Tags: Java HomeworkConclusion Of An Essay ExamplesBotox Research PapersConstruction DissertationsWorking At Height CourseHtw Saarland Anmeldung ThesisEssay Context StatementDefinition Literature ReviewChristmas Problem Solving
During a standard application process, you really have two opportunities to show and discuss your projects to the hiring team: a non-conversational opportunity (so either on your resume/CV or on your personal website — more on this later) as well as during an actual interview.
Once you’ve decided on a dataset you’d like to explore, the next step is actually figuring out what questions to answer and what to analyze.
If you recall what I said earlier: the best data science personal projects are eye-catching and skimmable.
He is currently building marketing analytics and automation tools at an early-stage start-up.
Before you start brainstorming topics, it’s important to think about the point of these projects: to show prospective employers you have strong technical skills and a knack for presenting data science results.
And the easiest way to make them that way is to create an awesome visualization.
Weblogic Security-Role-Assignment - Data Analysis Projects
No matter what you analyze, what question you try to answer, or what methodology you use, you need to think about how you will visualize your results.So you might be thinking — skim my data science project? The reality is that (at least during the early stages of the job application process) your application will be skimmed. Now, if a project catches their eye, a recruiter or hiring manager will spend more time reviewing your work.Which brings me to my next point: pick a project topic that will make potential recruiters and hiring managers say, Lastly: how many projects do you really need?I personally believe 2-3 good, interesting side projects is more than enough.Hiring companies just won’t spend the time looking through and reading the 4, 5, 6 projects you have.However, a project like this is in no way necessary for getting hired as a data scientist.This may be a subject for another blog post, but in my experience, aspiring data scientists seem to immediately jump to fancy machine learning or deep learning tutorials — and forget about learning the basics and honing their problem solving, critical thinking, and presentation skills.Once you have settled on how you will analyze your dataset, the next step is to start coding. Then I recommend you create a Git Hub account and read this introduction.What’s most important here is writing clean, easy to read, and well-commented code. Just pin the repos you want people to see and add clear and concise READMEs that explain what your project is about. Git Hub is a fantastic place to demonstrate your programming ability to hiring managers.If you’d like to go for an in-depth machine learning project — that’s great.But if you don’t, rest assured that simply answering an interesting and insightful question with your dataset is more than enough.