Four Data Science Projects to Make a Strong Portfolio

Data science is a rapidly growing field and several job seekers are getting attracted to the lucrative jobs this field has to offer. This means the demand for data scientists is increasing as organizations are adopting this technology without hesitation, but landing a job in data science still seems difficult. In order to get a job, you will need to stand out of the crowd and among many applicants. What's important for a data science job? Having a strong portfolio that shows your technical skills along with your soft skills. Overall, your portfolio needs to be impressive.

A Strong Data Science Portfolio

Data science is a vast term that constitutes several subfields like machine learning, computer vision, artificial intelligence, and NLP. Despite having the knowledge of these topics, it's critical to proving your abilities to perform the necessary tasks. For this, you need to include these four types of data science projects in your portfolio.

1. Data Cleaning

For a data scientist, 80% of the job task requires data cleaning. You can only build an efficient and solid model on organized data sets. Cleaning data can take up to hours because researching to figure out the purpose of every column in a data set costs focus. With practice, this task can be performed in lesser time as a data scientist will develop a keen eye to eliminate silos. Hence, employers look for candidates who are experienced in cleaning the data.

2. Exploratory Data Analysis

After your data is cleaned and organized, the next step is to do exploratory data analysis. By doing this, a data scientist can maximize the insights, reveal underlying patterns and structures, extract crucial information from them and detect anomalies. There are many ways to do this and most of them are graphical as it makes it easier to spot patterns and anomalies.

3. Data Visualization

One of the most important data science skills is visually representing data. This is a way of storytelling that lets you tell your findings and insights to the rest of the organization and stakeholders. When a data scientist builds any type of project, the objective is to uncover information that improves the data in any way, and these findings need to be shown.

To practice data visualization and impress your peers, there are many data sets that are publicly available. The most preferred choice is Kaggle.

4. Machine Learning

Machine learning fluency can decide your chances of landing a data science job. It's always advisable to have a strong ML foundation and master the basics. This will strengthen your skill base and give you the confidence to learn advanced skills fasters. Your portfolio should contain projects that will cover all the machine learning basics like regression (liner, logistics, etc), classification algorithms, and clustering.