Unlock the power of Python for data-driven decision-making as you master Python programming fundamentals and dive into data analysis. Acquire essential skills to clean and manipulate data, create insightful visualizations, and perform statistical analysis, all through hands-on projects with real-world datasets.
Master Python for data analysis, machine learning, and automation. Build predictive models, create dynamic dashboards, and unleash the power of data visualization. Launch your career in data science and Python engineering, equipped with Python, NumPy, Pandas, and Matplotlib.
Learn to extract information from databases by writing SQL queries, joining tables, aggregating data, and filtering results. You'll learn PostgreSQL in this class, but the concepts apply equally to other databases such as SQL Server and MySQL.
Become proficient in Microsoft Excel and go from beginner to pro with our hands-on Excel Bootcamp. Learn functions, formulas, Pivot Tables, macros, and time-saving techniques to conquer all the key skills needed to excel in Excel.
Learn how to use Tableau, a powerful data visualization tool, to create interactive and aesthetically pleasing charts, graphs, and maps. This course will teach you how to connect to datasets, analyze and structure data, customize visualizations, and publish your work.
Learn how to use Power BI, a powerful data visualization tool, to create interactive and aesthetically pleasing charts, graphs, and maps. Learn how to connect to datasets, analyze and structure data, customize visualizations, and publish reports.
Master the industry-standard tools and programming languages used by Data Analysts, including Excel, SQL, and Tableau, in this comprehensive classroom training program. Gain hands-on experience working on real-world projects and learn to organize, analyze, summarize, and visualize data to present actionable insights.
Learn the fundamentals of data analytics, including descriptive and inferential statistics, and how different industries and organizations use data for forecasting and decision-making. Explore statistical analysis and modeling, including statistical algorithms, theorems, and models.
Learn Python, SQL, automation, and machine learning to become a Data Scientist. Gain Python programming, data analysis, SQL querying, and predictive modeling skills. Perfect for beginners, this program prepares you for entry-level data science and Python engineering roles. Unlock high-paying job opportunities in the field of data science.
Learn the essential skills needed to become a Data Analyst or Business Analyst, including data analysis, data visualization, and statistical analysis. Gain practical experience through real-world projects and prepare for a successful career in the field of data analytics.
Data science refers to the field of using statistics, mathematics, programming, and data management skills to extract meaningful insights and uncover hidden trends. It involves using different tools, techniques, and processes such as data collection, cleansing, analysis, and visualization. This ultimately leads to better decision-making and enables the company to solve complex problems. Data Scientists also use machine learning and data mining techniques to uncover patterns. All in all, data science professionals play an important role in keeping the company afloat.
This field officially came to significance in the late 20th century, but it can most notably be traced back to early forms of statistics and computer science. In the late 1960s, data analysis laid the groundwork for the field, and by the 1990s, the internet came to prominence and caused the data field to explode. With the internet came an increased need for data professionals and an emphasis on the importance of data management. Since then, the field has shown no signs of slowing down and continues to evolve as new technologies emerge.
Data science skills are applicable across different industries and are highly in-demand. With the right data science training, you can work across various sectors that suit your personality and preferences. For instance, you could work in finance, healthcare, technology, marketing, education, government, or nonprofits. Each sector leverages data differently, but ultimately, data science experts are hired to analyze market trends, optimize business processes, uncover trends, improve decision-making, and boost productivity.
Because there are so many industries that hire Data Scientists, you can also take on numerous projects. For instance, some will develop predictive models to forecast retail sales or recommend different methods for enhancing the user experience for new digital products. You may also work on data visualizations, making it easier to present your findings to stakeholders, clients, and those who may not have technical expertise. No matter what you do, you’ll have a variety of mentally challenging yet fulfilling projects at hand.
Data science skills can also be applied to personal purposes. For example, if you have strong data science acumen, you could analyze your personal finances to devise a more effective budget. You could also think outside the box and use your skills to track your fitness data and enhance your weekly workout routine. You may choose to use your data science skills to study environmental data in your area so you can propose changes in policy at your local government. The possibilities are endless with data science, so there are many ways to use your skills on a professional or personal level.
There are many different skills you will learn in a data science class. You’ll explore statistical analysis, which helps you interpret data. In addition, you’ll likely practice using data visualization tools. Moreover, you’ll learn machine learning skills and data management techniques that ensure your data is organized. Plus, you’ll practice using different programming languages to help you complete your work. Together, these skills will help you tackle real-world challenges effectively.
In a data science class, you will learn how to perform statistical analysis through various hands-on practices. By completing these projects, you will learn how to collect, examine, and interpret data to extract meaningful insights and help the company prepare accordingly. This skill is essential for Data Science professionals as they will need to explain the relationship between the data they analyze and the company’s success. You will also likely gain proficiency in testing and regression analysis.
Most data science classes will cover essential data visualization tools such as Tableau, Power BI, or Matplotlib. With these skills, you’ll be able to create charts, graphs, and dashboards that help transform complex data into visuals that even non-technical audiences can understand. These tools help you communicate your findings to colleagues, clients, and stakeholders alike so you can make a true difference in the company’s success.
Learning how to build algorithms that enable computers to learn from data and make predictions is the foundation of machine learning, which is likely to be covered in a data science class. This area includes techniques such as classification, regression, and clustering to help you tackle real-world problems once you enter the Data Science field. You will develop predictive models in class so you will feel more comfortable doing so in the workplace and be able to build algorithms that enable the computer to operate autonomously.
Data management is an essential part of data science. This process involves collecting, storing, organizing, and accessing data—an essential skill for any data professional. This ensures that data collected by analysts and other data professionals is cleaned, easy to find, and ready for interpretation. It also improves the structure of your databases and enhances your ability to work with large datasets efficiently.
The vast majority of data science classes will cover programming languages that are essential to performing day-to-day job functions. For instance, Python and R will be covered extensively since they allow you to manipulate data, create algorithms, and automate otherwise time-consuming tasks. Learning a programming language will help you create scripts and develop applications that function based on data insights. Ultimately, programming proficiency makes you a more effective Data Science professional.
It can be relatively challenging to learn data science, but this mainly depends on your background and the effort you’re willing to put into it. Some of the specific factors that contribute to its difficulty include the wide range of subjects, tools, and skills you’ll need to master. In addition, mathematical concepts can be challenging and may take time to understand. Despite these obstacles, many people find data science to be an incredibly exciting and rewarding field that offers sufficient challenges with maximum reward. With the right resources, training method, and mindset, you can learn data science with far less difficulty. The key is to take small steps and remain committed to the learning process.
Data science is a relatively complex topic. It involves learning a wide range of subjects and tools such as statistics, machine learning, and data management. As such, it can be difficult to master. For example, mathematical concepts can be difficult, particularly for someone who finds this subject daunting. However, math forms the backbone of data science and is absolutely essential. Moreover, programming can be challenging, especially if you come from a non-technical background. Other challenging aspects include cleaning and preparing data, as well as applying your skills in real-world scenarios. It may be easy to understand the concepts in theory, but putting them into practice is another matter entirely.
How long it takes to learn data science depends on your starting point and your goals. Typically, to learn the basics, you can expect to spend a few weeks in class before you fully grasp the material. On the other hand, becoming a professional will likely require several months to a year of study. This includes learning Python, statistics, and data visualization tools, among other topics. There is a considerable amount of material to cover, which is to be expected. Even beyond formal training, you will likely spend a few years becoming fully confident in your skills. Your pace will also affect the timeline—for example, if you enroll in a part-time course, your learning journey may take twice as long since classes are more spread out.
If you’ve considered learning data science, there’s no time like the present. Fortunately, there are several ways to study, each with its own benefits and limitations. In-person classes are ideal for learners who want a traditional classroom experience with ample interaction, hands-on exposure, and face-to-face discussions. In this format, instructors are readily available to provide assistance and guidance throughout the process. While this is considered one of the most effective learning methods, it also requires time and reliable transportation. If this structure suits your lifestyle and you can accommodate commuting, in-person courses may be your best option.
Live online classes are another great option. Some people have busy schedules that make in-person learning difficult. Others may be unable to attend classes due to their location, transportation limitations, or personal obligations. Some learners simply prefer the flexible yet structured approach that live online courses offer. In this format, students benefit from real-time interaction, flexible scheduling, and additional resources such as lecture recordings. Of course, live online learning may involve occasional technical issues or at-home distractions. Additionally, you might feel that interaction is slightly reduced compared to in-person classes.
As for asynchronous classes, this is the most flexible learning method available. You can learn at your own pace while balancing other responsibilities. This method helps build time management skills and discipline and allows access to courses from a wide range of providers. However, asynchronous classes lack interaction. Without instructors or peers, you are responsible for navigating the course independently. Moreover, there is no feedback, which makes it difficult to assess your progress. Ultimately, asynchronous learning may be suitable for professionals looking to refresh their skills, but it may not be ideal for complete beginners.
There are dozens of free resources online that help you learn data science in more depth. You can explore websites, blogs, discussion boards, social media pages, and video tutorials. Many training centers have resources to assist in the learning process as well. For example, Noble Desktop’s YouTube channel and its Free Seminar page offer insights into different fields, tools, and job titles related to data science. Here, you can watch playlists and review materials to understand data science in greater depth. However, keep in mind that this is not a sufficient path to becoming a professional. If you want to find work as a data science expert, you will need to enroll in some form of professional training, whether at a traditional university or a reputable training center.
Excelling in data science means developing a range of skills, many of which may be supplementary or complementary. Of course, you will need to learn programming alongside data science concepts. In particular, you’ll want to learn languages like Python and R, as they are commonly used in this field. Familiarity with libraries like scikit-learn, NumPy, and Pandas makes handling data far less challenging. Additionally, SQL is best for database management. Using version control systems like Git is also important to learn.
Beyond technical skills, you can also learn tools and software for data visualization. For example, learning how to use Tableau can help you go beyond the basic data science job requirements and advance further in your field. You may also consider sharpening your understanding of business concepts, as understanding the industry and the way your company operates will help you better align your work with organizational goals.
Data science permeates many industries, ranging from finance and technology to automotive manufacturing and tourism. In each industry, Data Scientists hold unique roles and provide valuable contributions that help companies thrive. Read below to learn more about some of Canada’s top industries and how Data Scientists make a difference.
In the finance industry, data science is incredibly important. Data Scientists can work for banks, investment firms, insurance companies, and asset management organizations to help make informed financial decisions. They optimize investment strategies and improve customer experiences. In Canada, this sector plays a crucial role in the national economy, as virtually everyone requires financial management services.
Some of the top finance companies across Canada are both national and international. For example, the Royal Bank of Canada and Toronto-Dominion Bank are among the country’s largest. The Power Corporation of Canada is a major wealth management and holding company. Additionally, organizations such as Bank of America, Deloitte, Citigroup, and Morgan Stanley have a strong presence throughout the country.
The automotive manufacturing industry involves designing and producing motor parts and vehicles such as cars, trucks, buses, and other transportation. This sector is a vital component of the Canadian economy. Data science plays a crucial role in optimizing the manufacturing process, including supply chain management, inventory monitoring, and vehicle safety improvements. Companies hire Data Scientists to oversee production lines, predict maintenance needs, and analyze consumer satisfaction data.
Canada has a robust automotive industry, with several major manufacturers operating within the country. Toyota Motor Manufacturing Canada and BMW Canada Inc. Are leading employers. The Ford Motor Company of Canada Ltd., founded in 1904, remains a longstanding contributor. Volkswagen Group Canada, headquartered in Ontario, is also a top dealership in the country.
Tourism includes goods and services that cater to travelers—such as accommodations, transportation, attractions, tours, and other recreational activities. In Canada, this industry significantly contributes to the country’s economic strength. As a popular destination, Canada attracts millions of visitors annually, creating a demand for Data Scientists who can help companies track trends and improve services. Companies use data science to anticipate travelers’ needs, offer relevant services, and enhance overall operational efficiency.
Canada is home to many successful tourism companies, including Flight Centre Travel Group Limited, Air Canada, WestJet, Rosewood Hotel, Fairmont Pacific Rim, and the Four Seasons Resort and Residences. Popular tourist attractions include Niagara Falls in Ontario, Banff National Park in Alberta, Old Quebec City in Quebec, and the Capilano Suspension Bridge in Vancouver.
The technology industry involves a wide range of skills focused on the development and application of new, innovative tech tools. This can include software development, smart devices, and other digital services. It’s a fast-paced, ever-evolving industry that values enhancing the user experience, optimizing products, and promoting more efficient ways to work and live. Tech companies often hire data science experts to analyze data, automate processes, and improve overall efficiency.
Some of the top tech companies to work for as a Data Scientist include well-known companies such as Microsoft, Amazon, Google, and IBM. Moreover, Shopify and Salesforce Canada are leading ecommerce companies that help businesses manage their sales. In addition, OpenText, located in Ontario, is a leading information management company. All of these companies—and many more—require Data Scientists to make informed decisions and stay competitive in a rapidly evolving market.
Data science careers open a whole world of opportunities that are mentally stimulating, adequately challenging, and financially rewarding. More specifically, Canada offers many opportunities for data science professionals, as numerous industries are thriving. Here are a few positions you can pursue after completing your data science training.
A Data Scientist analyzes complex data to gain insights and develop predictive models. They are adept statisticians and use machine learning algorithms to solve problems, make better decisions, and uncover trends. The national average salary for a Data Scientist in Canada is close to $96,000, according to data compiled by Indeed. This can vary depending on your location—for example, Data Scientists in Vancouver earn around $101,000, while those in Calgary earn about $88,000.
Similar to a Data Scientist, Data Analysts focus on analyzing, interpreting, and visualizing data to help companies determine next steps. They collect data from various sources and create reports to improve profits, customer satisfaction, or operational efficiency. Data Analysts in Canada earn an average salary of $64,000, according to Glassdoor. However, total compensation can range from $57,000 to $94,000 with the right experience and job stability.
A Database Administrator manages and maintains databases. They ensure the database is secure, organized, and reliable, as it often contains vast amounts of sensitive user and company data. Database Administrators in Canada earn an average annual salary of $80,000, per Indeed. In Ottawa, the average is closer to $97,000, while in Montréal, salaries average around $76,000.
Business Analysts bridge the gap between IT and business operations. They use data to identify opportunities for growth and uncover areas for improvement. While they work closely with Data Scientists and Analysts, they focus more heavily on business strategy and financial forecasting. According to Indeed, Business Analysts in Canada typically earn around $76,000 annually. Salaries vary by region, with Vancouver being the highest-paying city, followed by Montréal, Scarborough, and Ottawa.
At Noble Desktop, you can enroll in the Data Science and AI Certificate. In this course, you will learn to analyze data using NumPy and Pandas, create data visualizations with Matplotlib, and apply machine learning algorithms. Projects include animated bar chart races, heat maps, and time series data graphs created in Python. You’ll also gain experience with SQL and other essential libraries. By the end of the course, you'll have a strong portfolio and a Certificate of Completion to demonstrate your skills.
If you want to specialize in data analytics, consider Noble Desktop’s ||CPN395||. This course takes you through the full data analysis process—from data collection to cleaning, analyzing, and visualizing. You’ll focus heavily on using Excel to manipulate and visualize data, along with learning Python and SQL. You'll also practice building interactive dashboards with Tableau and Python. Upon completion, you'll have a refined portfolio and a certificate to verify your hard work.
In the Python for Data Science Bootcamp, you’ll explore foundational programming concepts and learn to manage different types of data. You'll also practice simplifying and reusing code, perform data analysis using NumPy and Pandas, and create visualizations using Matplotlib. Additionally, you’ll learn how to make predictions using scikit-learn. Projects include 3D statistical models, histograms, and dual-axis line charts, all of which make communicating data findings easier to understand.
General Assembly offers a Data Science Short Course in Toronto that equips you to build predictive models using Python. The curriculum also includes statistics and machine learning topics designed to help you handle real-world data challenges.
Lighthouse Labs offers a Data Science Bootcamp in Vancouver, British Columbia, where students learn to extract data from diverse sources, build machine learning models, and use tools like GitHub, Seaborn, Python, and Matplotlib. Students also practice data wrangling with SQL, JSON, and XML.
In Montréal, Le Wagon hosts a Data Science course that teaches students how to transform raw data into actionable insights. Participants learn fundamental statistics, database design using SQL, and techniques for publishing their results in compelling formats.
Have you considered enrolling your data science team in corporate training? This can be an excellent way to enhance team cohesion, strengthen professional relationships, and boost productivity. Consider Noble Desktop for corporate training options, where you can customize lessons to focus on specific techniques, tools, or subject areas of interest. Whether your goal is to upskill or learn something entirely new, Noble’s expert instructors will support your team throughout the entire process. Corporate training is effective because it allows everyone to learn at the same pace and time, ensuring that no team member falls behind. Participants can ask questions, receive clarification, and operate as a more efficient, aligned team.
Plus, there are both in-person and live online options, so your team can join via a video conferencing platform or gather at the workplace for in-person instruction. This provides your team with greater flexibility and control over their training experience. Moreover, Noble Desktop offers benefits beyond the training sessions themselves. For example, you can purchase bulk vouchers for regular classes at a discounted rate. This means that individual team members can continue learning without financial strain. If you and your team are ready to take the next step in advancing as Data Science professionals, consider reaching out to Noble Desktop at corporate@nobledesktop.com to learn more about scheduling, pricing, and available training options.
Tess Robinson is an experienced Writing Professor and Content Writer for Noble Desktop. She has a background in various genres and mediums and specializes in technology topics, including data science. Tess is passionate about expanding her expertise and guiding others as they learn more about the design and technology landscape.