happening now? What might happen in the future?<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><span style=\"font-weight: 400;\">You can learn more about what data analytics is in the following video:<\/span><\/p>\n<style type=\"text\/css\">.embed-container { position: relative; padding-bottom: 56.25%; height: 0; overflow: hidden; max-width: 100%; } .embed-container iframe, .embed-container object, .embed-container embed { position: absolute; top: 0; left: 0; width: 100%; height: 100%; }<\/style>\n<div class=\"embed-container\"><iframe src=\"https:\/\/www.youtube.com\/embed\/yZvFH7B6gKI\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><span data-mce-type=\"bookmark\" style=\"display: inline-block; width: 0px; overflow: hidden; line-height: 0;\" class=\"mce_SELRES_start\">\ufeff<\/span><\/iframe><\/div>\n<div style=\"display: block; height: 20px; width: 100%;\"><\/div>\n<h2 id=\"types-of-data-analytics\"><span style=\"font-weight: 400;\">2. Types of data analytics<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">When analyzing data, there are different methods of extracting the information you need in order to draw out insights, patterns, and trends which guide business decisions. In data analytics and data science, we primarily focus on the following four methods:<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Descriptive analytics: What happened?<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">As the name suggests, this type of analysis purely describes what has happened and presents it in a digestible snapshot. Descriptive data analysis makes use of data aggregation and data mining to provide an overview of past actions, which is often the starting point for more in-depth analysis.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Diagnostic analytics: Why did it happen?<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The point of difference between descriptive and diagnostic analyses is that while descriptive analysis seeks to give an objective overview of what\u2019s happened, diagnostic analysis seeks to establish why those things may have happened. This can be done by <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/what-is-an-outlier\/\"><span style=\"font-weight: 400;\">identifying and handling outliers or anomalies within your data<\/span><\/a><span style=\"font-weight: 400;\">.\u00a0<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Predictive analytics: What is likely to happen in the future?<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Predictive analysis makes use of past patterns and trends in data in order to estimate the likelihood of a future outcome or event. In order to do this, a data analyst will devise predictive models that use the relationship between a set of variables.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"> predictive model may, for example, use the correlation between seasonality and sales figures to predict what points of the year are best for sales, and which are the worst. Based on this information, you may want to create marketing campaigns that will boost the quieter sales periods, and increase team power during intense sales periods.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Prescriptive: What is the best course of action?<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Think of prescriptive analysis as the conclusion of the other forms of analysis: now that we\u2019ve found out what happened, why it happened, and what may happen in the future, <\/span><b>what should be done next<\/b><span style=\"font-weight: 400;\">? <\/span><\/p>\n<p><span style=\"font-weight: 400;\">How can you avoid a future problem, or capitalize on an emerging trend? An everyday use of prescriptive data analysis is in maps and traffic apps. Think about Google Maps, for example. You type in your start and end destinations, and the app will come up with the best way to get you there, whether it\u2019s by foot, by public transport, bike, or by driving. It&#8217;ll also take into consideration the current traffic conditions, as well as reported quiet, flat, or scenic routes. Loaded with all of this information, you can make travel decisions that best suit your needs.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For a more in-depth look at each type, check out this guide: <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/different-types-of-data-analysis\/\"><span style=\"font-weight: 400;\">What Are the Different Types of Data Analysis?<\/span><\/a><\/p>\n<h2 id=\"data-analysis-process\"><span style=\"font-weight: 400;\">3. The data analysis process<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">While different types of data analysis require differing methodologies, skills, and know-how in order to glean useful insights, the underlying process remains the same. Let\u2019s take a look at the process a data analyst might follow:<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-10565\" src=\"http:\/\/careerfoundry.inbearbeitung.de\/en\/wp-content\/uploads\/2021\/11\/the-data-analysis-process-1.jpeg\" alt=\"An infographic showing each of the steps for the data analysis process\" width=\"1200\" height=\"400\" title=\"\" srcset=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-content\/uploads\/2021\/11\/the-data-analysis-process-1.jpeg 1200w, https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-content\/uploads\/2021\/11\/the-data-analysis-process-1-300x100.jpeg 300w, https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-content\/uploads\/2021\/11\/the-data-analysis-process-1-1024x341.jpeg 1024w, https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-content\/uploads\/2021\/11\/the-data-analysis-process-1-768x256.jpeg 768w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/p>\n<h3><span style=\"font-weight: 400;\">Step 1: Define the question<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">In order to establish the foundations of your analysis, a data analyst will first need to define their objective, otherwise known as a &#8220;problem statement.&#8221;<\/span><\/p>\n<p><span style=\"font-weight: 400;\">To start, the data analyst may ask: what business problem am I trying to solve? By defining this, it can set the framework for the entire analysis.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Step 2: Collect the data<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Once the analyst has established their objective for the analysis, they\u2019ll need to design a strategy for collecting the appropriate data. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">Firstly, they\u2019ll need to determine what kind of data they\u2019ll need: quantitative (numeric) data such as sales figures, or qualitative (descriptive) data, which may include customer surveys. You can <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/difference-between-quantitative-and-qualitative-data\/\"><span style=\"font-weight: 400;\">learn more about qualitative vs. quantitative data here<\/span><\/a><span style=\"font-weight: 400;\">.\u00a0<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Step 3: Clean the data<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">So, the data has been collected. Now what? It\u2019s time to clean! In this step, a data analyst will need to clean the data to make sure it\u2019s of high quality. This cleaning\u2014or \u201cscrubbing\u201d\u2014process involves:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Removing unwanted data points<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Removing major errors, duplicates, and outliers<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Filling in any missing data<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Bringing structure to the data<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">As you can imagine, this is a crucial part of the process. It\u2019s also the most time-consuming!\u00a0 To learn more about data cleaning, <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/what-is-data-cleaning\/\"><span style=\"font-weight: 400;\">check out our in-depth guide<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Step 4: Analyze the data<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Right! By this point, the data analyst has climbed the biggest mountain of the data-analyzing-journey\u2014that being the data clean\u2014and now they\u2019re ready for the fun part: the analysis! <\/span><\/p>\n<p><span style=\"font-weight: 400;\">We\u2019ve already explained the basics of the four types of data analysis\u2014descriptive, diagnostic, predictive, and prescriptive. This is the part where the data analyst will apply the methodologies associated with the analysis type that will best \u201csolve\u201d their problem statement.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Step 5: Visualize and share your findings\u00a0<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The data has been analyzed and insights have been gathered. However, this isn\u2019t the end of the data analytics process: the data analyst must now present their findings in a way that\u2019s clear and easily understood by key stakeholders. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">In order to do this, an analyst may use visualization software\u2014such as <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/what-is-tableau\/\"><span style=\"font-weight: 400;\">Tableau<\/span><\/a><span style=\"font-weight: 400;\"> or Microsoft Power BI\u2014that will generate reports, dashboards, or interactive visualizations. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">At this stage of the process, it\u2019s important that the data analyst is as clear and transparent in their findings as possible so that the relevant stakeholders can make informed decisions. You can <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/what-is-data-visualization\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">learn more about data visualization in our guide<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This is just a basic overview of the data analytics process. To learn more, read more in this article: <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/the-data-analysis-process-step-by-step\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">A Step-by-Step Guide to the Data Analysis Process<\/span><\/a><\/p>\n<h2 id=\"data-analyst-skills\"><span style=\"font-weight: 400;\">3. What skills do I need to become a data analyst?<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">While there\u2019s no clear-cut career path to become a data analyst, there are a few standard hard and soft skills that every data analyst entering the field will need. This list is by no means exhaustive, but see this as a starting point if you\u2019re considering a career change.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Hard skills needed to become a data analyst<\/span><\/h3>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Demonstrable knowledge in programming and querying languages, such as <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/what-is-python\/\"><span style=\"font-weight: 400;\">Python<\/span><\/a><span style=\"font-weight: 400;\"> and SQL<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Demonstrable proficiency in business intelligence and data analytics software, which may include RapidMiner, Tableau, and SAS<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Solid understanding of each step of the data analysis process<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Solid numerical and statistical skills<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Hard skills are, as you may have already figured out, the technical skills required to fulfill the requirements of a role. They are generally measurable in terms of proficiency\u2014ranging from basic proficiency to advanced expertise.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Soft skills required to become a data analyst<\/span><\/h3>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Great collaboration and communication skills<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">An eye for detail<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A methodical and logical approach<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A problem-solving mindset<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Soft skills, compared to hard skills, are <\/span><b>not measurable<\/b><span style=\"font-weight: 400;\">. Think of soft skills as being more like characteristics that are a part of your existing personality, though you may have picked up or refined these skills through other roles or experiences you\u2019ve had.<\/span><\/p>\n<h2 id=\"become-data-analyst\"><span style=\"font-weight: 400;\">4. How do I become a data analyst?<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Now that we\u2019ve gone over the basics of data analytics for beginners: what data analysis is, the types of data analysis, the data analysis process, and the skills possessed by data analysts, you might be wondering, \u201cGreat! So, how exactly do I become a data analyst, then?\u201d\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It\u2019s very possible to get hired as a data analyst without any formal training. For example, if you\u2019re interested in <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/what-is-a-healthcare-data-analyst\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">becoming a healthcare analyst<\/span><\/a><span style=\"font-weight: 400;\"> and you already work within the healthcare field and possess the soft skills required, your employer may be interested in providing a traineeship to skill you up on the hard skills required.. However, this would be considered a non-traditional route to entry.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For a more structured route into the field, here are some practical steps you can take:<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Complete a data analytics bootcamp or program<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Especially if you\u2019re thinking about entering the field with little to no experience, taking a dedicated <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/best-data-analytics-bootcamps\/\"><span style=\"font-weight: 400;\">data analytics bootcamp<\/span><\/a><span style=\"font-weight: 400;\"> is the best way to cover all of the basic skills and knowledge needed to become a data analyst.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It helps to look out for a course that has a <\/span><b>project-based curriculum<\/b><span style=\"font-weight: 400;\">\u2014as you can use these projects in your future portfolio\u2014as well as <\/span><b>one-on-one mentoring<\/b><span style=\"font-weight: 400;\"> and a <\/span><b>certificate of completion<\/b><span style=\"font-weight: 400;\">. Other nice-to-haves would include a focus on <\/span><b>job preparation<\/b><span style=\"font-weight: 400;\">, <\/span><b>networking opportunities,<\/b><span style=\"font-weight: 400;\"> and a <\/span><b>job guarantee<\/b><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">We\u2019ll talk a little more about data analytics bootcamps and courses a little later on in this article, so read on!<\/span><\/p>\n<p>In this post, we review some of the top <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/data-analytics-schools\/\">data analytics schools<\/a> on the market.<\/p>\n<h3><span style=\"font-weight: 400;\">Write a dedicated resum\u00e9<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The job market can be tough\u2014no matter the industry\u2014so having a solid resum\u00e9 is key to stand out to recruiters and potential future employers. If you\u2019re looking to change careers into data analytics, you\u2019ll need to re-write your resum\u00e9 to highlight the new skills you\u2019ve acquired during the course of your data analytics program\u2014or any other skills from previous roles that may be relevant!<\/span><\/p>\n<p><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/data-analyst-resume\/\"><span style=\"font-weight: 400;\">In this guide, we show you how to write a data analyst resume from start to finish<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Create a solid data analytics portfolio<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">You may think that once you\u2019ve written a bulletproof resum\u00e9, you\u2019re good to go, right? Wrong! Recruiters and employers want to see your skills and experience exemplified in previous projects, which is why most career-changers will have also built up a data analytics portfolio in addition to their resum\u00e9.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It\u2019s a good idea to host this portfolio online, so that you can update it regularly. You should include a <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/data-analytics-projects\/\">range of projects<\/a> that highlight different aspects of your data analytics skillset. Consider including projects you completed on your own as well as projects you completed as part of a team; projects using different programming languages; projects run using different methods of analysis; projects using visualizations and clearly-written explanations of your findings.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Learn more about <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/data-analyst-portfolio\/\"><span style=\"font-weight: 400;\">data analytics portfolios (with examples!) in this article<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Do your research, network, and apply for jobs<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The field of data analytics is wide-ranging, and roles you\u2019ll find online won\u2019t all come under the same name. We outline many of the job titles you might find online, and what the job descriptions may entail in <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/entry-level-data-analyst-jobs\/\"><span style=\"font-weight: 400;\">this guide<\/span><\/a><span style=\"font-weight: 400;\">. However, we recommend that you do your own research to discover which fields\u2014and more specifically, which companies\u2014suit your personal wants and needs best.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Once you\u2019ve narrowed down the list of companies and organizations you may be interested in, networking is key. This can be done by attending career fairs, getting in contact with recruiters, or reaching out to people on LinkedIn. It\u2019s a good way of getting information about upcoming roles that isn\u2019t always listed on a careers page.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Finally, take the plunge and start applying for jobs! Make sure that you tailor your cover letter to each individual job posting you\u2019re interested in. Yes, it\u2019s some extra work, but it pays off\u2014recruiters can spot a generic cover letter from a mile away. Putting in the extra effort shows that you have a genuine interest in the role. It may take a while for your efforts to pay off, but it\u2019s worth it in the long run! Plus, every interview is good practice for the next one.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">You might enjoy this recording of a <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/events\/how-to-become-a-data-analyst\/\"><span style=\"font-weight: 400;\">webinar we hosted about becoming a data analyst<\/span><\/a><span style=\"font-weight: 400;\">. We often host live workshops and webinars related to data analytics\u2014you can <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/events\/\"><span style=\"font-weight: 400;\">check out our upcoming events here<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h2 id=\"data-bootcamps-courses\"><span style=\"font-weight: 400;\">5. Data analytics for beginners: Recommended bootcamps and courses<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">You\u2019ve read this far into this article, and maybe you\u2019re at a point where you\u2019re considering data analytics as a career path. Great! <\/span><\/p>\n<p><span style=\"font-weight: 400;\">If you\u2019re coming into the field from a related discipline that works with data or statistics, you may only need to upskill in a few areas. If you\u2019re a relative beginner to data analytics, you may find a dedicated bootcamp or course useful to give you an overall understanding of the field.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"> There are many data analytics bootcamps and courses on the market, but here are a few of the best:<\/span><\/p>\n<h3><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/courses\/become-a-data-analyst\/\"><span style=\"font-weight: 400;\">The CareerFoundry Data Analytics Program<\/span><\/a><\/h3>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Mode of study:<\/b><span style=\"font-weight: 400;\"> Online<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Duration:<\/b><span style=\"font-weight: 400;\"> 8 months (15 hours per week, self-paced)\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Price:<\/b><span style=\"font-weight: 400;\"> $7,505 \u2013 7,900 USD<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">For beginners who want to fit their studies around their own schedule, the data analytics program offered by CareerFoundry may be a good fit. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">This comprehensive, online, self-paced program will take you from a relative newbie to job-ready data analyst in anywhere from 5-8 months. Being a self-paced program, you can complete modules whenever it suits your schedule\u2014as long as you hit certain milestones within the overall 8-month course duration.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"> You\u2019ll be teamed up with a dedicated mentor and tutor, who\u2019ll coach you through the modules and give you direct feedback on any projects you complete.<\/span><\/p>\n<p>CareerFoundry\u2019s offering comes in at $7,900 for the entire program, b<span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;the complete CareerFoundry UX Design Program starts from $7,900, but the cost of the tuition is dependent on your location and is competitively priced. A range of flexible payment options include paying upfront, or getting a small course discount. Contact CareerFoundry to find out your local pricing and if there are any partial scholarships available.&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:769,&quot;3&quot;:{&quot;1&quot;:0},&quot;11&quot;:4,&quot;12&quot;:0}\">ut the cost of the tuition is dependent on your location and is competitively priced. <\/span><\/p>\n<p><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;the complete CareerFoundry UX Design Program starts from $7,900, but the cost of the tuition is dependent on your location and is competitively priced. A range of flexible payment options include paying upfront, or getting a small course discount. Contact CareerFoundry to find out your local pricing and if there are any partial scholarships available.&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:769,&quot;3&quot;:{&quot;1&quot;:0},&quot;11&quot;:4,&quot;12&quot;:0}\">A range of flexible payment options include paying upfront, or getting a small course discount.\u00a0<a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/connect\/data-analytics-program-contact-advisor\/\" target=\"_blank\" rel=\"noopener\">Contact one of their program advisors<\/a>\u00a0to find out your local pricing and if there are any partial scholarships available.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">You can also try out the <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/short-courses\/become-a-data-analyst\/\"><span style=\"font-weight: 400;\">free introductory five-day short course<\/span><\/a><span style=\"font-weight: 400;\"> before committing to the full course.<\/span><\/p>\n<h3><a href=\"https:\/\/generalassemb.ly\/education\/data-analytics-remote-online\" rel=\"noopener\"><span style=\"font-weight: 400;\">The General Assembly Data Analytics Course<\/span><\/a><\/h3>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Mode of study:<\/b><span style=\"font-weight: 400;\"> Online or on campus (USA, Europe, Asia, and Australia)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Duration: <\/b><span style=\"font-weight: 400;\">10 weeks part-time (4 hours per week) or 1 week intensive<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Price:<\/b><span style=\"font-weight: 400;\"> $3,950 USD<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">This course is well-suited for those who are interested in learning the basics of data analytics, or employees working adjacent to data looking to upskill. Held either online or on-campus, you can study in the evenings over the space of ten weeks, or take a more intensive approach with the one-week accelerated course. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">By the end of the course, you\u2019ll have completed a capstone project that you can include in your portfolio, have access to the extensive General Assembly alumni network, and receive a certificate of completion.<\/span><\/p>\n<h3><a href=\"https:\/\/online-learning.harvard.edu\/course\/business-analytics?delta=0\" rel=\"noopener\"><span style=\"font-weight: 400;\">The Harvard University Business Analytics Course<\/span><\/a><\/h3>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Mode of study:<\/b><span style=\"font-weight: 400;\"> Online<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Duration:<\/b><span style=\"font-weight: 400;\"> 8 weeks (5-6 hours per week, self-paced)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Price:<\/b><span style=\"font-weight: 400;\"> $1,750 USD<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">This program is best suited for those looking for a university-endorsed course, without having to commit to a four-year degree. O<\/span><\/p>\n<p><span style=\"font-weight: 400;\">ffered through the Harvard Business School online platform, this online course gives a solid introduction to the key concepts of data analytics, including how to interpret data, how to develop and test hypotheses, and how to perform single and multiple variable regression analysis. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">If you\u2019re looking to learn how to develop a \u201cdata mindset\u201d in order to make smarter business decisions through a flexible, reasonably-priced course, this is a great option.<\/span><\/p>\n<h3><a href=\"https:\/\/www.springboard.com\/courses\/data-analytics-career-track\/\" rel=\"noopener\"><span style=\"font-weight: 400;\">The Springboard Data Analytics Bootcamp<\/span><\/a><\/h3>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Mode of study:<\/b><span style=\"font-weight: 400;\"> Online<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Duration:<\/b><span style=\"font-weight: 400;\"> 6 months (15-20 hours per week)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Price: <\/b><span style=\"font-weight: 400;\">$11,300 USD<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Remember our list of hard and soft skills we mentioned earlier? If you feel like you possess some\u2014but not all\u2014of these skills, and want to complete the list in order to change careers to work in data analytics, the Springboard Data Analytics Bootcamp may be suitable for you. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">Over the course of the 6-month bootcamp, you&#8217;ll learn the fundamentals of the data analytics process, including how to frame structured thinking, analyze business problems, connect data using SQL, visualize data using Python, and communicate your analysis. There are some prerequisites for enrollment, but if you don\u2019t initially qualify, you can take their <\/span><a href=\"https:\/\/courses.springboard.com\/p\/intro-to-business-analytics\" rel=\"noopener\"><span style=\"font-weight: 400;\">Intro to Business Analytics course<\/span><\/a><span style=\"font-weight: 400;\"> instead.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For a closer look at courses and qualifications, check out <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/best-data-analytics-certification-programs\/\"><span style=\"font-weight: 400;\">this round-up of the best data analytics certification programs<\/span><\/a><span style=\"font-weight: 400;\">.\u00a0<\/span><\/p>\n<h2 id=\"data-analytics-projects\"><span style=\"font-weight: 400;\">6. Data analytics projects for beginners<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">If you\u2019re working towards changing careers to become a data analyst, you\u2019ll need to create a data analytics portfolio. Portfolios are an easy way to show recruiters and potential employers\u2014through projects\u2014your understanding of the data analytics process, as well as your proficiency using industry-standard tools.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">What kinds of processes should you highlight in your beginner data analytics projects? You could check out <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/where-to-find-free-datasets\/\"><span style=\"font-weight: 400;\">free datasets online<\/span><\/a><span style=\"font-weight: 400;\">\u2014<\/span><a href=\"https:\/\/towardsdatascience.com\/how-to-build-your-own-dataset-for-data-science-projects-7f4ad0429de4\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">or build your own<\/span><\/a><span style=\"font-weight: 400;\">\u2014and use them to show some of the following beginner processes as projects:<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Scraping the web for data<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Data scraping means pulling data from an existing website and compiling it into a usable format. You could scrape data from <\/span><a href=\"https:\/\/medium.com\/geekculture\/4-web-scraping-projects-that-will-help-automate-your-life-6c6d43aefeb5\" rel=\"noopener\"><span style=\"font-weight: 400;\">job websites, or even sports analytics<\/span><\/a><span style=\"font-weight: 400;\">! Just make sure you have the appropriate permissions before you start scraping.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Carrying out exploratory analyses<\/span><\/h3>\n<p><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/exploratory-data-analysis\/\"><span style=\"font-weight: 400;\">Exploratory data analysis<\/span><\/a><span style=\"font-weight: 400;\"> is the process of identifying initial trends, patterns, and characteristics in a dataset using languages like R and Python, which have swathes of <\/span><a href=\"https:\/\/analyticsindiamag.com\/exploratory-data-analysis-in-python-vs-r\/\" rel=\"noopener\"><span style=\"font-weight: 400;\">pre-existing algorithms<\/span><\/a><span style=\"font-weight: 400;\"> that you can use to perform this analysis.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Cleaning untidy datasets<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Data cleaning\u2014otherwise known as data cleansing or <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/data-wrangling\/\"><span style=\"font-weight: 400;\">data wrangling<\/span><\/a><span style=\"font-weight: 400;\">\u2014is a lengthy part of the data analysis process. It\u2019s also very important to clean data properly in order to achieve accurate results. Showing a simple dataset \u201cbefore and after\u201d will highlight your competency in this task.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Communicating your results using visualizations<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">For the stakeholders you\u2019ll work with as a data analyst, visualizations are of utmost importance. Instead of being bogged down with numbers and algorithms, your stakeholders will see the meaningful information you\u2019ve gleaned from your dataset in the form of a visualization, which may look like a chart, graph, or map. The <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/data-visualization-types\/\"><span style=\"font-weight: 400;\">type of visualization<\/span><\/a><span style=\"font-weight: 400;\"> you land on will depend on the insights you\u2019ve gleaned and how effectively you can present them.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Need some more inspiration to kickstart your own data analytics portfolio? <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/data-analytics-portfolio-examples\/\"><span style=\"font-weight: 400;\">Check out some of our favorite portfolios here<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h2 id=\"data-analytics-books\"><span style=\"font-weight: 400;\">7. Best data analytics books for beginners<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">For anyone looking to take a deeper dive into data analytics outside of the practical aspects of the field, there are a wealth of data analytics books available. Here are four of our top picks for data analytics beginners:<\/span><\/p>\n<h3><a href=\"https:\/\/www.goodreads.com\/en\/book\/show\/23594979-data-analytics-made-accessible\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">Data Analytics Made Accessible<\/span><\/a><span style=\"font-weight: 400;\">, by Anil Maheshwari<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">As the title states, this book is an overview of the field of data analytics, made accessible for those without any prior knowledge or experience of the field. At the beginning of each chapter (which span the fundamentals of data analytics, from data warehousing to decision trees) Maheshwari includes a \u2018caselet\u2019, to provide real-world context to the reader. It also includes beginner tutorials in the appendix, to get a taste for the data analytics process.<\/span><\/p>\n<h3><a href=\"https:\/\/www.goodreads.com\/book\/show\/43726517-hello-world?from_search=true&amp;from_srp=true&amp;qid=6VJNcO3APk&amp;rank=1\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">Hello World: Being Human in the Age of Algorithms<\/span><\/a><span style=\"font-weight: 400;\">, by Hannah Fry<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">British mathematician Hannah Fry takes a deep dive into the world of artificial intelligence, stripping it down to its simplest form\u2014algorithms. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">In <\/span><b>Hello World<\/b><span style=\"font-weight: 400;\">, Fry looks at how data and algorithms have the power to transform our world\u2014seemingly either for better or for worse, and nowhere in-between. It\u2019s an entertaining introduction to the world of AI, written in a way that can be understood by anyone interested in how AI functions, as well as its ethical dilemmas.<\/span><\/p>\n<h3><a href=\"https:\/\/www.goodreads.com\/book\/show\/2272880.The_Drunkard_s_Walk?from_search=true&amp;from_srp=true&amp;qid=RrI87XjH0B&amp;rank=1\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">The Drunkard&#8217;s Walk: How Randomness Rules Our Lives<\/span><\/a><span style=\"font-weight: 400;\">, by Leonard Mlodinow<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">This book, by American theoretical physicist Leonard Mlodinow, explores the issues of randomness, chance, and probability in our daily lives. And how does this relate to data analytics, you may ask? <\/span><\/p>\n<p><span style=\"font-weight: 400;\">While seemingly &#8220;random&#8221;, Mlodinow uses these themes to explore the opposite\u2014human\u2019s reliance on statistics and data to dictate future actions and decisions is not foolproof on its own, and actually requires strong critical thinking skills in order to make the \u2018best\u2019 decisions. This book is great for anyone interested in the more complex aspects of probability and statistics, while also reminding you of the human side of data-based decision making.<\/span><\/p>\n<h3><a href=\"https:\/\/www.goodreads.com\/book\/show\/39644156-how-smart-machines-think?ac=1&amp;from_search=true&amp;qid=EHQyFiFqsP&amp;rank=1\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">How Smart Machines Think<\/span><\/a><span style=\"font-weight: 400;\">, by Sean Gerrish<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Have you ever wondered about how self-driving cars work, or how your streaming service manages to find exactly what you want to watch, without you having to search for it? Wonder no more!<\/span><\/p>\n<p><span style=\"font-weight: 400;\"> This book, written by an expert <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/machine-learning-engineer\/\" target=\"_blank\" rel=\"noopener\">machine learning engineer<\/a>, outlines some of the key ideas that enable some of our \u2018smart\u2019 machines to perceive and interact with the world, through the theory and practice of creating machine learning algorithms. For any data analyst looking to get into machine learning and artificial intelligence, this is a must-read.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">If you\u2019re looking to get offline and learn more about the world of data analytics, <\/span><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/best-data-books\/\"><span style=\"font-weight: 400;\">you can get a full data analytics reading list in this article<\/span><\/a><span style=\"font-weight: 400;\">.\u00a0<\/span><\/p>\n<p><strong>Bonus reading:\u00a0<\/strong><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/data-analytics-blogs\/\">12 Must-Read Data Analytics Blogs<\/a><\/p>\n<h2 id=\"summary\"><span style=\"font-weight: 400;\">8. Summary and further reading<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">So, there you have it! Our guide to data analytics for beginners. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">In this post, we\u2019ve explored the fundamental basics of data analytics as a field, the main types of analysis, as well as an overview of the data analytics process. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">Then, we looked at the basics of entering the field\u2014what skills do you need, and what process should you follow in order to become a data analyst? What are some of the best data analytics bootcamps on the market? What kind of beginner projects could you take on in order to show off your newly-learned <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/what-are-the-key-skills-every-data-analyst-needs\/\">data analytics skills<\/a>? Finally, what are the best books on the topics geared towards beginners?<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This guide is by no means exhaustive, but we\u2019ve provided links to other guides that should round out the information we\u2019ve covered here.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">To learn more about the fundamentals of data analytics for beginners, sign up for CareerFoundry&#8217;s <\/span><strong><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/short-courses\/become-a-data-analyst\/\">free, 5-day introductory data analytics course<\/a><\/strong><span style=\"font-weight: 400;\">. You\u2019ll receive a short course on everything data analytics-related, delivered daily to your inbox. You may also be interested in these articles:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/data-analyst-job-with-no-experience\/\"><span style=\"font-weight: 400;\">Is It Possible to Get a Job as a Data Analyst With No Experience?<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/data-analyst-career-prospects\/\"><span style=\"font-weight: 400;\">Where Could a Career in Data Analytics Take You? A Guide to Data Analytics Jobs<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/data-analyst-salaries-by-industry\/\"><span style=\"font-weight: 400;\">Which Industries Pay the Highest Data Analyst Salaries?<\/span><\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>We cover everything you need to know about data analytics for beginners in this guide, all the way from the data analytics process to the best data analytics courses on the market.<\/p>\n","protected":false},"author":120,"featured_media":10566,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_lmt_disableupdate":"yes","_lmt_disable":"","footnotes":""},"categories":[3],"tags":[],"class_list":["post-10564","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-analytics"],"acf":{"homepage_category_featured":false},"modified_by":"Matthew Deery","_links":{"self":[{"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/posts\/10564","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/users\/120"}],"replies":[{"embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/comments?post=10564"}],"version-history":[{"count":8,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/posts\/10564\/revisions"}],"predecessor-version":[{"id":31510,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/posts\/10564\/revisions\/31510"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/media\/10566"}],"wp:attachment":[{"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/media?parent=10564"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/categories?post=10564"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/tags?post=10564"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}