Statistics is the scientific discipline responsible for researching and developing methods to collect, organize and analyze data. Implementing statistical methods and models helps us obtain relevant conclusions to make decisions with empirical evidence. But how does it relate to data science? Are both the same thing? What can I do to study and work as a statistician in Canada?
If you want to know how to use statistics in data science, you are in the right place because here we will discuss why statistics is so important for data science, what you have to do to become a statistical data scientist in Canada, and what you can do with Statistics and Data science.
Are you ready to learn about these fascinating sciences and maybe fall in love with them? Let's find out!
Data Science in a Nutshell
As you may know, data science is a multidisciplinary field. Data Science requires mastery of various skills and concepts traditionally associated with Statistics, Mathematics, and Computer Science, among many others. However, we should note that data science is not the meeting point of all these areas but rather an integration of them.
So, what exactly is data science? Data science involves studying data to extract meaningful information and reach objective conclusions and decision-making. Data science combines mathematics, statistics, artificial intelligence (AI), and computer engineering to analyze large amounts of data.
In data science, you will find, for example, concepts from Linear Algebra, Calculus, and Probability, among many others. Data science will have tremendous job prospects since it involves working with emerging technologies such as artificial intelligence, cloud computing, the internet of things (IoT), quantum computing, augmented reality (AR), and virtual reality (VR), among many others.
Why are statistics so important for data science?
Did you know that 90% of the data in the world today has been created impressively in the last two years? If you were wondering why statistics are so important for data science, it is because they have taken a leading role in interpreting and understanding this massive production of information.
The data universe is in an infinite expansion. We cannot compare the amount of data created today with the amount created at the beginning of 2000. And this is why statistics are of great importance since we can objectively understand the world around us. Statistics is of great importance in data analysis because:
- It allows a more accurate description of a massive amount of information.
- It enables results to be prioritized in a meaningful and objective way.
- It provides objective decision-making.
How much do Data Scientists need to know about statistics?
Statistics are at the core of machine learning algorithms in data science. Data scientists use statistics and quantified mathematical models to gather, review, analyze, and draw conclusions from data. So, if you were wondering how much data scientists need to know about statistics, well, quite a lot. Some essential concepts of Statistics that are applied in Data Science are:
- Measures of central tendency: It attempts to describe a data set by identifying the central position within that data set. The three most common values used to measure central tendency are the mean, median, and mode.
- Measures of dispersion: There are five most commonly used measures of dispersion. These are the range, variance, standard deviation, mean deviation, and quartile deviation. They help to get an understanding of the distribution of data.
- Population and sample: The population is the set of possible data values. And a sample contains a part, or a subset, of this population. To emphasize, the sample size is always less than the size of the population from which it is taken.
- Central limit theorem: It is a critical concept in probability theory because it implies that probabilistic and statistical methods that work for normal distributions can be applied to many problems involving other types of distributions.
- Sampling and sampling techniques: It is a statistical analysis technique used to select, manipulate, and analyze a representative subset of the data points to identify patterns and trends in the more extensive data.
- Selection bias: It is a systematic error due to nonrandom sampling of a population, which makes some values in the population less likely to be included than others, which is not equally balanced or objectively represented.
- Correlation: Correlation is a metric that measures the degree to which variables are associated.
In addition, data science is closely related to mathematics, programming, computer science, and artificial intelligence, so it is necessary to have good knowledge in all these areas of study to become the best data scientist in Canada.
Discover the best statistician course on Superprof.
What are some Statistical Concepts for Data Scientists?
Are you ready to test your statistical concepts? Find the correct answer to complete the sentence. Ready? Go!
- It refers to the complete set of individuals, objects, or phenomena:
- Population
- Sample
- Variable
- It is a subset of a population. For example, 50,000 Canadian high school students are a subset of all the students in Canada's population:
- Variable
- Modality
- Sample
- It is the discrepancy between the characteristics of the samples and those of the population:
- Mean
- Bias
- Population
- It is a characteristic that can present different modalities. For example, the___________ "gender" only presents two modalities (woman and man):
- Variable
- Mean
- Mode
- It is the average of the values in the series:
- Mode
- Mean
- Bias
We hope you haven't cheated. Are you ready to continue discovering more about Statistics and Data Science? Let's do it together!
Check out this online statistics course.
How to become a Data Scientist in Canada?
So if you are considering pursuing a career as Data Scientist in Canada, this could probably be one of the best professional decisions you can make. Did you know that Data Science jobs in Canada are among the top fifteen digital occupations with higher growth and demand in the upcoming years, according to the Information and Communications Technology Council (ICTC)?
To become a Data Scientist in Canada, you should focus on getting an undergraduate or postgraduate degree in Data Science and related degrees such as Business information systems, Computer science, Economics, Information Management, or Mathematics and Statistics. In all these types of diplomas, we are sure that you will come across all the statistical concepts that apply to data science.
Some job positions you might get with a Data Science degree in Canada are:
- Data Scientist
- Data Analyst (if you want to know more about Analysts' jobs using Statistics go here!)
- Data Engineer
- Data Architect
- Data Storyteller
- Machine Learning Scientist
- Machine Learning Engineer
- Business Intelligence Developer
- Database Administrator
- Technology Specialized Roles
Remember that you can study for the diploma you want and then choose the professional path of your dreams, even if it is not the expected one. This list of data science roles will only help you get started as you explore more Data Science job positions. For example, if you want you can even work as a Sports Statistician with a Data Science degree.
Best Universities to study Data Science in Canada
Some great universities to go to for a Data Science degree in Canada are:
- The University of British Columbia: Bachelor of Science with a Major in Data Science and a Master of Data Science.
- The University of Toronto: Bachelor of Science in Data Science Specialist and MSc in Applied Computing program.
- Mc Gill University: Undergraduate and graduate programs in Applied Artificial Intelligence, Computer Engineering, Computer Science, Software Engineering, Statistics, and Computer Science, among many others.
- Queen University: Undergraduate and graduate programs in Computing in Data Analytics and Data Science and Statistics.
- HEC Montreal: Undergraduate and graduate programs in Data Science and Business Analytics
Data scientists need a four-year bachelor's degree. Most of the professionals working in the industry have a graduate degree and even a Ph.D. And remember, it's worth the effort! The relationship between pursuing higher education and founding better employment options is significantly positive. Also, if you were asking yourself if statistics and data science is a good major, you have to know that a statistics and data science degree could earn you an average of $76,000 per year in Canada, according to Glassdoor.
However, other professionals in Statistics such as Actuaries and Researchers have also very good job prospects. Get more information about Exploring Statistics as an Actuary and Using Statistics as a Researcher here!
Learn Statistics with Superprof!
Here is a summary of what Data Analysis and Statistics involve and what diplomas might interest you to start the Data Analysis career of your dreams. As we know that being always in constant preparation is the best option to achieve the career of our dreams, you can start practicing and improving your knowledge of Statistics and Data Analysis with Superprof's private tutoring!
Go to our official Superprof page to find thousands of excellent expert tutors in Statistics and associated sciences, such as Math and Computer Science. Are you ready to conquer your dreams?
Welcome to the Superprof family!
Statistical Concepts for Data Scientists: Answers
- Population
- Sample
- Bias
- Variable
- Mean
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