With 600 hours of guided learning and 240 hours of practical experience, students will gain an excellent understanding of theoretical knowledge and experience practical application. Learners can also take a work placement to enhance their understanding of big data by carrying out operations in a real business environment or complete a Capstone project. Modules for the Data Analytics program are highlighted below.
Why study Big Data at TSoM?
The Data Analytics Diploma offered by TSoM offers deep insights into data science and will familiarize you with the terminology and core concepts used in big data applications and systems. The program will help you break into the industry and prep you for big data career opportunities. This Diploma in Data Analytics Co-op places equal emphasis on theoretical concepts and practical application.
Finding big data jobs becomes easier once you have work experience and training. To help students upskill, TSoM has partnered with Amazon Web Services Educate (AWS) and Tableau to provide opportunities to earn microcredentials.
Co-op education experience is offered as part of the Data Analytics program as it combines academia with business experience.
The one-year Diploma in Data Analytics Co-op offered by TSoM is a full-time program lasting 52 weeks. Over this duration, students will spend 24 weeks learning from their tutors and 12 weeks on their co-op education experience. Due to COVID-19, this program is currently being delivered online, and once it’s safe to do so, students can come on-campus for the remainder of their studies.
TSoM’s diploma will help you develop the required expertise for data analyzation and decision-making.
Students keen to pursue Data Analytics must meet the following criteria:
- Have an Ontario Secondary School Diploma or equivalent
- Age limit of 18 years
- Pass the Wonderlic test
- Successful completion of TSoM EAP Level 4 or IELTS 5.5 score, or equivalent, or pass the TSoM English Assessment (written onsite or online with exam proctor)
*Check the English Proficiency page for more information.
In order to successfully progress in the program, you must have access to a personal computer or laptop, with the following minimum configurations:
- CPU: 64-bit x86 Intel or AMD Processor from 2011, or later, with full virtualization support with a minimum 2GHz or faster core speed. Intel i5 or higher with 4 cores or more is recommended and your machine must also support VMware and VirtualBox
- RAM: 6GB or more is recommended
- Storage: Minimum 256GB HDD/SSD or higher
- USB3 support
- Wireless Adapter with N or AC standard
Additionally, TSoM offers access to computer labs on-campus, but availability cannot be guaranteed, and some program software may not be available on all open access computers.
Given that big data benefits are huge and hold the power to transform businesses, anyone looking for a competitive edge can take this program.
Those looking to become a data scientist or work towards effective decision-making using data will also gain from this program as it’s a great way to upskill and stand out from the crowd when searching for roles, as you’ll be armed with contemporary knowledge and expertise. It’s also valuable for entrepreneurs if you’re looking to bring a data-oriented approach to your business.
Skills you will acquire on a Big Data program
The best big data certifications can provide you with expertise crucial to the sector – some of these are highlighted below.
- Structured Query Language (SQL) – this mandatory industry database language is something that all data experts must be familiar with. It’s an essential part of the TSoM Big Data program and is a skill that every employer looks for.
- Microsoft Excel – while knowledge of programming languages like R is necessary for managing larger data sets, advanced Excel methods are also beneficial in this industry.
- Critical thinking – every analyst must possess critical thinking skills which can be further developed on this program as this helps in extracting and analyzing information better.
- R or Python-Statistical Programming – this powerful programming language can perform advanced analysis and easily tackles large data sets which makes it an essential element of every Big Data program.
- Data visualization – alongside working on your findings, you must ensure they are well presented too. To get your point across effectively, it’s important to master data visualization – the art of presenting data through charts, graphs, etc. Tableau’s visualization software is considered an industry-standard analytics tool for this purpose.
- Presentation skills – with data visualization, you need impressive presentation skills and this is something students will pick up on this program.
- Machine learning – artificial intelligence (AI) and predictive analytics are two buzzwords that are trending in the field of business, particularly in the IT sector. While not all analysts are required to understand machine learning, it’s good to get acquainted with related tools and concepts.
There are a number of big data career paths, each offering tremendous growth. The jobs mentioned below are dynamic and offer the chance to advance your knowledge and skills.
- Data scientists – work with prestigious organizations like IBM, Amazon, Google and more to collect, analyze and interpret large volumes of data. This role offers a better understanding of company functions and offers opportunities to improve your performance.
- Data engineers – hired by industry tech giants, data engineers find trends in data sets and work towards designing algorithms that can help get valuable information from raw data.
- Quantitative analysts – these professionals are hired by companies for making effective business and financial decisions. This job is popular across investment banks, private equity firms and insurance companies.
- Quantitative modelers – quantitative modelers are required in trading and sales divisions of investment banks to use algorithms for finding profitable opportunities.
- Business analysts – business analysts focus on shaping detailed business analysis after studying the market and outlining problems and the scope for profit using data quality metrics.
- Information analysts – hired to analyze data, cross-check information and produce reports and forecasts on how the organization should move forward.
Apply in just four easy steps:
- Call us and get in touch with a student advisor
- Get answers to your questions
- Discuss program details and other related information
- Enroll with TSoM and start your program
You can study online and choose from the following start dates
Students can pay their fees online via PayMyTution, money order, debit card or bank wire transfer.
1. How can you apply big data in different industries?
Big data and data science have multiple applications throughout several major industries. In a world dominated by data-backed business decisions, here are some notable applications of big data:
- Human resource management – big data is used for predicting employee attrition based on specific criteria and historical data.
- Telecom industries – service network providers calculate customer loyalty by using data analytics to assess when and which customer is likely to switch to a different service provider.
- Banking and finance – the banking sector is one of the major data analytics users in the world. From assessing the popularity of different credit cards to the ability of customers paying back loans, big data is used extensively.
2. Do I need computer programming knowledge?
This field consists of two broad categories ― data engineers and scientists. Data scientists process data to creating meaningful insights and basic knowledge of computer programming is a bonus, but not mandatory.
Data engineers are responsible for creating different tools and software architecture which can enhance the efficiency and speed of data processing. Therefore, extensive knowledge of computer programming and languages is essential.
3. How does big data differ from data analytics?
Although big data and data analytics are often used interchangeably, there are many differences. Big data refers to an immense volume of raw, unprocessed data which is collected from different sources. This data is unstructured and requires high computer power to process and analyze.
Data analytics refers to the process of extracting meaningful results and insights after the data has been structured and processed. The results gained from data analysis are used to make strategic business decisions.
4. How vast is the sector?
Big data has revolutionized how data is processed and stored across all fields. This has led to a rapid increase in the big data industry globally and in Canada. According to a report by the Information and Communications Technology Council in Canada, the big data market is estimated to be growing at $1.1 billion per year. This figure doesn’t include the indirect operational benefits in other fields.
5. What kinds of jobs are available for a Data Analytics graduate?
As a big data or data analytics professional, you can expect to work in major fields like banking, healthcare or entertainment. Here are a few interesting data analytics positions which can be taken up with a Diploma in Data Analytics:
- Data scientist
- Data engineer
- Quantitative analyst
- Quantitative modeler
- Business analyst
- Information analyst