Training On Big Data Analytics
SparkData provides extensive training programs on big data analytics and career development, such as SAS/SQL/R/Python/Hive/Spark programming, business analytics, consumer data analysis, database marketing, credit risk analysis and management etc.
In the past 7 years, we have trained over 100 students, and more than 90% of them are successful in stepping into this industry. These students now work as business analysts and data scientists in the major banks such as RBC, TD, BMO, CIBC, Scotia Bank, telecom companies such as Bell Canada, Rogers, Telus, retail companies such as Loblaw’s, Canadian Tire and government sectors as well. Please visit our Career Planning page for details.
Each year, we offer below recurring training programs:

Timeline:
8 weeks, Jan 9— Feb 27, 2021.
Price: $980
SAS/SQL Programming for Data Scientists and Analysts
This training is for beginners with no knowledge and skills in programming. It focuses on the big data analysis by using SAS and SQL programming. You will gain the most important and essential skills for data analysts and scientists via this program.
Furthermore, students can pass SAS Base/Adv exams to gain the global SAS programming certificates via this training.
Outlines:
- Fundamental knowledge and concepts about SAS programming.
- Read in data from various sources such as CSV, delimited files, Excel and databases.
- Data Manipulation and Analysis:
Sort/dedup data, subset and expand data, data aggregation and rollup, data reshape and mutation, create analytic reports. - SQL and Macro Advanced Programming
- Write out data to create external files such as CSV, text, Excel, HTML etc.
- Homework after each lecture and a financial case study for you to practice.

Timeline:
4 weeks, Dec 5, 2020— Jan 9, 2021.
Price: $799
Python Programming for Data Scientists and Analysts
This Python training is for beginners with zero knowledge and skills in programming. It focuses on the numpy and pandas packages which are especially for data processing and manipulation. This program will offer you the most important and essential skills for data scientists in real work.
Outlines:
- Data types in Python: scalar, list, tuple, dict, series, data frame etc.
- Read in data from various sources such as CSV, delimited files, Excel and databases.
- NumPy and pandas packages for data analysis and manipulation:
Sort/dedup data, subset and expand data, data aggregation and rollup, data reshape and mutation, create analytic reports. - Write out data to create external files such as CSV, text, Excel, HTML etc.
- Homework after each lecture and a financial case study for you to practice.

Timeline:
4 months, March — June 2021.
Please contact us for details: info@sparkdata.ca
Big Data and Business Analytics
This training program is suitable for people who have SAS, R or Python programming skills. You can choose any of the above 3 tools to work on projects. This intensive training will focus on both technical skills such as programming, statistical analyses and more important, domain knowledge and business insights. It will enable you to gain the fundamental knowledge and skills for business analytics quickly.
It includes 4 hands-on projects in customer marketing and risk management, covering financial, telecom, and retail and credit risk industries:
- Customer Distribution and Deactivation Analyses of Wireless Telecom Business.
- Financial Campaign Development and Management
- Distribution, Profiling and RFM Analyses of Retail Customers
- Credit Risk Analytics and Management
Note: Each project requires hands-on coding by SAS, R or Python, and a 30 min oral presentation in class.

Timeline:
2 months, upcoming in 2021.
Please contact us for details: info@sparkdata.ca
Machine Learning and Predictive Modeling in Business Analytics
This is an advanced training for people with data analysis experience and statistics knowledge.
Machine learning and predictive modeling use statistics and mathematics to predict outcomes. It is the advanced core technology of many business analytics such as database marketing, customer loyalty and retention, customer churn and win back, credit risk analysis etc.
Through statistical modeling, we can use current customer data to predict the future behaviors of customers such as customer response, customer churn and attrition, credit loan default etc. These predictive models can be used for various business activities such as customer acquisition, upsell/cross sell campaigns, customer retention/winback campaigns, credit loan adjudication, credit risk analysis and management, collections and recovery etc.