Data science is a popular and versatile field in IT. There are a lot of challenges to overcome and a lot of practice that needs to be done to stay relevant in data science. Kf you want to get hired with no prior experience in data science, then that can feel daunting.
So, if you have been studying the field through books and practice and earning a certification or two, you might still be unemployed or still searching for a better position. Will these practices earn you a job in the sector? Well, it is not all you need to land a great job in data science. First of all, you must have a strong and inspiring portfolio that you can present to your clients. Second, make sure that you have enough practice with various data science projects to make your mark on the hiring committee.
5 Data Science Projects to Get You Hired
If you are a little short on these projects or don’t know how to start and what to make out of them, then you have come to the right place. The following are some of the best data science projects that you can work with to build your portfolio and take your skills to a whole other level
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- Data Cleaning
If you want to become more and more valuable with a data science group, then you need to show them how experienced you are at data cleaning. About 80% of the time, you will be cleaning data, so you have to be extremely technical and consistent about it. All you have to do when a new project comes in is to find some messy data sets and then ultimately start cleaning. If you are working with Python, then Pandas is very useful. If you are working with the R language, then you can make great use of the dplyr package. Working with the data cleaning related elements, you must have tknowledge of the following skills:
- Importing data
- Joining the multiple datasets
- Detecting the missing values
- Detecting anomalies
- Imputing for the missing values
- Data quality assurance
The most obvious task once you start out with the process of data cleaning is to make sure that the overall quality of the data doesn’t get any distorted or out of sync even in the slightest. Otherwise, it could have a heavy impact on the rest of the project’s performance.
- Exploratory Data Analysis
Exploratory data analysis (EDA) is another aspect of data science. It’s a realistic approach that generates the questions while at the same time investigating them with visualizations. EDA will allow the analyst to work with data to extract meaningful insight from the data. Different sections are covered, such as working with the business analysis to extract important user consumption and shopping behaviors and then using them for your own advantage.
It is also about making future predictions about various business-related elements based on the insight extracted from the current datasets. It could lead you to look into sections and make leads that you might have otherwise thought out as not important before.
Pandas and Matplotlib are some of the famous libraries in Python. For the R users, you are better off with the ggplot2 package.
- Interactive Data Visualizations
These include tools such as dashboards. These tools are not only useful for the data science teams but also for the business-oriented teams and customers. Dashboards provide the data science enthusiasts with a compatible platform where they can join hands with others and embark on a journey of drawing insights from the data together. These can become an interactive tool for business-oriented customers. Technical details are not that important here, as strategic goals are taken more seriously.
For Python users, the Bokeh and Plotly libraries are perfect for working on dashboards. For the R language, the RStudio’s Shiny package can serve the same purpose and might be worth a try.
- Machine Learning
As part ofAI, machine learning is rising in popularity, and the market as well is shifting to make use of it. Being a data science enthusiast, it would be the biggest mistake not to indulge in this specific domain.
Before you are all ready to develop a highly complex machine learning model for your portfolio, take a step back and try to stick with the basics. Linear regression and logistic regression are both great, and the models are easier to interpret and communicate to upper-level management. Focus on a project that might have a business impact, because at the end of the day that is something everyone likes to work with or adapt to.
- Communication
Now, this might not sound like the most exciting part of this whole project of developing your portfolio, but it is definitely the most important one. Being able to effectively communicate with others is what distinguishes great data science from good data science. Both the slides and notebooks can prove to be a healthy start while developing your communication skills, and keep on practicing until you have got it right.
If you are interested in a dedicated data science certification and want to develop a career around it, then it is suggested to work on your data science portfolio on the side, as this way you will have a bulletproof and thought-out plan for landing a great job for yourself.