Top 6 Data Science Projects To Help You Get Hired in 2020

Considering the fundamentals of Data Science, it is the organization and analysis of large amounts of data. Gaining more knowledge around data science is a terrific practice for any professional in the field or hoping to be in the field. But you must demonstrate an ability to use that knowledge, or prospective employers may be hesitant to hire you.

And picking the right project goes a long way toward showing employers how well you’ve mastered your skills. Now let us begin by taking a look at what the best data science projects have in common. 

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Data Science Projects: What the Best Have in Common

They solve the right problems. Make sure the issues your projects solve are challenging but not so challenging that you get derailed. Find the right balance between complexity and clarity.

They effectively manage the project. Create an outline to get organized and ensure you don’t overlook anything. The outline can contain these stages:

  • Generating a hypothesis.
  • Studying the appropriate data.
  • Cleaning up the data.
  • Assign variables to the data.
  • Create predictive models to back up your hypothesis.
  • Share your results with the stakeholders.

Here’s a list of useful data science examples:

  • Digital advertisement placement automation
  • Getting the most value from a sports team’s rosters
  • Identifying the next generation of world-class athletes
  • Identifying and predicting pandemics
  • Personalizing healthcare recommendations
  • Optimizing shipping routes in real-time
  • Tracking down and eliminating tax fraud

Next, let us look at some of the top data science projects.

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The Top Data Science Projects

Here are six data science project types that attract attention from prospective employers.

1. Data Scrubbing/Cleaning

So the first Data Science project that we will be discussing about is the data scrubbing/cleaning. Cleaning data can be tedious, and the tedium stems from the volume of information data scientists must handle. The task is crucial, though.

And showing an employer that you’re adept at data cleaning makes you more appealing. Begin by choosing a couple of datasets that need a good cleaning. Here’s a link to some useful ones.

After you make your choices, you’ll need the right tools. If you use Python, visit the Pandas library. If you’re more of an R type, take advantage of dplyr.

2. Exploratory Data Analysis

The next data science project that we will be discussing is the Exploratory Data Analysis. Exploratory Data Analysis, or EDA for short, is the process of making sense of your data by investigating it. You then discover patterns, spot trends, check for anomalies, and test hypotheses. Finally, you present your findings using statistics and graphics. Providing statistics and infographics to present your findings.

Say you and your friends want to try a restaurant that no one in the group has visited. You want to choose the right spot, so you check reviews, talk to people who’ve eaten there, and investigate the restaurant’s menu on their website. Congratulations, you’ve conducted exploratory data analysis!

If you’re looking for some useful EDA datasets, check here. Python users should check out the Matplotlib library, while R devotees should use ggplot2.

The next data science project that we'll be discussing about is the Interactive Data Visualization.

3. Interactive Data Visualization

Interactive Data Visualization is about creating graphical elements such as dashboards, maps, and charts to present information.

Everyone from the data science group to corporate-minded end users can benefit from this practice. Imagery catches users’ eyes more effectively than blocks of text, so more people can accurately interpret it, and use it.

Dash by Plotly is a great web-based analytics app for Python users, while R users benefit from RStudio’s Shiny.

Because businesses regard Interactive Data Visualization is critical to decision-making, you will attract attention by choosing this field. Here’s a list of data visualization project ideas to help you start.

4. Clustering Methods

Clustering, in the context of data science, is the practice of grouping similar objects into sets, or clusters. Data scientists use algorithms to cluster the information in a given dataset.

In a clustering project, you’ll show how to classify data and categorize it relative to features and characteristics.

The advantage: Clustering projects grant many data sources for you to use. Pick a few and put together your plan, using algorithms like KNN or DBSCAN to cluster your data.

5. Machine Learning

If you’ve seen stories about self-driving automobiles, then you’ve been exposed to machine learning. Artificial intelligence and machine-learning are waves of the future, and setting up machine learning projects shows that you’re keeping up with the latest trends.

Don’t let machine learning terms like “neural networks” intimidate you. They are easy to implement if you use the right tools, like this Neural Networks tutorial, for instance. 

Put together a simple project—no need to build SkyNet or the HAL 9000. Focus on linear or logic regression. Ensure your projects focus on what that businesses find useful, such as fraud detection, customer attrition, and load defaults.

6. Effective Communication Exercises

If you can’t communicate the importance of data models to end-users, then it’s borderline worthless. Communication is key here.

This project is different because you’ve already done your research, data cleaning, and graphic representations. Now it’s time to demonstrate your ability to present data in clear, relevant, easily understood manners. ts.

Good communication often involves a presentation delivered to an audience (in this case: prospective employers). The delivery should flow smoothly, incorporate visual elements, provide useful information, and it should be tailored to your audience.

Additional Thoughts on the Top Data Science Projects

The purpose of these projects is to show prospective employers that you have the necessary skills to fill a data science position. Build up a portfolio of projects, preferably someplace like GitHub. Data science projects are your showcase. They show the world you know your stuff, and you can apply it across a slew of project types: any type an employer may throw at you.

We live in a data-dependent world, with a tsunami of information. The world, especially the commercial sector, needs data scientists to make sense of that onslaught of information. The right project demonstrates your skill and understanding in this challenging field.

Of course, it’s also wise to increase your knowledge of data science, especially after knowing about the various data science projects, which brings us to the next point.

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Do You Want to Learn About Data Science?

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