Data science involves using mathematical skills, programming languages, and data management to pull out meaningful insights and spot trends within the data. The process is completed by using various tools and techniques to collect, organize, clean, analyze, and visualize data. This helps businesses improve their overall success through more informed decision-making and problem-solving. Data science professionals also develop machine learning models and get into data mining to uncover patterns. All in all, they play a key role in any company’s success.
The field of data science truly started in the late 20th century, but it is rooted in any form of statistics and computer science. The '60s and '70s laid the groundwork for data science, but the internet boom in the '90s allowed the field to take off. With this surge came a slew of new tools, devices, and technological reasons for data science to integrate itself into virtually every industry. As more companies rely on data to understand their consumers and boost sales, data science becomes more relevant.
What Can You Do with Data Science Training?
Data science skills are versatile and in high demand across different industries, so you can find a career path that suits you. For example, you can become a data science professional and help retail companies increase profits. You could also uncover hidden trends in the healthcare industry and help improve patient care. Data Scientists are even hired by the government and nonprofits, so there are many paths you can choose from with data science training.
Because so many industries hire data science experts, you’ll get to work on all kinds of projects. You might build machine learning algorithms, predictive models, or data visualizations. Any project you complete will essentially assist with enhancing user experiences, forecasting trends in the market, or assisting with conveying difficult data concepts. No matter what you dive into, you’ll face some challenging yet exciting and rewarding workplace tasks.
While data science is a professional field, you can also use your skills for personal reasons. If you want to manage your budget a little better, you can use your skills to analyze your finances. You could also track your workout progress and improve your weekly exercise routine. Plus, you could use your data science skills to monitor trends in your community (environmental, crime-related, or otherwise) to propose new policy changes at local town hall meetings. The possibilities are virtually endless, which gives you plenty of reason to learn data science.
What Will I Learn in a Data Science Class?
Data science classes vary in their length, material, and depth, but typically you’ll dive into the most essential skills. This includes statistical analysis to help you interpret data, visualization methods to help with conveying information, and machine learning techniques to build models. Moreover, you’ll study data management strategies and programming languages to support your work and keep your information organized. Together, these skills make an incredible data science education.
Statistical Analysis
In a data science class, you’ll engage in hands-on projects that teach you how to perform statistical analysis. By collecting, examining, and interpreting data, you’ll uncover meaningful insights that can guide your business decisions. It’s essential for data professionals as you need to communicate the relationships between your analysis and the company’s success. You’ll also gain experience with regression analysis which will serve as a useful skill in statistical analysis.
Data Visualization
Most data science courses also discuss the most common data visualization tools such as Tableau, Power BI, or Matplotlib. With these skills, you’ll create enlightening charts, graphs, and dashboards that make complex data far easier to understand. The ability to communicate your findings to others—whether colleagues, stakeholders, or laymen—is essential.
Machine Learning
Machine learning is also high on the list of data science priorities. You’ll learn to develop algorithms that allow computers to learn without human intervention. Moreover, you’ll explore techniques like classification, regression, and clustering to add a more practical approach to your understanding of machine learning. By building predictive models in class, you’ll learn how to apply these skills in the real-world too.
Data Management
Data management is a critical component of data science, which involves the process of collecting, storing, organizing, and accessing data. By managing your data appropriately, you will be able to cleanse, retrieve, and prepare the data for analysis. This keeps the process moving much more smoothly and improves the structure of your databases.
Programming
Above all else, programming languages are necessary for any data science professional. You will focus heavily on languages like Python and R to manipulate data and create algorithms. Additionally, you’ll be able to automate repetitive tasks with these skills. By learning these languages, your data science expertise will flourish and enable you to become a more efficient and versatile data science professional.
How Hard is It to Learn Data Science?
Learning data science can be a bit challenging, but it mostly depends on your background and the effort you put into learning. There’s a lot of material to cover, so it makes sense that there could be a learning curve. Still, difficulty is entirely subjective—what’s easy for one person may be relatively challenging for another. That said, many people discover that data science is an exciting and rewarding field that balances the difficulty with the payoffs. With the right training and resources (in addition to a positive attitude) you will learn data science in no time.
What Are the Most Challenging Parts of Learning Data Science?
Typically, the most challenging concepts in data science revolve around mathematical concepts, like statistics, machine learning, and data management. For many who come from a non-technical background, programming can also present a significant challenge. Additionally, data cleansing, preparation, and analysis can be a time-consuming task that requires a high level of attention. Moreover, many people think it’s difficult to apply these concepts realistically, even if they feel like they can understand them theoretically.
How Long Does It Take to Learn Data Science?
The timeline for learning data science will differ depending on several key factors. If you’re getting into the basics, you might spend a few weeks in class before you feel comfortable. However, if you’re aiming for a professional level of skills, expect to invest several months to a year before mastering the material. More than that, you’ll likely need a couple of years to gain the true confidence of a professional. Because you have to learn so many skills and tools, it may benefit you to partake in continuing learning methods. It’s also important to remember that a part-time or full-time course will affect the timeline as well.
Should I Learn Data Science in Person or Online?
If you’re thinking about diving into data science, now is a great time to start. There are various learning paths you could take. In-person classes offer a traditional feel and provide ample interaction and hands-on exposure to the material. Plus, you’ll get real-time interaction and support from the instructor and peers. In-person classes require a commute, so they can be a bigger commitment. However, if you have a flexible schedule and reliable transportation, you can certainly benefit from an in-person class.
Live online classes are the best option for those who need a more flexible learning method. If you have a busy schedule or just simply can’t make the commute, this is the next best way to learn the material, interact with your instructor and peers, and complete hands-on projects. However, you may encounter technical issues that can derail your learning experience. Still, this method fits better into a busy schedule or for someone who lives a little further away from a training center.
Asynchronous classes are incredibly flexible and allow you to learn at your own pace. These enable you to develop strong self-discipline and time management skills, but there is zero interaction and without an instructor, you will likely feel less sure of whether you are performing the tasks and completing projects adequately. Because of this, asynchronous classes are a better option for current professionals or those who just want to learn a few simple skills rather than a total beginner who wants to become a Data Scientist.
Can I Learn Data Science Free Online?
By exploring free online resources such as websites, discussion boards, social media profiles, and video tutorials, you can get a grasp on data science concepts and tools. You can explore training center websites such as Noble Desktop for their Free Seminar page, or search through their YouTube channel to explore different playlists related to data science. However, it’s important to remember that these resources are fantastic supplemental learning tools but they cannot serve as a substitute for a course. To break into the field, you will need formal training, whether from a university or a training center. Still, it’s smart to continue using free resources as a study tool.
What Should I Learn Alongside Data Science?
To excel in the data science field, consider developing a wide range of both necessary and complementary skills. Programming is a must, particularly in languages like Python and R. Being familiar with libraries such as scikit-learn, Matplotlib, Pandas, and NumPy is also necessary to make handling data far less difficult. Moreover, SQL makes managing databases possible, and understanding version control systems like Git helps immeasurably.
However, you could also learn other tools that make data science projects easier. For instance, you may want to study data visualization tools. Tableau and Excel are some of the most commonly used, but you could also learn how to use PowerPoint, as it’s a helpful presentation tool. You may also want to explore the world of business and finance, as many data science professionals work alongside Financial Analysts and are usually tasked with helping companies make smart business decisions.
Industries That Use Data Science
Data science is needed across various industries, meaning Data Scientists can find work almost anywhere. For example, many can work in finance or technology to help companies make more informed decisions to improve their financial standing or the products created. In addition, Data Scientists can work in tourism and find new ways to streamline operations. Even media and entertainment companies need Data Scientists for anything from ads to recommendations. Truly, becoming a Data Scientist is one of the most diverse and flexible career paths if you are interested in making a difference for the company.
Finance
The finance industry includes banks, insurance companies, investment firms, and other financial services that help their clients manage, invest, and protect their money. Data Scientists are often needed in the finance industry to assess potential risks. They will evaluate the credit risk of a potential client or individuals and companies. Additionally, a Data Scientist may be tasked with creating algorithms that assist with stock trading, enabling individuals and companies to make better decisions. Data science professionals can also easily detect fraud such as unusual financial transactions or data breaches.
In Toronto, there are a considerable number of finance companies where a Data Scientist could find employment. For instance, the Royal Bank of Canada has over 24 locations in Toronto alone. Other notable financial institutions in this city include BMO Financial Group, HSBC, Capital One, Manulife, and Scotiabank.
Technology
Any type of creation, development, or sale of technological products can be clumped into the tech industry. This includes software, hardware, telecommunications, and smart devices. In the tech field, Data Scientists perform many functions. They have a direct role in product development such as analyzing user data to improve software and other digital products. Moreover, they conduct predictive analysis to forecast trends, recommend new products, and improve customer loyalty. They may also work with natural language processing, which is a branch of artificial intelligence (AI) that focuses on mimicking human conversation. This enables Data Scientists to create chatbots that help customers with questions or issues.
As an emerging tech space, Toronto is often considered the Silicon Valley of Canada. There are big companies and small start-ups that emerge every year. The city has many notable tech companies such as Microsoft, Ecobee, Google, Intel, Cisco, IBM, and SAP Canada Inc. With so many opportunities in this sector, it is easy to see why Toronto is a great place for Data Science professionals to live.
Media and Entertainment
Whether it’s a production company or a streaming service, media and entertainment companies need data science professionals. Many may wonder how data science fits into the media and entertainment industry, but these companies thrive on understanding their audiences’ engagement levels, behaviors, and preferences. As such, data science is important in improving consumer satisfaction and marketing tactics. For instance, Data Scientists can develop algorithms to recommend content to viewers on streaming services such as movies and shows on Netflix. Data Scientists can also target advertisements to ensure the intended audience is reached.
Toronto has a significant media and entertainment industry, particularly film and television production. The city hosts the Toronto International Film Festival, which is highly regarded worldwide. In addition, there are many notable companies within this sector such as Corus Entertainment, Canadian Broadcasting Corporation, Live Nation Entertainment, and Rogers Communications. In terms of production companies, Warner Bros. And The Walt Disney Company are strongholds in the region as well.
Tourism
Hotels, travel agencies, airlines, and various attractions are all included under the tourism industry umbrella. This industry fluctuates with the seasons, economic conditions, and consumer preferences, but typically, this can be predicted through data-driven insights. In the tourism industry, Data Scientists can teach companies more about traveler behaviors and preferences so they can offer different travel packages and discounts. In addition, they can use their data analytics skills to gauge customer satisfaction and streamline booking operations. Overall, they play a big role in improving the customer experience.
Tourism is a major industry in Canada, particularly in Toronto. This city is known for its diverse attractions, different cultural events, and unique neighborhoods. For example, there are many outdoor activities such as visiting the Toronto Zoo, the Toronto Islands, and the beaches in this region. You can also explore the museums, historical locations, and eclectic markets. In addition, there are some incredibly successful tourism companies in the area such as Cosmos, St. Clair Travel Agency, Kemp Travel Group, Hostaway, and Flight Centre.
Data Science Job Titles and Salaries
Careers in data science offer a wealth of opportunities that are intellectually engaging as much as they are financially rewarding. If you reside in Toronto and want to explore your options, here are a few job positions you can apply for. This is an ideal landscape to explore once you’ve completed your data science training.
Data Scientist
Data Scientists analyze and interpret complex datasets to generate actionable insights. They also build predictive models, use machine learning techniques, and apply statistical methods to solve problems. The average Data Scientist in Toronto earns roughly $93,000 each year according to Glassdoor. However, there are many ways you can earn additional pay or advance through the levels of seniority.
Data Analyst
A Data Analyst is similar to a Data Scientist in many ways, but they focus on extracting, processing, and visualizing data most of all. This helps companies make more informed decisions and take new paths if necessary. They’ll use different techniques and tools to convey their findings effectively. Indeed reports that Toronto-based Data Analysts earn close to $71,000 annually, but this can vary greatly depending on the company for which you work.
Machine Learning Engineer
Machine Learning Engineers design and implement machine learning models. They also make sure the models are operating as intended, so they will optimize them as needed. They work closely with other data science professionals to ensure these models are effective. Additionally, Machine Learning Engineers can earn a desirable salary in Toronto. Glassdoor shares that they can expect a yearly salary of $105,000, not including any bonuses, commissions, or earnings from profit sharing.
Data Engineer
A Data Engineer maintains the infrastructure for data collection, storage, and processing. Specifically, they will design and optimize the data pipeline, which means data is easy to access and properly formatted so other data science professionals can perform their job with ease. Data Engineers in Toronto can earn roughly $104,000 each year according to Indeed. However, some salaries for Data Engineers can go as high as $200,000, depending on the level of expertise and the company.
Data Science Classes Near Me
At Noble Desktop, you can take their Data Science and AI Certificate which teaches you how to analyze data using NumPy and Pandas, make interactive dashboards with Dash Enterprise, and create machine learning models. You will also write programs in Python to automate otherwise time-consuming tasks. Through hands-on projects such as learning SQL to query databases and create functions or developing 3D stats models, you will feel like a data science pro in no time. You’ll leave the program with a well-developed professional portfolio and a certificate of completion to showcase your skills.
If you want to specialize in data analytics, Noble Desktop also has a Data Analytics Certificate. Here, you’ll learn to interpret data and find trends to offer meaningful insights. You’ll complete projects that give you experience collecting, cleaning, analyzing, and visualizing data with Python and extracting information with SQL. Moreover, you’ll learn how to use Tableau for visualization so everyone can understand your findings. You can add these projects to your portfolio and provide your certificate of completion at any job interview.
For a more in-depth look at machine learning, consider the Python for Data Science and Machine Learning Bootcamp. The instructor will guide students by using Python and its corresponding libraries to clean and analyze data. In addition, you’ll learn to create data visualizations with Matplotlib and Plotly as well as build interactive dashboards with Dash Enterprise. By the end of the bootcamp, you will feel confident in your data science and machine learning abilities and can continue creating, evaluating, and maintaining machine learning algorithms.
Thinkful has a Data Science Flex course where students can prepare for a career in data science. They’ll learn data cleansing with Python and data querying with SQL. In addition, students will learn how to create, train, and evaluate supervised and unsupervised machine learning models. In this program, you will also choose a specialization. It is important to note that you will be accepted into the program once you complete three weeks of paperwork and pass an assessment test.
The Using Data Science Tools in Python course from New Horizons is a quick overview of Python for data analysis. The specific tools covered in this class include Python, NumPy, Pandas, and Motplotlib to perform data analysis and create different data visualizations. You’ll feel up to speed and ready to take on another course if you wish to continue developing your data science skills.
At the Juno College of Technology on Queen Street West, you could enroll in the Data Analytics course that is welcome to beginners. Students will practice using tools like Google Sheets to organize and analyze data. They will then visualize said findings with Tableau and get an introduction to Python for data analytics purposes.
Data Science Corporate Training
If your team has considered corporate training but isn’t sure where to start, you’re in the right place. Noble Desktop offers a variety of corporate training options that provide your group with an effective way to advance in your skills. Corporate training has been known to improve company morale, workplace relationships, and productivity, so it is worth checking out. At Noble Desktop, your group can personalize their training options if they want to learn a specific skill, tool, or subject. That way everyone learns the same thing at the same pace. Moreover, you can choose between in-person and live online options which gives your team more say in how they prefer to learn.
There are many reasons to consider corporate training at Noble Desktop. Of course, the lessons are taught by an expert instructor who has experience in the field. You can also benefit greatly from the additional resources, hands-on practice, and customization options. Most of all, your team can purchase bulk vouchers for regular classes at a discounted rate. This enables your group to continue with their learning journey without the worry of financial strain. If you are ready to explore corporate training, consider reaching out to Noble Desktop at corporate@nobledesktop.com and discuss scheduling, pricing, and training options today.
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.
