If it’s been a while since you’ve mastered a skill, especially one that you don’t use on a daily basis, it’s possible for your knowledge and proficiency to deteriorate some. The deterioration might occur in just a few specific areas, and not across the subject as a whole. The question then becomes how to identify the areas that need a refresher course, so that you can use your time and money wisely before you tackle a data science assessment.
Ai+ Training has recently launched AiQ Assessments featuring assessments on many of the essential and core data science topics including machine learning, deep learning, data visualization, SQL, and NLP. With AiQ, you’ll get the hands-on practice that will enable you to self-assess your data science, machine learning, deep learning, and coding skills.
Once you’ve identified the areas that need improvement, you can strategically choose training courses that address your unique knowledge gaps, saving you from wasting time on a broad overview that might not satisfy your learning needs.
Some of the courses that can help you address these knowledge gaps for data science assessments include:
Algorithms Data Structures & Optimization with Dr. Jon Krohn
Learn the key algorithms for working with data structures, including those for searching, sorting, hashing, and traversing data.
NP Fundamentals in Python with Matt Brems
Join this course for an introduction to NLP, including cleaning text data, transforming data with CountVectorizer and TFIDFVectorizer as well as how to fit machine learning models in scikit-learn and evaluate their performance. You will also learn how to build pipelines and GridSearch over NLP hyperparameters.
Time Series Forecasting (with Python) with Marta Markiewicz
Get an introduction to the most prominent and widely used solutions in time series forecasting, explaining their advantages and disadvantages together with tips and recommendations on the suited-for-purpose model usage.
So, why wait? Check out these AiQ data science Assessments to quickly identify and fill in your knowledge gaps.