Blended courses that mix in-person instruction with online platforms are increasingly popular in secondary education. These tools record a rich amount of data on students' study habits and social interactions. Prior research has shown that these metrics are correlated with students' performance in face to face classes. However, predictive models for blended courses are still limited and have not yet succeeded at early prediction or cross-class predictions even for repeated offerings of the same course. In this work, we use data from two offerings of two different undergraduate courses to train and evaluate predictive models on student performance based upon persistent student characteristics including study habits and social interactions. We analyze the performance of these models on the same offering, on different offerings of the same course, and across courses to see how well they generalize. We also evaluate the models on different segments of the courses to determine how early reliable predictions can be made. This work tells us in part how much data is required to make robust predictions and how cross-class data may be used, or not, to boost model performance. The results of this study will help us better understand how similar the study habits, social activities, and the teamwork styles are across semesters for students in each performance category. These trained models also provide an avenue to improve our existing support platforms to better support struggling students early in the semester with the goal of providing timely intervention.
These days, I am sure 90% of LinkedIn traffic contains one of these terms: DS, ML or DL -- acronyms for Data Science, Machine Learning or Deep Learning. Beware of the cliche though: "80% of all the statistics are made on the spot". If you blinked on these acronyms perhaps you need to google a bit and then continue reading the rest of this post. This post has 2 goals. First, it attempts to put all the fellow Data Science learners at ease.
These days, I am sure 90% of LinkedIn traffic contains one of these terms: DS, ML or DL -- acronyms for Data Science, Machine Learning or Deep Learning. Beware of the cliche though: "80% of all the statistics are made on the spot". If you blinked on these acronyms perhaps you need to google a bit and then continue reading the rest of this post. This post has 2 goals. First, it attempts to put all the fellow Data Science learners at ease. Second, if you have just begun on the Data Science, this may serve you as a guide to the next step.
The course is taken by Dinesh Babu a CBAP certified professional and a Senior Business Analyst. He undertakes Data Analysis Training for Indian as well as students overseas.During his teaching with spans over 8 years, his deep knowledge and expertise on the subject has won his several awards the most notable of which is the Best Data Science Teacher in Delhi- 2017. This course gives an introduction into the field of business intelligence and business analytics-domains which extensively use data to take decisions. The course delves into statistics, quantitative analysis, exploratory predictive models, and fact-based management which are indispensable tools for any aspiring data scientist/analyst. Following are the specialties of the program: 1.
The application of artificial intelligence (AI) and machine learning to the business and IT, from intelligent IT operations (AIOps) to service management to software testing, is keeping the data revolution moving at lightning speed. That's why data science remains a popular concentration for computer science students who have the talent for math and analytics. And it's why more organizations are clamoring for data scientists who can help make decisions faster and put their businesses ahead of competitors. In today's age data science expertise with desirable knowledge in relatable fields is rare to find and therefore we have enlisted top 10 data science experts who you can follow in Twitter. Hilary is the Founder of Fast Forward Labs, a machine intelligence research company, and the Data Scientist in Residence at Accel.