![]() ![]() ![]() ![]() Students will be prepared to move on to MATH 3060 Advanced Statistical Techniques for Data Analytics and COMP 4254 Advanced Topics in Data Analytics. Upon successful completion, participants will be able to identify the process of data analysis, the roles of data analytics practitioners and how to create analytics models. This is a required course in the Applied Data Analytics Certificate, ADAC from BCIT Computing. Participants will gain experience with data exploration, testing hypothesis, and predictive analytics. Labs and exercises include: using advanced analytics with SQL, advanced Excel and R Studio to clean data and perform analysis. Discussions include the fundamentals of using the R Language for data analytics. Through a number of problems and case studies, students will build, assess, and deploy different models to gain insights into the data by testing probable hypotheses using primarily SQL and Excel. Starting with an overview of the business cycle for data mining, students learn how to translate a business problem into a data mining problem. Core courses include: statistics for data analytics, data visualization, IT security, Internet and IT Law, and an introduction to Big Data. It introduces data analytics fundamentals to students who have previously acquired skills in Excel spreadsheets, database systems, Structured Query Language (SQL) programming and statistics. Applied Data Analytics, (ADAC) is built on top of ADAD, with focus on data analysis using Excel, SQL, Python, and R, along with BI tools for data-driven decision-making. This hands-on course follows on from COMP 1630, COMP 2362 (or COMP 2364) and MATH 1060. ![]()
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