classify products into high-selling, low-selling and average-selling. This will aid in making decisions of which products to remain in the inventory. Once the analysis is complete, use the insights gained to make
Use these data analytics portfolio project ideas for beginners to build your own, and get some expert advice for your data portfolio in this guide.
In this blog, we will discuss the best projects in Data Science for beginners to try out and expand their knowledge and skill set. These Data Science project ideas will also help you get a taste of how to deal with real-world Data Science problems. Table of Contents: Top Data Science P...
Data Analysis Abid Ali AwanCertified data scientist, passionate about building ML apps, blogging on data science, and editing. Topics Data Analysis 25 Machine Learning Projects for All Levels 10 Data Visualization Project Ideas for All Levels Top 11 Data Mining Projects to Build Your Portfolio 60+...
Nov projects: The R-programming project for November is a sentiment analysis on song lyrics by different artists. There is lots of data wrangling involved to aggregate different lyrics, and compare the lyrics favored by 2 different artists. The code repository is added to theProjects page here....
To make any decision, big or small, you need the right data at your fingertips. You can dig a little deeper into your audience’s thoughts, ideas, and perspectives with qualitative data and see a much clearer picture. Whiledata analysis in qualitative researchcan be challenging, it is not ...
In such projects, you would use APIs to get the actual data. Through data cleaning and analysis, you would get insights, which you could present in some nice visualizations. Finally, you could post it on reddit, get the feedback and potentially take them into account to improve your project...
The Play of Ideas: Computer-Assisted Qualitative Data Analysis SoftwareIn this paper, we discuss the application of Computer Assisted Qualitative Data Analysis Software (CAQDAS) in the social sciences. We present the different types of data analysis packages and we illustrate in...
controlled experiments or architecture analysis (from Table 2 in [39]) are unsuited. Instead, we used anobservationalevaluation approach [39]. Subsequent versions of DAPS were applied in real data-analytics projects, which were done by students of professional and executive programs in data science...
Communication: Strong written and verbal communication skills are essential for conveying complex ideas and insights to colleagues and stakeholders. Industry Knowledge: Familiarity with the industry in which one operates enhances the effectiveness of data analysis and informs decision-making. Staying...