A New Season In Life (Update After a 7 Month Hiatus) - Cubicle Chic

A New Season In Life (Update After a 7 Month Hiatus) - Cubicle Chic A New Season In Life (Update After a 7 Month Hiatus) Home Life, Life as a blogger, Lifestyle November 11, 2019 1 Comment It's been 7 months since I last opened my WordPress manager. We should recently let that hit home a tad… … Before this break, I blogged nearly relentless for a long time. What an odd seven months it's been, with me not having created a solitary article. Today, it's with overwhelming sadness that I share this with you folks, my dear perusers… I've had nothing to say. Did you think I was going to state I'm closing the blog down? Oh, no. Never. I buckled down each one of those years for Cubicle Chic to endure the demise of obscure causes. Without a doubt, being a full-time Mom occupies constantly and vitality I can assemble. Indeed, I set out to turn into a full-time blogger 2 years back and have missed the mark inside and out. Of course, I've had a significant instance of a personality emergency as a result of claiming a blog name Cubicle Chic and having no work space to call mine. However, stop and think for a minute… Even when I had nothing to state, I despite everything want to compose. To make. To deliver. To abandon something. Call me crazy.All of this made me consider something distracting yet calming. Individuals state online networking is for the shallow and that it's everything about flaunting. It's anything but difficult to sort everything as prideful Millenials setting up a breathtaking front by curating each jealousy commendable detail of their life and flawlessly pressing them into the 9 squares joined via painstakingly investigated hashtags. It is anything but an off-base articulation to make and the vast majority of us are liable of it in any event at certain focuses. Be that as it may, I think it takes advantage of something more profound and more primal.A artist composes, an artist makes, a picture taker catches what he/she finds on the planet and an author composes. At the point when it's a solitary, unmistakable, and good aptitude (the thoughtful that requires some in vestment and exertion to create), its yield is esteemed masterful and productive. The issue with making anything via web-based networking media when you are a layman is that it appears as though it requires no exertion. It's simple on the grounds that Facebook has made it simple for you to feature photographs, works, workmanship or anything that you need to impart to the world. What's more, after the post gets open, you get likes. The Godforsaken preferences. Preferences that ruin everything. Preferences that spoil the most perfect of goals. Preferences that veil the longing to make and make it seem as though you're stooping for attention.But it isn't so straightforward. In any event not for me. There is euphoria in the demonstration of making something. Picking the correct word to pass on my musings. Assembling phrases that sound excellent. Fixating on the request for sentences for greater lucidity. This is the reason I write.Which carries me to the fate of Cubicle Chic.I still ne ed to include esteem and be of administration to my perusers. Yet, going ahead, I will compose things that I have individual interests in. Books that motivate me. Thoughts that animate me. Individuals and stories that carry me to tears. I need to expound on things that inspire me, and ideally, through my composition, I can elevate you, too.In the interim, I'd prefer to share a couple of things that have been enhancing my spirit lately. Four Things That Brought Me to Tears Last MonthIt's an assortment of a book, a Facebook cut, a TV arrangement, and a film. On the off chance that you appreciate things that make you think while crying a couple of tears, click on every one of the thumbnails beneath or just bookmark them. You don't think twice about it, I promise. 1. Call Me American; 2. Beam Chen â€" The Swan; 3. Modern Love; 4. Last Christmas 1. Book: Call Me American by Abdi Nor Iftin. This was a groundbreaking book not just in light of the fact that it gave me an exhaustive comprehe nsion of the outrage that has been attacking Somalia, however the mankind that drives forward despite unadulterated underhandedness. It likewise gives me how comparable we as a whole are, paying little mind to our way of life, religion, race, and life objectives. It's the most lovely book I've perused in 2018. 2. Facebook video: A short clasp of him practicing with a harpist before a major show at the Walt Disney Concert lobby a couple of days prior. Despite the fact that there are individuals talking out of sight and some broad clamor, he played so perfectly it gave me goosebumps and made me cry! 3. Television Series: It's an Amazon Prime TV arrangement dependent on the New York Times segment with a similar name. Huge numbers of them are about flighty love and love that are past simply sentimental. My preferred one is scene 1! 4. Film: It's a straightforward Rom Com that anybody would appreciate. In any case, there are some acceptable messages that the film is passing onâ€"helping other people is eventually what brings you joy. It likewise happens to be the principal film I've seen with the spouse in theater in this whole year. I altogether appreciated the film including the 10 minutes I spent crying and sniffling! Stay tuned for additional. Much obliged to you for perusing!

Why Should Your Business Care About Sexual Harassment

Why Should Your Business Care About Sexual Harassment Why Should Your Business Care About Sexual Harassment? Sexual harassment is serious business. Unfortunately, it has probably been around as long as business culture itself has been around. But no one thinks its a laughing matter anymore (not that it ever was). In fact, failing to taking it seriously could mean a disaster for a business. Awareness of the problemâ€"and the legal and cultural steps to solve itâ€"really increased in the last 25 years. In 1991, at a hotel convention, Marine and Navy officers become infamous for drunken assaults on dozens of fellow personnel. The “Tailhook Scandal” was a black eye the U.S. military because it was alleged that harassment was an accepted aspect of life in uniform. Many people claimed military higher-ups were simply looking the other way. The result was legal action against the hotel and embarrassing investigations into the military hierarchy. The same year, Anita Hills allegations of sexual harassment nearly torpedoed Clarence Thomass nomination to the Supreme Court. The Senate judiciary committee hearings were closely watched by Americans, and although Hills side of the story was never proved or disproved, the number of sexual harassment claims shot up 50 percent in the next year. As a nation, we were becoming aware of workplace sexual harassment as a serious issue. The private sector has also had more than its share of high-profile cases (though not as high-profile as these). Employers know they must take harassment seriously or they could find themselves in court. The safety of their employees must be their top priority or they will be held accountable. Here are a few of the reasons your business should do everything it can to stamp out sexual harassment: It maintains legal standards and avoids litigation. The cost of doing business is high enough without spending more on lawyers and consultants, which is what your company will have to do to fend off allegations of harassment. Under the Civil Rights Act of 1991, even a small business can be required to pay up to $50,000 in damages. For large companies with more than 500 employees, the damages could hit $300,000. An expensive settlement or punitive damages payment will make you wish you had invested in harassment and discrimination training before it was too late. It ensures a good reputation for your company. The legal and ethical problems that result from harassment should be a businesss biggest concerns. But you cant ignore another kind of damage that can result from allegationsâ€"damage to your brand. If youre a small business in a niche market, a reputation for harassment on the job could turn into your biggest PR nightmare. It improves the productivity of all employees. Everyone at work is impacted by harassment. Harassed women suffer from debilitating stress reactions that include anxiety, depression, headaches, sleep disorders and more. The demoralizing effects of harassment will drive employees to seek employment elsewhere. Even those not directly affected can have their work disrupted. On the other hand, addressing the issue head-on with workplace training has been demonstrated to increase employee loyalty and reduce turnover. A safe work environment is a productive work environment, and keeping workers happy is the best way to keep the company focused on its mission. The U.S. Equal Employment Opportunity Commission (EEOC) says all employers should take all steps necessary to prevent sexual harassment from occurring, such as affirmatively raising the subject, expressing strong disapproval, developing appropriate sanctions, informing employees of their right to raise, and how to raise, the issue of harassment under Title VII, and developing methods to sensitize all concerned.” The answer is training. You have to adopt a proactive system such as 360training.coms Harassment and Discrimination Prevention program. Doing it now could mean avoiding legal, ethical and reputation problems further down the line.

You recalled an interesting

You reviewed an intriguing conversation with your housemates a year back, about more men turning out to be house spouses. It resembled a red identification of boldness to them. Be that as it may, ladies would in any case do the cleaning. You room was chaotic more often than not. You would refer to the coursework as a reason, regardless of whether your mom got wore out on hearing it. You have a psychological rundown. (You need to extend your assortment of comic books, and this would take as much time as is needed.) You attempted to get a soft cover duplicate of the books (on the understanding rundown), just to see them dissipate on one corner of your room. (What's more, your mom thought there was a major issue with you.) Those days were no more. Your first employment saw you before a glossy work area. It enlivened you, however changing propensities would be something else. Keeping a clean working environment appeared to be reasonable from the start, until you saw a folded paper. And afterward dispersed pens. Spilled espresso on a couple of events. You attempted to be a perfect monstrosity, however this was not normal for you. Be that as it may, you don't need your partners to have a horrible impression of you. Not that they saw these things, as they were excessively invested in their undertakings. (There will be an ideal spot and perfect opportunity to realize them better.) You made plans to adhere to a daily schedule. You figured out how to do it two months after the fact. You liked it, in any event, telling your previous housemates throughout the end of the week. These were the things you gained from the experience: A clean work area is the place you need to be. You've seen flawless workplaces. You were intrigued, with the end goal that it helped you to remember your previous life. You don't need your place to be a blemish, so better take care of business. You need to be progressively beneficial during working hours. Specialists would be an exemption, as the final result would matter the most. It will be ideal to maintain everything in control, however. You need to be in control, which is a decent quality in the workplace. Tidiness is useful for your wellbeing. You don't need be debilitated, as it may influence your partners. (The normal virus can be infectious.) You'll progress admirably in case you're fit as a fiddle. You would avoid the quill duster as much as possible, however. Your previous housemates appeared to be uninterested. It worked out that Matt was moving. (He got ready for marriage. You anticipate his wedding.) Roger quit his place of employment, as he was going to go on a vacation in the Far East. He thought of the landmass, however he made sense of that a goal with less expensive choices would be better. You were green with envy. Roger realized how to spend shrewdly. Budgetary proficiency would be your other issue.

What Is Data Science A Beginners Guide To Data Science

What Is Data Science A Beginners Guide To Data Science What Is Data Science? A Beginners Guide To Data Science Back Home Categories Online Courses Mock Interviews Webinars NEW Community Write for Us Categories Artificial Intelligence AI vs Machine Learning vs Deep LearningMachine Learning AlgorithmsArtificial Intelligence TutorialWhat is Deep LearningDeep Learning TutorialInstall TensorFlowDeep Learning with PythonBackpropagationTensorFlow TutorialConvolutional Neural Network TutorialVIEW ALL BI and Visualization What is TableauTableau TutorialTableau Interview QuestionsWhat is InformaticaInformatica Interview QuestionsPower BI TutorialPower BI Interview QuestionsOLTP vs OLAPQlikView TutorialAdvanced Excel Formulas TutorialVIEW ALL Big Data What is HadoopHadoop ArchitectureHadoop TutorialHadoop Interview QuestionsHadoop EcosystemData Science vs Big Data vs Data AnalyticsWhat is Big DataMapReduce TutorialPig TutorialSpark TutorialSpark Interview QuestionsBig Data TutorialHive TutorialVIEW ALL Blockchain Blockchain TutorialWhat is BlockchainHyperledger FabricWhat Is EthereumEthereum TutorialB lockchain ApplicationsSolidity TutorialBlockchain ProgrammingHow Blockchain WorksVIEW ALL Cloud Computing What is AWSAWS TutorialAWS CertificationAzure Interview QuestionsAzure TutorialWhat Is Cloud ComputingWhat Is SalesforceIoT TutorialSalesforce TutorialSalesforce Interview QuestionsVIEW ALL Cyber Security Cloud SecurityWhat is CryptographyNmap TutorialSQL Injection AttacksHow To Install Kali LinuxHow to become an Ethical Hacker?Footprinting in Ethical HackingNetwork Scanning for Ethical HackingARP SpoofingApplication SecurityVIEW ALL Data Science Python Pandas TutorialWhat is Machine LearningMachine Learning TutorialMachine Learning ProjectsMachine Learning Interview QuestionsWhat Is Data ScienceSAS TutorialR TutorialData Science ProjectsHow to become a data scientistData Science Interview QuestionsData Scientist SalaryVIEW ALL Data Warehousing and ETL What is Data WarehouseDimension Table in Data WarehousingData Warehousing Interview QuestionsData warehouse architectureTalend T utorialTalend ETL ToolTalend Interview QuestionsFact Table and its TypesInformatica TransformationsInformatica TutorialVIEW ALL Databases What is MySQLMySQL Data TypesSQL JoinsSQL Data TypesWhat is MongoDBMongoDB Interview QuestionsMySQL TutorialSQL Interview QuestionsSQL CommandsMySQL Interview QuestionsVIEW ALL DevOps What is DevOpsDevOps vs AgileDevOps ToolsDevOps TutorialHow To Become A DevOps EngineerDevOps Interview QuestionsWhat Is DockerDocker TutorialDocker Interview QuestionsWhat Is ChefWhat Is KubernetesKubernetes TutorialVIEW ALL Front End Web Development What is JavaScript â€" All You Need To Know About JavaScriptJavaScript TutorialJavaScript Interview QuestionsJavaScript FrameworksAngular TutorialAngular Interview QuestionsWhat is REST API?React TutorialReact vs AngularjQuery TutorialNode TutorialReact Interview QuestionsVIEW ALL Mobile Development Android TutorialAndroid Interview QuestionsAndroid ArchitectureAndroid SQLite DatabaseProgramming s Guide To Data Science Last updated on Jun 20,2019 208.7K Views Hemant Sharma23 Comments Bookmark 1 / 10 Blog from Data Science Introduction myMock Interview Service for Real Tech Jobs myMock Interview Service for Real Tech JobsMock interview in latest tech domains i.e JAVA, AI, DEVOPS,etcGet interviewed by leading tech expertsReal time assessment report and video recording Try Data Scientist Mock Interview Become a Certified Professional As the world entered the era of big data, the need for its storage also grew. It was the main challenge and concern for the enterprise industries until 2010. The main focus was on building framework and solutions to store data. Now when Hadoop and other frameworks have successfully solved the problem of storage, the focus has shifted to the processing of this data. Data Science is the secret sauce here. All the ideas which you see in Hollywood sci-fi movies can actually turn into reality by Data Science. Data Science is the future of Artificial Intelligence. Th erefore, it is very important to understand what is Data Science and how can it add value to your business.Edureka 2019 Tech Career Guide is out! Hottest job roles, precise learning paths, industry outlook more in the guide.Downloadnow.In this blog, I will be covering the following topics.The need for Data Science.What is Data Science?How is it different from Business Intelligence (BI) and Data Analysis?The lifecycle of Data Science with the help of a use case.By the end of this blog, you will be able to understand what is Data Science and its role in extracting meaningful insights from the complex and large sets of data all around us.To get in-depth knowledge on Data Science, you can enroll for live Data Science online course by Edureka with 24/7 support and lifetime access.Lets Understand Why We Need Data ScienceTraditionally, the data that we had was mostly structured and small in size, which could be analyzed by using the simple BI tools. Unlike data in the traditional systems which was mostly structured, today most of the data is unstructured or semi-structured. Lets have a look at the data trends in the image given below which shows that by 2020, more than 80 % of the data will be unstructured. This data is generated from different sources like financial logs, text files, multimedia forms, sensors, and instruments. Simple BI tools are not capable ofprocessing this huge volume and variety of data. This is why we need more complex and advanced analytical tools and algorithms for processing, analyzing and drawing meaningful insights out of it.This is not the only reason why Data Science has become so popular. Lets dig deeper and see how Data Science is being usedin various domains.How about if you could understand the precise requirements of your customers from the existing data like the customers past browsing history, purchase history, age and income. No doubt you had all this data earlier too, but now with the vast amount and variety of data, you can t rain models more effectively and recommend the product to your customers with more precision. Wouldnt it be amazing as it will bring more business to your organization?Lets take a different scenario to understand the role of Data Science in decision making.How about if your car had the intelligence to drive you home? The self-driving cars collect live data from sensors, including radars, cameras and lasers to create a map of its surroundings. Based on this data, it takes decisions like when to speed up, when to speed down, when to overtake, where to take a turn making use of advanced machine learning algorithms.Lets see how Data Science can be used in predictive analytics. Lets take weather forecasting as an example. Data from ships, aircrafts, radars, satellites can be collected and analyzed to build models. These models will not only forecast the weather but also help in predicting the occurrence of any natural calamities. It will help you to take appropriate measures beforehand and save many precious lives.Lets have a look at the below infographic to see all the domains where Data Science is creating its impression.Now that you have understood the need of Data Science, lets understand what is Data Science.What is Data Science?Use of the term Data Science is increasingly common, but what does it exactly mean? What skills do you need to become Data Scientist? What is the difference between BI and Data Science? How are decisions and predictions made in Data Science? These are some of the questions that will be answered further. First, lets see what is Data Science. Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. How is this different from what statisticians have been doing for years?The answer lies in the difference between explaining and predicting.As you can see from the above image, a Data Analyst usually explains what is going on by processing history of the data. On the other hand, Data Scientist not only does the exploratory analysis to discover insights from it, but also uses various advanced machine learning algorithms to identify the occurrence of a particular event in the future. A Data Scientist will look at the data from many angles, sometimes angles not known earlier.So, Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science) and machine learning.Predictive causal analytics If you want a model which can predict the possibilities of a particular event in the future, you need to apply predictive causal analytics. Say, if you are providing money on credit, then the probability of customers making future credit payments on time is a matter of concern for you. Here, you can build a model which can perform predictive analytics on the payment history of the customer to predict if the future payments will be on time or not.Pr escriptive analytics: If you want a model which has the intelligence of taking its own decisions and the ability to modify it with dynamic parameters, you certainly need prescriptive analytics for it. This relatively new field is all about providing advice. In other terms, it not only predictsbut suggests a range of prescribed actions and associated outcomes. The best example for this is Googles self-driving car which I had discussed earlier too. The data gathered by vehicles can be used to train self-driving cars. You can run algorithms on this data to bring intelligence to it. This will enable your car to take decisions like when to turn, which path to take, when to slow down or speed up. Machine learning for making predictions If you have transactional data of a finance company and need to build a model to determine the future trend, then machine learning algorithms are the best bet. This falls under the paradigm of supervised learning. It is called supervised because you alrea dy have the data based on which you can train your machines. For example, a fraud detection model can be trained using a historical record of fraudulent purchases.Machine learning for pattern discovery If you dont have the parameters based on which you can make predictions, then you need to find out the hidden patterns within the dataset to be able to make meaningful predictions. This is nothing but the unsupervised model asyou dont have any predefined labels for grouping. The most common algorithm used for pattern discovery is Clustering. Lets say you are working in a telephone company and you need to establish a network by putting towers in a region. Then, you can use the clustering technique to find those tower locations which will ensure that all the users receive optimum signal strength.Lets see how the proportion of above-described approaches differ for Data Analysis as well as Data Science. As you can see in the image below, Data Analysis includes descriptive analytics and p rediction to a certain extent. On the other hand, Data Science is more about Predictive Causal Analytics and Machine Learning.I am sure you might have heard of Business Intelligence (BI) too. Often Data Science is confused with BI. I will state some concise and clear contrasts between the two which will help you in getting a better understanding. Lets have a look.Business Intelligence (BI) vs. Data ScienceBI basically analyzes the previous data to find hindsight and insight to describe the business trends. BI enables you to take data from external and internal sources, prepare it, run queries on it and create dashboards to answer the questions like quarterly revenue analysis or business problems. BI can evaluate the impact of certain events in the near future.Data Science is a more forward-looking approach, an exploratory way with the focus on analyzing the past or current data and predicting the future outcomes with the aim of making informed decisions. It answers the open-ended qu estions as to what and how events occur.Lets have a look at some contrasting features.FeaturesBusiness Intelligence (BI)Data ScienceData SourcesStructured (Usually SQL, often Data Warehouse)Both Structured and Unstructured( logs, cloud data, SQL, NoSQL, text)ApproachStatistics and VisualizationStatistics, Machine Learning, Graph Analysis, Neuro- linguistic Programming (NLP)FocusPast and PresentPresent and FutureToolsPentaho, Microsoft BI,QlikView, RRapidMiner, BigML, Weka, RThis was all about what is Data Science, now lets understand the lifecycle of Data Science.A common mistake made in Data Science projects is rushing into data collection and analysis, without understanding the requirements or even framing the business problem properly. Therefore, it is very important for you to follow all the phases throughout the lifecycle of Data Science to ensure the smooth functioning of the project.Lifecycle of Data ScienceHere is a brief overview of the main phases of the Data Science Life cycle: Phase 1Discovery:Before you begin the project, it is important to understand the various specifications, requirements, priorities and required budget. You must possess the ability to ask the right questions.Here, you assess if you have the required resources present in terms of people, technology, time and data to support the project.In this phase, you also need to frame the business problem and formulate initial hypotheses (IH) to test.Phase 2Data preparation:In this phase, you require analytical sandbox in which you can perform analytics for the entire duration of the project.You need to explore, preprocess and condition data prior to modeling. Further, you will perform ETLT (extract, transform, load and transform) to get data into the sandbox.Lets have a look at the Statistical Analysis flow below. You can use R for data cleaning, transformation, and visualization. This will help you to spot the outliers and establish a relationship between the variables.Once you have cl eaned and prepared the data, its time to do exploratory analytics on it. Lets see how you can achieve that.Phase 3Model planning:Here, you will determine the methods and techniques to draw the relationships between variables.These relationships will set the base for the algorithms which you will implement in the next phase.You will apply Exploratory Data Analytics (EDA) using various statistical formulas and visualization tools. Lets have a look at various model planning tools.R has a complete set of modeling capabilities and provides a good environment for building interpretive models.SQL Analysis services can perform in-database analytics using common data mining functions and basic predictive models.SAS/ACCESS can be used to access data from Hadoop and is used for creating repeatable and reusable model flow diagrams.Although, many tools are present in the market but R is the most commonly used tool.Now that you have got insights into the nature of your data and have decide d the algorithms to be used. In the next stage, you will apply the algorithm and build up a model.Phase 4Model building: In this phase, you will develop datasets for training and testing purposes.You will consider whether your existing tools will suffice for running the models or it will need a more robust environment (like fast and parallel processing).You will analyze various learning techniques like classification, association and clustering to build the model. You can achieve model building through the following tools.Phase 5Operationalize:In this phase, you deliver final reports, briefings, code and technical documents.In addition, sometimes a pilot project is also implemented in a real-time production environment. This will provide you a clear picture of the performance and other related constraints on a small scale before full deployment. Phase 6Communicate results:Now it is important to evaluate if you have been able to achieve your goal that you had planned in the first phase. So, in the last phase, you identify all the key findings, communicate to the stakeholders and determine if the results of the project are a success or a failure based on the criteria developed in Phase 1. Now, I will take a case study to explain you the various phases described above.Case Study: Diabetes PreventionWhat if we could predict the occurrence of diabetes and take appropriate measures beforehand to prevent it? In this use case, we will predict the occurrence of diabetes making use of the entire lifecycle that we discussed earlier. Lets go through the various steps.Step 1:First, we will collect the data based on the medical history of the patient as discussed in Phase 1. You can refer to the sample data below. As you can see, we have the various attributes as mentioned below. Attributes: npreg Number of times pregnant glucose Plasma glucose concentration bp Blood pressure skin Triceps skinfold thickness bmi Body mass index ped Diabetes pedig ree function age Age income IncomeStep 2:Now, once we have the data, we need to clean and prepare the data for data analysis.This data has a lot of inconsistencies like missing values, blank columns, abrupt values and incorrect data format which need to be cleaned. Here, we have organized the data into a single table under different attributes making it look more structured.Lets have a look at the sample data below.This data has a lot of inconsistencies.In the column npreg, one is written in words, whereas it should be in the numeric form like 1. In column bp one of the values is 6600 which is impossible (at least for humans) as bp cannot go up to such huge value. As you can see the Income column is blank and also makes no sense in predicting diabetes. Therefore, it is redundant to have it here and should be removed from the table.So, we will clean and preprocess this data by removing the outliers, filling up the null values and normalizing the data type. If you remember, thi s is our second phase which is data preprocessing.Finally, we get the clean data as shown below which can be used for analysis.Step 3:Now lets do some analysis as discussed earlier in Phase 3.First, we will load the data into the analytical sandbox and apply various statistical functions on it. For example, R has functions like describe which gives us the number of missing values and unique values. We can also use thesummary function which will give us statistical information like mean, median, range, min and max values. Then, we use visualization techniques like histograms, line graphs, box plots to get a fair idea of the distribution of data.Step 4:Now, based on insights derived from the previous step, the best fit for this kind of problem is the decision tree. Lets see how?Since, we already have the major attributes for analysis like npreg, bmi, etc., so we will use supervised learning technique to build amodel here. Further, we have particularly used decision tree because it tak es all attributes into consideration in one go, like the ones which have a linear relationship as well as those which have a non-linear relationship. In our case, we have a linear relationship between npreg and age, whereas the nonlinear relationship between npreg and ped.Decision tree models are also very robust as we can use the different combination of attributes to make various trees and then finally implement the one with the maximum efficiency.Lets have a look at our decision tree.Here, the most important parameter is the level of glucose, so it is our root node. Now, the current node and its value determinethe next important parameter to be taken. It goes on until we get the result in terms of pos or neg. Pos means the tendency of having diabetes is positive and neg means the tendency of having diabetes is negative. If you want to learn more about the implementation of the decision tree, refer this blog How To Create A Perfect Decision TreeStep 5:In this phase, we will run a small pilot project to check if our results are appropriate. We will also look for performance constraints if any. If the results are not accurate, then we need to replan and rebuild the model.Step 6: Once we have executed the project successfully, we will share the output for full deployment.Being a Data Scientist is easier said than done. So, lets see what all you need to be a Data Scientist. A Data Scientist requires skills basicallyfrom three major areas as shown below.As you can see in the above image, you need to acquire various hard skills and soft skills. You need to be good at statistics and mathematics to analyze and visualize data. Needless to say, Machine Learning forms the heart of Data Science and requires you to be good at it. Also, you need to have a solid understanding of the domain you are working in to understand the business problems clearly. Your task does not end here. You should be capable of implementing various algorithms which requiregood coding skills. Fin ally, once you have made certain key decisions, it is important for you to deliver them to the stakeholders. So, good communication will definitely add brownie points to your skills. I urge you to see this Data Science video tutorial that explains what is Data Science and all that we have discussed in the blog. Go ahead, enjoy the video and tell me what you think.What Is Data Science? Data Science Course Data Science Tutorial For Beginners | EdurekaThis Edureka Data Science course video will take you through the need of data science, what is data science, data science use cases for business, BI vs data science, data analytics tools, data science lifecycle along with a demo.In the end, it wont be wrong to say that the future belongs to the Data Scientists. It is predicted that by the end of theyear 2018, there will be a need of around one million Data Scientists. More and more data will provide opportunities to drive key business decisions. It is soon going to change the way we look at the world deluged with data around us.Therefore, a Data Scientist should be highly skilled and motivated to solve the most complex problems.l hope you enjoyed reading my blog and understood what is Data Science.Check out our Data Science certification traininghere, that comes with instructor-led live training and real-life project experience.Recommended videos for you The Whys and Hows of Predictive Modelling-I Watch Now Python Classes Python Programming Tutorial Watch Now Python for Big Data Analytics Watch Now The Whys and Hows of Predictive Modeling-II Watch Now Machine Learning With Python Python Machine Learning Tutorial Watch Now Linear Regression With R Watch Now Business Analytics with R Watch Now Python Loops While, For and Nested Loops in Python Programming Watch Now Sentiment Analysis In Retail Domain Watch Now Business Analytics Decision Tree in R Watch Now Python Tutorial All You Need To Know In Python Programming Watch Now Python List, Tuple, String, Set And Di ctonary Python Sequences Watch Now Diversity Of Python Programming Watch Now Mastering Python : An Excellent tool for Web Scraping and Data Analysis Watch Now Application of Clustering in Data Science Using Real-Time Examples Watch Now Python Programming Learn Python Programming From Scratch Watch Now Web Scraping And Analytics With Python Watch Now Introduction to Business Analytics with R Watch Now Python Numpy Tutorial Arrays In Python Watch Now 3 Scenarios Where Predictive Analytics is a Must Watch NowRecommended blogs for you Scrapy Tutorial: How To Make A Web-Crawler Using Scrapy? 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Introduction to Data Analysis Read Article Data Scientist Salary How Much Does A Data Scientist Earn? Read Article How to Convert a String to integer using Python Read Article Top Data Science Interview Questions For Budding Data Scientists In 2020 Read Article Comments 23 Comments Trending Courses in Data Science Python Certification Training for Data Scienc ...66k Enrolled LearnersWeekend/WeekdayLive Class Reviews 5 (26200) What Is Data Science A Beginners Guide To Data Science What Is Data Science? A Beginners Guide To Data Science Back Home Categories Online Courses Mock Interviews Webinars NEW Community Write for Us Categories Artificial Intelligence AI vs Machine Learning vs Deep LearningMachine Learning AlgorithmsArtificial Intelligence TutorialWhat is Deep LearningDeep Learning TutorialInstall TensorFlowDeep Learning with PythonBackpropagationTensorFlow TutorialConvolutional Neural Network TutorialVIEW ALL BI and Visualization What is TableauTableau TutorialTableau Interview QuestionsWhat is InformaticaInformatica Interview QuestionsPower BI TutorialPower BI Interview QuestionsOLTP vs OLAPQlikView TutorialAdvanced Excel Formulas TutorialVIEW ALL Big Data What is HadoopHadoop ArchitectureHadoop TutorialHadoop Interview QuestionsHadoop EcosystemData Science vs Big Data vs Data AnalyticsWhat is Big DataMapReduce TutorialPig TutorialSpark TutorialSpark Interview QuestionsBig Data TutorialHive TutorialVIEW ALL Blockchain Blockchain TutorialWhat is BlockchainHyperledger FabricWhat Is EthereumEthereum TutorialB lockchain ApplicationsSolidity TutorialBlockchain ProgrammingHow Blockchain WorksVIEW ALL Cloud Computing What is AWSAWS TutorialAWS CertificationAzure Interview QuestionsAzure TutorialWhat Is Cloud ComputingWhat Is SalesforceIoT TutorialSalesforce TutorialSalesforce Interview QuestionsVIEW ALL Cyber Security Cloud SecurityWhat is CryptographyNmap TutorialSQL Injection AttacksHow To Install Kali LinuxHow to become an Ethical Hacker?Footprinting in Ethical HackingNetwork Scanning for Ethical HackingARP SpoofingApplication SecurityVIEW ALL Data Science Python Pandas TutorialWhat is Machine LearningMachine Learning TutorialMachine Learning ProjectsMachine Learning Interview QuestionsWhat Is Data ScienceSAS TutorialR TutorialData Science ProjectsHow to become a data scientistData Science Interview QuestionsData Scientist SalaryVIEW ALL Data Warehousing and ETL What is Data WarehouseDimension Table in Data WarehousingData Warehousing Interview QuestionsData warehouse architectureTalend T utorialTalend ETL ToolTalend Interview QuestionsFact Table and its TypesInformatica TransformationsInformatica TutorialVIEW ALL Databases What is MySQLMySQL Data TypesSQL JoinsSQL Data TypesWhat is MongoDBMongoDB Interview QuestionsMySQL TutorialSQL Interview QuestionsSQL CommandsMySQL Interview QuestionsVIEW ALL DevOps What is DevOpsDevOps vs AgileDevOps ToolsDevOps TutorialHow To Become A DevOps EngineerDevOps Interview QuestionsWhat Is DockerDocker TutorialDocker Interview QuestionsWhat Is ChefWhat Is KubernetesKubernetes TutorialVIEW ALL Front End Web Development What is JavaScript â€" All You Need To Know About JavaScriptJavaScript TutorialJavaScript Interview QuestionsJavaScript FrameworksAngular TutorialAngular Interview QuestionsWhat is REST API?React TutorialReact vs AngularjQuery TutorialNode TutorialReact Interview QuestionsVIEW ALL Mobile Development Android TutorialAndroid Interview QuestionsAndroid ArchitectureAndroid SQLite DatabaseProgramming s Guide To Data Science Last updated on Jun 20,2019 208.7K Views Hemant Sharma23 Comments Bookmark 1 / 10 Blog from Data Science Introduction myMock Interview Service for Real Tech Jobs myMock Interview Service for Real Tech JobsMock interview in latest tech domains i.e JAVA, AI, DEVOPS,etcGet interviewed by leading tech expertsReal time assessment report and video recording Try Data Scientist Mock Interview Become a Certified Professional As the world entered the era of big data, the need for its storage also grew. It was the main challenge and concern for the enterprise industries until 2010. The main focus was on building framework and solutions to store data. Now when Hadoop and other frameworks have successfully solved the problem of storage, the focus has shifted to the processing of this data. Data Science is the secret sauce here. All the ideas which you see in Hollywood sci-fi movies can actually turn into reality by Data Science. Data Science is the future of Artificial Intelligence. Th erefore, it is very important to understand what is Data Science and how can it add value to your business.Edureka 2019 Tech Career Guide is out! Hottest job roles, precise learning paths, industry outlook more in the guide.Downloadnow.In this blog, I will be covering the following topics.The need for Data Science.What is Data Science?How is it different from Business Intelligence (BI) and Data Analysis?The lifecycle of Data Science with the help of a use case.By the end of this blog, you will be able to understand what is Data Science and its role in extracting meaningful insights from the complex and large sets of data all around us.To get in-depth knowledge on Data Science, you can enroll for live Data Science online course by Edureka with 24/7 support and lifetime access.Lets Understand Why We Need Data ScienceTraditionally, the data that we had was mostly structured and small in size, which could be analyzed by using the simple BI tools. Unlike data in the traditional systems which was mostly structured, today most of the data is unstructured or semi-structured. Lets have a look at the data trends in the image given below which shows that by 2020, more than 80 % of the data will be unstructured. This data is generated from different sources like financial logs, text files, multimedia forms, sensors, and instruments. Simple BI tools are not capable ofprocessing this huge volume and variety of data. This is why we need more complex and advanced analytical tools and algorithms for processing, analyzing and drawing meaningful insights out of it.This is not the only reason why Data Science has become so popular. Lets dig deeper and see how Data Science is being usedin various domains.How about if you could understand the precise requirements of your customers from the existing data like the customers past browsing history, purchase history, age and income. No doubt you had all this data earlier too, but now with the vast amount and variety of data, you can t rain models more effectively and recommend the product to your customers with more precision. Wouldnt it be amazing as it will bring more business to your organization?Lets take a different scenario to understand the role of Data Science in decision making.How about if your car had the intelligence to drive you home? The self-driving cars collect live data from sensors, including radars, cameras and lasers to create a map of its surroundings. Based on this data, it takes decisions like when to speed up, when to speed down, when to overtake, where to take a turn making use of advanced machine learning algorithms.Lets see how Data Science can be used in predictive analytics. Lets take weather forecasting as an example. Data from ships, aircrafts, radars, satellites can be collected and analyzed to build models. These models will not only forecast the weather but also help in predicting the occurrence of any natural calamities. It will help you to take appropriate measures beforehand and save many precious lives.Lets have a look at the below infographic to see all the domains where Data Science is creating its impression.Now that you have understood the need of Data Science, lets understand what is Data Science.What is Data Science?Use of the term Data Science is increasingly common, but what does it exactly mean? What skills do you need to become Data Scientist? What is the difference between BI and Data Science? How are decisions and predictions made in Data Science? These are some of the questions that will be answered further. First, lets see what is Data Science. Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. How is this different from what statisticians have been doing for years?The answer lies in the difference between explaining and predicting.As you can see from the above image, a Data Analyst usually explains what is going on by processing history of the data. On the other hand, Data Scientist not only does the exploratory analysis to discover insights from it, but also uses various advanced machine learning algorithms to identify the occurrence of a particular event in the future. A Data Scientist will look at the data from many angles, sometimes angles not known earlier.So, Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science) and machine learning.Predictive causal analytics If you want a model which can predict the possibilities of a particular event in the future, you need to apply predictive causal analytics. Say, if you are providing money on credit, then the probability of customers making future credit payments on time is a matter of concern for you. Here, you can build a model which can perform predictive analytics on the payment history of the customer to predict if the future payments will be on time or not.Pr escriptive analytics: If you want a model which has the intelligence of taking its own decisions and the ability to modify it with dynamic parameters, you certainly need prescriptive analytics for it. This relatively new field is all about providing advice. In other terms, it not only predictsbut suggests a range of prescribed actions and associated outcomes. The best example for this is Googles self-driving car which I had discussed earlier too. The data gathered by vehicles can be used to train self-driving cars. You can run algorithms on this data to bring intelligence to it. This will enable your car to take decisions like when to turn, which path to take, when to slow down or speed up. Machine learning for making predictions If you have transactional data of a finance company and need to build a model to determine the future trend, then machine learning algorithms are the best bet. This falls under the paradigm of supervised learning. It is called supervised because you alrea dy have the data based on which you can train your machines. For example, a fraud detection model can be trained using a historical record of fraudulent purchases.Machine learning for pattern discovery If you dont have the parameters based on which you can make predictions, then you need to find out the hidden patterns within the dataset to be able to make meaningful predictions. This is nothing but the unsupervised model asyou dont have any predefined labels for grouping. The most common algorithm used for pattern discovery is Clustering. Lets say you are working in a telephone company and you need to establish a network by putting towers in a region. Then, you can use the clustering technique to find those tower locations which will ensure that all the users receive optimum signal strength.Lets see how the proportion of above-described approaches differ for Data Analysis as well as Data Science. As you can see in the image below, Data Analysis includes descriptive analytics and p rediction to a certain extent. On the other hand, Data Science is more about Predictive Causal Analytics and Machine Learning.I am sure you might have heard of Business Intelligence (BI) too. Often Data Science is confused with BI. I will state some concise and clear contrasts between the two which will help you in getting a better understanding. Lets have a look.Business Intelligence (BI) vs. Data ScienceBI basically analyzes the previous data to find hindsight and insight to describe the business trends. BI enables you to take data from external and internal sources, prepare it, run queries on it and create dashboards to answer the questions like quarterly revenue analysis or business problems. BI can evaluate the impact of certain events in the near future.Data Science is a more forward-looking approach, an exploratory way with the focus on analyzing the past or current data and predicting the future outcomes with the aim of making informed decisions. It answers the open-ended qu estions as to what and how events occur.Lets have a look at some contrasting features.FeaturesBusiness Intelligence (BI)Data ScienceData SourcesStructured (Usually SQL, often Data Warehouse)Both Structured and Unstructured( logs, cloud data, SQL, NoSQL, text)ApproachStatistics and VisualizationStatistics, Machine Learning, Graph Analysis, Neuro- linguistic Programming (NLP)FocusPast and PresentPresent and FutureToolsPentaho, Microsoft BI,QlikView, RRapidMiner, BigML, Weka, RThis was all about what is Data Science, now lets understand the lifecycle of Data Science.A common mistake made in Data Science projects is rushing into data collection and analysis, without understanding the requirements or even framing the business problem properly. Therefore, it is very important for you to follow all the phases throughout the lifecycle of Data Science to ensure the smooth functioning of the project.Lifecycle of Data ScienceHere is a brief overview of the main phases of the Data Science Life cycle: Phase 1Discovery:Before you begin the project, it is important to understand the various specifications, requirements, priorities and required budget. You must possess the ability to ask the right questions.Here, you assess if you have the required resources present in terms of people, technology, time and data to support the project.In this phase, you also need to frame the business problem and formulate initial hypotheses (IH) to test.Phase 2Data preparation:In this phase, you require analytical sandbox in which you can perform analytics for the entire duration of the project.You need to explore, preprocess and condition data prior to modeling. Further, you will perform ETLT (extract, transform, load and transform) to get data into the sandbox.Lets have a look at the Statistical Analysis flow below. You can use R for data cleaning, transformation, and visualization. This will help you to spot the outliers and establish a relationship between the variables.Once you have cl eaned and prepared the data, its time to do exploratory analytics on it. Lets see how you can achieve that.Phase 3Model planning:Here, you will determine the methods and techniques to draw the relationships between variables.These relationships will set the base for the algorithms which you will implement in the next phase.You will apply Exploratory Data Analytics (EDA) using various statistical formulas and visualization tools. Lets have a look at various model planning tools.R has a complete set of modeling capabilities and provides a good environment for building interpretive models.SQL Analysis services can perform in-database analytics using common data mining functions and basic predictive models.SAS/ACCESS can be used to access data from Hadoop and is used for creating repeatable and reusable model flow diagrams.Although, many tools are present in the market but R is the most commonly used tool.Now that you have got insights into the nature of your data and have decide d the algorithms to be used. In the next stage, you will apply the algorithm and build up a model.Phase 4Model building: In this phase, you will develop datasets for training and testing purposes.You will consider whether your existing tools will suffice for running the models or it will need a more robust environment (like fast and parallel processing).You will analyze various learning techniques like classification, association and clustering to build the model. You can achieve model building through the following tools.Phase 5Operationalize:In this phase, you deliver final reports, briefings, code and technical documents.In addition, sometimes a pilot project is also implemented in a real-time production environment. This will provide you a clear picture of the performance and other related constraints on a small scale before full deployment. Phase 6Communicate results:Now it is important to evaluate if you have been able to achieve your goal that you had planned in the first phase. So, in the last phase, you identify all the key findings, communicate to the stakeholders and determine if the results of the project are a success or a failure based on the criteria developed in Phase 1. Now, I will take a case study to explain you the various phases described above.Case Study: Diabetes PreventionWhat if we could predict the occurrence of diabetes and take appropriate measures beforehand to prevent it? In this use case, we will predict the occurrence of diabetes making use of the entire lifecycle that we discussed earlier. Lets go through the various steps.Step 1:First, we will collect the data based on the medical history of the patient as discussed in Phase 1. You can refer to the sample data below. As you can see, we have the various attributes as mentioned below. Attributes: npreg Number of times pregnant glucose Plasma glucose concentration bp Blood pressure skin Triceps skinfold thickness bmi Body mass index ped Diabetes pedig ree function age Age income IncomeStep 2:Now, once we have the data, we need to clean and prepare the data for data analysis.This data has a lot of inconsistencies like missing values, blank columns, abrupt values and incorrect data format which need to be cleaned. Here, we have organized the data into a single table under different attributes making it look more structured.Lets have a look at the sample data below.This data has a lot of inconsistencies.In the column npreg, one is written in words, whereas it should be in the numeric form like 1. In column bp one of the values is 6600 which is impossible (at least for humans) as bp cannot go up to such huge value. As you can see the Income column is blank and also makes no sense in predicting diabetes. Therefore, it is redundant to have it here and should be removed from the table.So, we will clean and preprocess this data by removing the outliers, filling up the null values and normalizing the data type. If you remember, thi s is our second phase which is data preprocessing.Finally, we get the clean data as shown below which can be used for analysis.Step 3:Now lets do some analysis as discussed earlier in Phase 3.First, we will load the data into the analytical sandbox and apply various statistical functions on it. For example, R has functions like describe which gives us the number of missing values and unique values. We can also use thesummary function which will give us statistical information like mean, median, range, min and max values. Then, we use visualization techniques like histograms, line graphs, box plots to get a fair idea of the distribution of data.Step 4:Now, based on insights derived from the previous step, the best fit for this kind of problem is the decision tree. Lets see how?Since, we already have the major attributes for analysis like npreg, bmi, etc., so we will use supervised learning technique to build amodel here. Further, we have particularly used decision tree because it tak es all attributes into consideration in one go, like the ones which have a linear relationship as well as those which have a non-linear relationship. In our case, we have a linear relationship between npreg and age, whereas the nonlinear relationship between npreg and ped.Decision tree models are also very robust as we can use the different combination of attributes to make various trees and then finally implement the one with the maximum efficiency.Lets have a look at our decision tree.Here, the most important parameter is the level of glucose, so it is our root node. Now, the current node and its value determinethe next important parameter to be taken. It goes on until we get the result in terms of pos or neg. Pos means the tendency of having diabetes is positive and neg means the tendency of having diabetes is negative. If you want to learn more about the implementation of the decision tree, refer this blog How To Create A Perfect Decision TreeStep 5:In this phase, we will run a small pilot project to check if our results are appropriate. We will also look for performance constraints if any. If the results are not accurate, then we need to replan and rebuild the model.Step 6: Once we have executed the project successfully, we will share the output for full deployment.Being a Data Scientist is easier said than done. So, lets see what all you need to be a Data Scientist. A Data Scientist requires skills basicallyfrom three major areas as shown below.As you can see in the above image, you need to acquire various hard skills and soft skills. You need to be good at statistics and mathematics to analyze and visualize data. Needless to say, Machine Learning forms the heart of Data Science and requires you to be good at it. Also, you need to have a solid understanding of the domain you are working in to understand the business problems clearly. Your task does not end here. You should be capable of implementing various algorithms which requiregood coding skills. Fin ally, once you have made certain key decisions, it is important for you to deliver them to the stakeholders. So, good communication will definitely add brownie points to your skills. I urge you to see this Data Science video tutorial that explains what is Data Science and all that we have discussed in the blog. Go ahead, enjoy the video and tell me what you think.What Is Data Science? Data Science Course Data Science Tutorial For Beginners | EdurekaThis Edureka Data Science course video will take you through the need of data science, what is data science, data science use cases for business, BI vs data science, data analytics tools, data science lifecycle along with a demo.In the end, it wont be wrong to say that the future belongs to the Data Scientists. It is predicted that by the end of theyear 2018, there will be a need of around one million Data Scientists. More and more data will provide opportunities to drive key business decisions. It is soon going to change the way we look at the world deluged with data around us.Therefore, a Data Scientist should be highly skilled and motivated to solve the most complex problems.l hope you enjoyed reading my blog and understood what is Data Science.Check out our Data Science certification traininghere, that comes with instructor-led live training and real-life project experience.Recommended videos for you The Whys and Hows of Predictive Modelling-I Watch Now Python Classes Python Programming Tutorial Watch Now Python for Big Data Analytics Watch Now The Whys and Hows of Predictive Modeling-II Watch Now Machine Learning With Python Python Machine Learning Tutorial Watch Now Linear Regression With R Watch Now Business Analytics with R Watch Now Python Loops While, For and Nested Loops in Python Programming Watch Now Sentiment Analysis In Retail Domain Watch Now Business Analytics Decision Tree in R Watch Now Python Tutorial All You Need To Know In Python Programming Watch Now Python List, Tuple, String, Set And Di ctonary Python Sequences Watch Now Diversity Of Python Programming Watch Now Mastering Python : An Excellent tool for Web Scraping and Data Analysis Watch Now Application of Clustering in Data Science Using Real-Time Examples Watch Now Python Programming Learn Python Programming From Scratch Watch Now Web Scraping And Analytics With Python Watch Now Introduction to Business Analytics with R Watch Now Python Numpy Tutorial Arrays In Python Watch Now 3 Scenarios Where Predictive Analytics is a Must Watch NowRecommended blogs for you Scrapy Tutorial: How To Make A Web-Crawler Using Scrapy? 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5 Signs Youre Bored In Your Job (And What To Do About It) - Walrath Recruiting, Inc.

5 Signs Youre Bored In Your Job (And What To Do About It) - Walrath Recruiting, Inc. Its an unfortunate truth that not everything stays exciting forever. Even if your job started out very exciting and engaging, it may not stay that way over time. Feeling like you may have lost interest in your job? These 5 telltale signs indicate that youre definitely bored of your job. Well discuss how you can tell, and what you should do about it! 1. You dread going to work. This is usually the first sign that youre no longer energized by your work. Do you hate Mondays, and dread waking up each morning for work? If so, youre probably not too excited to get to work. It may be a slow realization over time. Think about how you feel in the morning on the drive to work. Is it a good, excited feeling? If not,  its a probable sign that youre bored with your job. 2. You dont find your work exciting or rewarding. If you dread work, its probably because youre not overly excited about the tasks you are doing every day. This is especially likely to happen if you are doing repetitive tasks. When every day is the same thing, it can be very easy to get bored by the repetition. If you never have any new or exciting projects, its not surprising that youve lost interest in what you do. Well discuss how you can address this later! 3. The day drags on. One of the most prevalent symptoms of boredom at work is how time seems to slow. When youre not engaged in your work, you become hypersensitive to the passage of time. No longer can you get lost in a project and have hours pass in the blink of an eye. Instead, minutes seem to stretch out to hours. The more bored you are with what you do, the longer the day seems to drag on. 4. Youre always thinking about other things. If youre bored at work, youll eventually try to fixate on other things to help the time pass. If you find yourself constantly checking sports scores, reading the news, or browsing social media, there is a problem. Theres a reason you looking for other sources to pass the time. Usually, its because youre bored and uninterested in that work. 5. You do the bare minimum every day.   Another telltale sign of boredom at work is doing the absolute bare minimum. If you are doing just enough to get by and spend the least amount of time possible at work, its a sign. Thats not to say you have to overwork yourself! However, if you notice you are doing just enough to get by each day, its representative of a bigger issue. What You Can Do About It So, now you know that youre bored with your job. This leaves you with two options. Firstly, you can try to transform this job into the one you want it to be. Its possible that somewhere along the way you lost the initial interest that attracted you to the position. Think back to why you wanted the job, and what excited you about it. Sometimes we get so caught up in work, we forget to focus on what excited us about that work. Other times, you may need to make some bigger changes. Consider talking to your manager and discussing your frustrations, if you feel comfortable. This may lead to more exciting projects or rewarding work. Attending conferences and seminars is a great way to find inspiration as well! On the other hand, perhaps the job is beyond saving. If theres nothing you can do to salvage your enjoyment in your current job, you have to consider leaving. Begin by asking yourself how you want to approach it, and deciding when to execute. If youre considering just outright quitting, you should also consider the implications of doing so. Either way, you will have to start looking for a new job that excites you and challenges you! Boredom affects many workers across many industries, and although the journey may be different, the end result is the same. Its an unfortunate situation to be in, and getting out of it does require a big decision to be made. Dont allow yourself to get comfortable, keep challenging yourself, and look to further your career!