Data science portfolio — how should you start?

If you have a degree in Data science or any related field from a premier institute, you are likely to get hired, since your employers trust your institute, and the fact that it’s in a subject that’s relevant to their own work..But, if you are self learnt or not an alumnus of a premier institute, your portfolio will be doing a lot of heavy lifting for you.To put it in perspective, if you’re applying for a job with 100 other applicants, and a hiring manager spends 5 hours sifting through the resumes, you have 3 mins to impress him..Having an excellent portfolio will maximize your chances of getting to the phone screening round, compared to a sub par or no portfolio.What is an effective portfolio?A well-rounded portfolio should showcase your value as an asset for the business..The company is investing in you and hence, you have to convince them that you are a good fit for the role they are hiring you for..An asset to the business, necessarily, means you have the skills to generate revenue and opportunities for the business.As a Data Scientist, you add value to a business by:extracting insights from data and helping in decision makingcreating solutions that add direct value to customerscreating solutions that add direct value to the businesscollaborating and sharing knowledge with others in an organizationHow to create a compelling portfolio?Now that the importance of having a strong portfolio is established, it’s time to understand what comprises a portfolio..It should be noted that a portfolio which is versatile in it’s content leaves a lasting impression..Following are some of the things that should be considered:Well structured GitHub projectsAt the minimum, you should have a few projects up on GitHub or your blog, where the code is visible and well-documented..Each project should be well-documented, with a README file both explaining how to set it up, and explaining the characteristics of the data.2..Different projectsDepending on which area you are targeting for employment, you should work on projects that highlight the skills required in that field..For instance, if your goal is to work as a machine learning engineer, you should undertake end-to-end projects involving a lot of machine learning techniques..For a data analyst, your focus should be on data cleansing and data exploration, and as a BI expert, you should work on representing data with charts and figures.2.1 Data cleansingData cleaning requires a hefty amount of work and is an important skill that is expected of a data scientist..A data cleaning project demonstrates that you can reason about data, and can take data from many sources and consolidate it into a single data set.2.2 Exploratory data analysisEDA is a crucial aspect of data science whose significance cannot be stressed enough.. More details

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