Find out what a career in data analytics could look like. Get details on tasks, skills, salaries, and more.
Learn how to leverage big data and artificial intelligence to solve business problems. Develop skills in data exploration, visualization, and predictive modelling using machine learning techniques. Become job-ready in just 19 months.
What Do Data Analysts Do?
Data analysts gather and analyze information to discover hidden patterns. They turn raw data into insights that companies use to make better decisions.
They are typically responsible for:
- Collecting data from sources like databases and surveys
- Cleaning and organizing the data to make sure it’s accurate and complete
- Spotting trends and patterns in the data
- Producing charts and graphs to represent the data in an easy-to-understand way
- Explaining their findings to business decision makers
Herzing College Data Analytics With AI Training
This training is designed to help you develop the data management and AI skills sought by today’s employers. An internship is included, giving you valuable real-world experience even before you graduate.
- 19-month diploma
- 5-week internship included
- Career support to find your first job after graduation
- Flexible scheduling and online learning format for maximum convenience
- Continuously updated course content, reviewed by an expert curriculum committee
- Frequent start dates; enrol at any time
- Financial assistance may be available for students who qualify
A Quality Online Learning Experience
Herzing offers everything you need to succeed in your online education.
- Easily connect with experienced and knowledgeable instructors whenever you need support
- Programs include three hours of live sessions each week for real-time learning and interaction
- Dedicated academic and technical support
Career Outlook
Professionals with skills in data analytics can find work with:
- Software and IT services firms
- Healthcare organizations
- Banks and insurance companies
- Retailers
- Government agencies
- Educational institutions
- Manufacturing firms
- Transportation and logistics companies
- Energy and utility companies
Potential job titles include:
- Data analyst
- Business intelligence analyst
- Data scientist
- AI/machine learning specialist
- Big data engineer
- Marketing analyst
Study Topics
Our Data Analytics / Artificial Intelligence training covers data management and analysis, cybersecurity, predictive analytics, and advanced machine learning.
You will learn how to:
- Formulate business problems in the context of big data, propose potential solutions, and establish key performance indicators to evaluate the effectiveness of these solutions
- Design and set up the necessary data analytics platforms and environments
- Execute data ingestion processes, transferring data from various sources to the prepared analytics environment
- Conduct comprehensive data exploration, summarization, and visualization to glean insights from the dataset
- Distinguish between target variables and independent variables in the data
- Develop predictive models using suitable machine learning techniques
- Implement the model in a predictive service, enabling its deployment for real-world applications
Click below for full course descriptions.
This foundational course provides an introduction to the field of data analytics. Students will learn the essential concepts, tools, and techniques involved in data collection, cleaning, analysis, and visualization. The course emphasizes practical applications using tools like Excel and introduces statistical methods to derive insights from datasets. Ethical considerations and the societal impacts of data analytics are also explored.
This course focuses on the techniques and strategies involved in preparing data for analysis. Topics include data cleaning, transformation, and scaling, as well as the construction and optimization of data pipelines. Students gain practical skills in managing and preprocessing large datasets to ensure accuracy and relevance for analytical tasks.
This course introduces programming languages commonly used in data analytics, such as Python and R. Students will explore their syntax, data structures, and libraries like NumPy, pandas, and ggplot2. The course emphasizes the development of data analysis scripts, debugging, and visualization techniques for solving real-world problems.
This course provides an in-depth understanding of Microsoft SQL Server, covering topics such as server installation, configuration, and database design. Students will learn to manage security, optimize performance, and implement indexing and full-text searching. Practical applications include creating databases and executing backup and restore operations.
This course explores technologies for efficient data storage, including relational and NoSQL databases, cloud storage, and distributed file systems. Students will learn techniques like data compression, deduplication, and hierarchical storage management, enabling them to design scalable and secure storage solutions.
This course examines the role of data within modern enterprises, focusing on data architecture, governance, and integration. Students will explore strategies for ensuring data quality, security, and compliance while learning how to use data analytics tools to support organizational objectives.
This course introduces statistical methods critical for data analysis, including probability, hypothesis testing, regression analysis, and Bayesian statistics. Through practical exercises, students learn to apply these methods to interpret and analyze data effectively.
This course focuses on the principles of data visualization and communication. Students will learn to design dashboards and reports using visualization tools, transforming complex datasets into clear and engaging visual formats for various audiences.
This course introduces big data concepts, challenges, and technologies, including Hadoop, Spark, and NoSQL databases. Students gain hands-on experience in managing large datasets and extracting insights using distributed computing frameworks and data processing tools.
This course explores cloud computing for data storage, analysis, and management. Students will learn about cloud service models (IaaS, PaaS, SaaS), cloud-based data analytics platforms, and best practices for ensuring data security and compliance in cloud environments.
This course prepares students for career success by teaching resume writing, cover letter creation, and interview techniques. Emphasis is placed on networking, professional communication, and personal branding to enhance employability in competitive job markets.
This course addresses the ethical challenges in data analytics, including issues of privacy, confidentiality, and bias. Students will explore frameworks for ethical decision-making and strategies for advocating responsible data practices in real-world scenarios.
This course introduces machine learning techniques and their applications in predictive analytics. Students will learn to develop, evaluate, and apply predictive models to solve industry-specific challenges, focusing on both theory and hands-on projects.
This course provides a foundational understanding of machine learning, covering supervised and unsupervised learning algorithms such as decision trees, neural networks, and clustering. Students will gain hands-on experience in developing and evaluating machine learning models.
This course explores advanced machine learning techniques, including deep learning and reinforcement learning. Students will learn to design and implement sophisticated models, addressing challenges associated with large-scale data and complex algorithms.
This course examines how AI technologies enhance decision-making processes. Students will explore tools and platforms for intelligent decision-making, analyze real-world case studies, and develop strategies to implement AI-driven solutions.
This course introduces generative AI models such as GANs, VAEs, and transformers. Students will explore their applications in creating synthetic data, art, and text, along with the ethical considerations of deploying generative AI technologies.
The internship provides students with on-the-job training in their field of study. Students apply their knowledge and skills in an industry setting, gaining valuable professional experience.
Instructors
Tania Iram
Instructor, Data Analytics With AI
Admission Requirements
What you need to get started.
- Have a minimum of a Canadian provincial high school diploma or equivalent, or be a mature student
- Pass an entrance test administered by Herzing College
- Be interviewed in detail regarding interest in the field
- Note: admission to some programs may include additional requirements
Specific admission requirements for programs delivered through Herzing College Montreal:
- Regulation respecting the French knowledge requirements for the issue of an Attestation of College Studies Charter of the French language (chapter C-11, s. 88.0.18, 2nd par.).
- 1. The French knowledge requirements that a student must meet in order to be issued an Attestation of College Studies in accordance with section 88.0.18 of the Charter of the French language (chapter C-11) correspond, on the Échelle québécoise des niveaux de compétence en français,
° to level 7 in oral expression and comprehension;
° to level 4 in written expression and comprehension.
- 2. The student shows that the French knowledge requirements provided for in section 1 are met by providing to the college-level educational institution a valid certificate of the results of a standardized test that reports those results.
Not all programs and learning formats available at all campus locations.
A Data Analytics With AI program is registered at and delivered by Herzing College Toronto. Please click for information on program tuition and fees
A Data Analytics With AI program (LEA.EP) is registered at and delivered by Herzing College Montreal. This program leads to an Attestation of Collegial Studies (ACS) recognized by the Ministère de l’Enseignement supérieur (MES).
Herzing College Montreal is a post-secondary institution recognized by the Ministry of Education and Higher Learning (permit number 749758) and a secondary vocational studies institution (permit number 534501).
