Data Science with Python Training in Toronto, Canada

Since Canada enjoys an elite education system, you can have more opportunities here than any other place. Canada is home to several great universities such as Saint Mary's University, Carleton University, Seneca College, Trent University, University of British Columbia, Simon Fraser University etc. These institutes offer prominent courses in data science -

Here is the skill set you need to become a Data Scientist in Canada:

• R Programming

If you want to become a master Data Scientist, you need to have a thorough understanding of at least one analytical tool. Knowledge of R programming helps in solving any data science problem easily.

• Python Coding

One of the most popular languages used in Data Science, Python is simple and versatile. It can take various data formats and help in data processing. It also aids the data scientists in creating and performing operations on a dataset.

• SQL Database and Coding

SQL is a database language that helps the data scientists in accessing, communicating and working on the data. This helps in gaining insights into the formation and structure of a Database. MySQL is another such language that has concise commands that significantly reduce the technical skills required for performing operations on a database.

• Apache Spark

Apache Spark is one of the most popular data sharing technologies. It is a big data computation technology like Hadoop, only it is better. The other difference is that Spark makes cache of its computations in the system memory while Hadoop reads and writes to the disk.

Apache Spark helps data science algorithms run faster. It also prevents loss of data along with help in disseminating the data processing of a large dataset. Spark also can handle the complex unstructured datasets easily. The speed with which it operates helps the data scientist carry out the project more quickly.

• Hadoop Platform

Although it is not a requirement, it is preferred by various data science projects. A study done on LinkedIn proved that for becoming a data science engineer, Hadoop was a leading skill requirement.

• Unstructured Data

Data Scientists work with unstructured data which is not labelled and organized into database values. This unstructured data include videos, blog posts, audio samples, social media posts, customer reviews, etc.

• Machine Learning and Artificial Intelligence

If you want to pursue a career in the field of Data Science, you need to be proficient in Machine Learning and Artificial Intelligence. Following are the concepts that you need to make yourself familiar with:

• • Neural Network
  • Decision tree
  • Reinforcement Learning
  • Logistic regression
  • Adversarial learning
  • Machine learning algorithms, etc.
    
• Data Visualization

Visualization tools like ggplot, d3.js, matplotlib, and Tableau are used to help the data scientist visualize the data. After the processes are performed on a dataset and converted into complex results, this result is converted into a format that is easy to understand. Data Scientists work with data directly and grasp insights from this data. This will also help them to act on the outcomes.

If you want to be a successful Data Science professional, one must have these behavioural traits: 

• Curiosity – An undying curiosity for knowledge is required while dealing with huge amounts of data. 
• Clarity – While working in the field of Data Science, one must ask questions like ‘why’ and ‘so what’. You should always know what and why you are doing it before you clean up or write data. 
• Creativity – One needs to be creative in order to find ways for visualization of data, developing new modeling features and new tools. A good Data Scientist must be able to find out what is missing and what must be included to get the desired results. 
• Skepticism – This is a major trait as it separates the line between a Data Scientist and other creative minds. Skepticism is required to keep creativity in check, help you not get carried away and stay in the real world. 

Canada is home to various leading companies, such as Aviva, Allstate, Capital One, Paytm,  GroupM, Expedia, etc. Here are the 5 proven benefits of the sexiest job of the 21st century:

• High Pay: The rise in demand has made Data Scientist jobs one of the highest paying jobs in the IT industry. The average pay in Canada is $102,500/yr.
• Good bonuses: Some of the other perks of being a Data Scientist are an impressive bonus, signing perks, and equity shares. 
• Education: Due to the demand of knowledge in this field, you need to have either a Masters or a PhD degree to become a successful data scientist. You can try working as a researcher for private or government institutions or as a lecturer. 
• Mobility: Getting a job as a Data Scientist will get you a handsome salary and a higher standard of living as most of the businesses that collect data are in developed countries. 
• Network: Being a Data Scientist opens many gates to the tech world. You can refer to research papers in international journals, and tech talks that will help you network with other Data Scientists. This can be used for referral purposes. 

These are the essential business skills to become a flourishing Data Scientist. Irrespective of where you are situated in Canada or England, one must have the following: 

1. Analytic Problem-Solving – Before you can find a solution to a problem, one must be able to understand and analyze the problem. This helps in getting the clear perspective of the problem and helps you develop the right strategies for the problem.
2. Communication Skills – One of the key responsibilities of a Data Scientist is to communicate deep business and customer analytics to the company.
3. Intellectual Curiosity – If you don’t ask questions like ‘why’, data science is not the field for you. If you want to produce value to the commercial enterprise, you need to create a combination of thirst and curiosity.
4. Industry Knowledge – Having a strong knowledge of the industry you are working in is one of the most important skills a Data Scientist can have. This will give a clear idea of what should be attended to and what should be ignored. 

Here is what you need to do to brush up your Data Science skills and get a job as a Data Scientist:

• Boot camps: If you want to brush up on the basics of Python, boot camps are the way to go. Lasting for about 4-5 days, boot camps offer theoretical knowledge and hands-on experience. 
• MOOC courses: There are several online courses that you can start to understand the latest trends in the IT industry. Taught by Data Science experts, these courses come with assignments that polish your implementation skills. 
• Certifications: If you want to add additional skills to your CV and improve it, certification is an option for you. There are several famous data science certifications including:
  • Cloudera Certified Associate – Data Analyst
  • Cloudera Certified Professional – CCP Data Engineer
  • Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
• Projects: Taking on a project will help you find new answers to already answered questions. It will help you attain refined thinking and skills. 
• Competitions: You can try participating in competitions like Kaggle that improve your problem-solving skills by making you find a solution that fulfills all the requirements. 

Data has become an inevitable part of our lives. Companies collect this data and use this for improving the customer experience, thereby increasing their profits. This requires hiring qualified and experienced Data Scientists. The following kind of companies offers Data Scientist jobs:

• Small companies with fewer data and fewer resources use Google analytics for the data analysis. 
• Mid-sized companies have more data so they need someone to apply Machine Learning techniques to that data to gain useful insights.
• Big companies have a team of Data Scientists that are specialized in ML, Visualization, etc. 

If you want to be successful in the field of Data Science, you need to practice and work your way through the Data Science problems. Here are some ways how you can practice your Data Science skills according to your level:

• Beginner Level
  • Iris Data Set: It is the most resourceful, easy, versatile and popular dataset available in the field of pattern recognition. If you want to learn various classification techniques, it is the easiest dataset for you. This is for beginners who want to kick start their career in the field of Data Science. It has 4 columns and 50 rows. Practice Problem: Predicting what the class of the flower is on the basis of these parameters. 
  • Loan Prediction Data Set: The banking section is one of the fields that make the most use of Data analytics and data science methodologies. The Loan Prediction Data Set will give you experience working on concepts that are used in banking and insurance. This includes the strategies implemented, the challenges faced, variables that affect the outcome, etc. It is a classification problem data set consisting of 13 columns and 615 rows.
    Practice Problem: This includes predicting if the bank will give you a loan or not. 
  • Bigmart Sales Data Set: Retail sector is an industry that heavily relies on Data analytics for the optimization of their business processes. Data Science and Business analytics is used for customizations, product bundling, inventory management, etc. Mainly used in Regression problems, the Bigmart Sales data set has 12 variables and 8523 rows.
    Practice Problem: Predicting the sales of the retail store. 
• Intermediate Level:
  • Black Friday Data Set: This dataset consists of the sales transactions data of a retail store. If you want to explore and expand your engineering skills, this data set is for you. It will give you an understanding of the daily shopping experiences of millions of customers. It is a regression problem with 12 columns and 550,069 rows.
    Practice Problem: The problem is to find out the amount of total purchase.
• Human Activity Recognition Data Set: This Data Set consists of 30 human subjects that were collected using smartphone’s recordings using inertial sensors. It has 561 columns and 10,299 rows.
  Practice Problem: It is used for the prediction of human activity.
  • Text Mining Data Set: This Data Set was obtained by the Siam Text Mining Competition, held in 2007. It consists of reports of aviation safety issues encountered on certain flights. With 30,438 rows and 21,519 columns, this dataset is multi-classification and high dimensional problem.
    Practice Problem: The problem is the classification of the documents based on their labels. 
• Advanced Level:
  • Urban Sound Classification: All the beginners in the field of Machine Learning go through basic and simple Machine learning problems like Titanic survival prediction, etc. However, these problems do not offer a taste of real-world issues. For the implementation of machine learning techniques to real-world problems, you can try the urban sound classification dataset. It contains 8,732 urban sounds categorized into 10 classes. This will introduce you to audio processing in the real world scenarios.
    Practice Problem: Categorizing a sound obtained from audio. 
  • Identify the digits data set: Consisting of 7000 images, with the size of 31MB, with dimensions of 28X28 each, this data set helps the developers in studying, analyzing and recognizing the different elements present in the image.
    Practice Problem: Identification of elements in a given image
  • Vox Celebrity Data Set: Audio processing is an important field in Deep learning. This dataset is for large scale speaker identification. Words spoken by celebrities are extracted from YouTube videos. It is used in identifying and isolating speech recognition. It consists of 100,000 words from 1,251 celebrities.
    Practice Problem: Identify the voice of the celebrity.

Becoming a successful data scientist involved following the below-mentioned steps:

1. Getting started: First, you need to select a programming language that you are comfortable in. The most common programming languages used in the field of Data Science are R and Python. 
2. Mathematics and statistics: The field of Data Science requires dealing with data that can be textual, numerical or image. A Data Scientist has to decipher patterns and relationships between these data. So, basic understanding of algebra and statistics is essential. 
3. Data visualization: Data visualization is one of the key steps while becoming a top-notch data scientist. You need to help the non-technical teams to understand the content as well. So, it is important to learn data visualization to communicate with the end users. 
4. ML and Deep learning: Deep learning and Machine Learning skills are required for the analysis of data and are a must on your CV. 

Many people often wonder how to start preparing for a career in the field of Data Science. These are the essential steps you must follow, be it in Canada or the United States -

• Degree/certificate: It is important that you start with covering your fundamentals through a basic course. This can be either an online or an offline course. You will be able to learn the application of cutting-edge tools that will help you get a tremendous career growth. The field of Data Science demands continuous learning due to the rapid advancements. Data Scientists have more PhDs than any other job in the IT industry. 
• Unstructured data: The most important part of the roles of a Data Scientist is the identification of patterns in the data. Usually this data is unstructured and can’t be fit into a database. Structuring this data takes a lot of work and makes the job a lot more complex. As a data scientist, you must have the ability to understand and manipulate the data.  
• Software and Frameworks: As a Data Scientist, you would have to deal with large volumes of unstructured data. For this, you need to be comfortable in using a programming language, software and frameworks involved in Data Science. 
  • R is one such programming language that has a steep learning curve. It is one of the most used programming languages for finding solutions to statistical problems. It is the preferred language for the analysis by about 43% of Data Scientists. 
  • When the amount of data to be analyzed is way too large as compared to the available memory, the majority of Data Scientist use the Hadoop framework. Hadoop is capable of conveying data to various points on the machine. Second to Hadoop, Spark is a popular choice of framework. It is a faster option for computational work. Unlike Hadoop, it prevents the loss of data. 
  • Once you have a complete understanding of the programming language and the framework, you need to get a thorough knowledge of databases. A Data Scientist must be proficient in SQL queries. 
• Machine learning and Deep Learning: Once you have collected and prepared the data, the next step is the application of Machine Learning algorithms on the data for the analysis. Deep learning is used to train the model to work with the provided data.
• Data visualization: Most of the data science projects involve making business decisions after analyzing and visualizing the data. It is the job of a Data Scientist to analyze the data and provide it to the management in the form of charts and graphs. There are several tools available for data visualization like ggplot2, matplotlib, etc. 

When it comes to Data Scientists, 46% of them have a PhD, while 88% of them have a Master’s degree. Canada offers several opportunities as it is home to several great universities such as Saint Mary's University, Carleton University, Seneca College, Trent University, University of British Columbia, Simon Fraser University etc. A degree will help you land a job as a Data Scientist because of the following reasons:

• Networking – You will get an opportunity to connect and make friends and acquaintances while getting the degree. Networking will help you a lot in landing a job later.
• Structured learning – While you are in college, earning your degree, you will have to follow a schedule and keep up with the curriculum.
• Internships – During your course, you will have to get through an internship that will give you the practical hands-on experience.
• Recognized academic qualifications for your résumé – If you have a degree from a reputed institution, it will look good on your resume and help you get a head-start in the race of getting a good job. 

Canada has some of the best educational institutions in the world. There are numerous globally-acclaimed universities such as the Simon Fraser University, the University of British Columbia, Carleton University, Saint Mary's University, Seneca College, Trent University, etc.which offer advanced degrees. You need to grade yourself on the basis of the below-mentioned scorecard to determine for sure if you need a Master’s degree in Data Science or not. If your total is more than 6 points, it is advised for you to get a Master’s degree:

• Strong background in STEM (Science/Technology/Engineering/Management): 0 point
• Weak STEM background (biochemistry/biology/economics or another similar degree/diploma): 2 points
• Non-STEM background: 5 points
• < 1 year of experience in Python: 3 points
• Never been part of a job that requires you to code: 3 points
• Not good at independent learning: 4 points
• Can’t understand when you find out that this scorecard is a regression algorithm: 1 point

If you want to become a successful data scientist, you must have the knowledge of a programming language as it is one of the most essential skills. Here is why it is so important:

• Data sets: While working in Data Science, one has to deal with large volumes of data. To analyze these large data sets, knowledge of programming is essential. 
• Statistics: If a person has the knowledge of programming, it becomes easier to work with statistics. The knowledge of statistics will be of no use, if the data scientist doesn’t have the knowledge to implement this knowledge. 
• Framework: To use Data Science in an efficient and effective manner, programming ability is a must. Programming languages help the data scientist build a framework that an organization can use to analyze experiments, manage the data pipeline, and visualize data. This data can be accessed by the right person at any time. This takes away the need for manual work a lot as the whole process is automatic.

Here is what you need to learn to get a job as a Data Scientist:

• Getting started: First, you need to select a programming language that you are comfortable in. Python and R are the most common programming languages used in Data Science. You need to understand the actual meaning of Data Science and what are the roles and responsibilities of a Data Scientist. 
• Mathematics: When it comes to Data Science, analyzing raw data and finding relationships and patterns is essential. So, it is very important that you have a good grasp of mathematics and statistics. You need to pay special attention to Probability, Inferential statistics, Descriptive statistics, and Linear algebra. 
• Libraries: The process involved in Data Science ranges from preprocessing the data to plotting the structured data to applying Machine Learning algorithms. Here are some of the famous libraries:
  • Scikit-learn
  • Pandas
  • Matplotlib
  • ggplot2
  • SciPy
  • NumPy
• Data visualization: It is the job of a Data Scientist to find the pattern in the data and make it as simple as possible. This can be done by using a graph to visualize the data. There are several libraries that can be used for this including: 
  • Ggplot2 - R
  • Matplotlib - Python
• Data preprocessing: Data is available in the form of structured as well as unstructured data. If the data is unstructured, data scientists need to preprocess the data to make it ready for the analysis. Feature engineering and variable selection is used for preprocessing. Once the data is available in the structured form, it can be injected into the Machine Learning tool for the analysis. 
• ML and Deep learning: To be a data scientist, it is a must to have deep learning skills along with Machine learning skills. While dealing with a huge set of data, deep learning is preferred as the deep learning algorithms are designed to work for this specific purpose. You should have a clear understanding of topics like CNN, RNN, and neural networks. 
• Natural Language processing: Proficiency in Natural Language Processing is a must for every data scientist. This involves the classification and processing of data in text form.  
• Polishing skills: If you want to exhibit your data science skills, competitions like Kaggle are the way to go. Apart from the online competitions, you can also experiment and explore the field by creating your own projects. 

Follow the below 5 steps to prepare for the job of a Data Scientist:

• Studying: Study the following topics for the preparation of the interview:
  • Statistics
  • Statistical models
  • Probability
  • Understanding of neural networks
  • Machine Learning
• Meetups and conferences: You need to build your network and expand your connections. This can be done by visiting data science conferences and tech meetups.
• Competitions: Next, you need to polish your skills by implementing and testing them in competitions like Kaggle. 
• Referral: Referrals can help you a lot in getting a job as a Data Scientist. So make sure that your LinkedIn profile is up to date. 
• Interview: If you have done all the above-mentioned steps, you can go for the interview. If you are not able to get the job, learn from your mistakes. Find answers to the questions you weren’t able to answer and do well the next time.

The responsibility of a data scientist is to analyze the vast amount of structured and unstructured data, look for patterns, and inference information. This is done in order to meet the needs and goals of the business.  

Today, tons of data is generated every day and this has increased the importance of a Data Scientist. This is because the generated data is filled with ideas and patterns that can help in the advancement of the business. It is the job of a Data Scientist to study the data, extract the relevant information and make sense of this data so that it can benefit the business. 

Data Scientist Roles & Responsibilities:

• First, the data, relevant to the business, is fetched. This data can be structured as well as unstructured. 
• Next comes the organization and analysis of the data. 
• After this, programs, tools, and Machine Learning techniques are created to make sense of the data.
• Lastly, statistical analysis is performed on this data to predict future outcomes. 

Harvard Review 2012 declared Data Scientist as the hottest job of the 21st century. The base salary of a Data Scientist is 36% higher than any other predictive analysis job due to high demand and less number of data scientists. Toronto offers good opportunities as it is home to many leading tech companies, including Oracle, Cisco, Shopify etc 

The earning of a Data Scientist depends on the following factors:

• Type of company
  • Governmental & Education sector: Lowest pay 
  • Public: Medium pay 
  • Startups: Highest pay 
• Roles and responsibilities
  • Data analyst: $60,212/yr
  • Database Administrator: $80,000/yr
  • Data scientist: $102,500/yr

To be a successful Data Scientist, one must have the knowledge of computer science, math and trend recognition. A Data Scientist’s job is deciphering large volumes of data and then mining the data to get the relevant part. Next, this relevant data is analyzed to make predictions regarding the similar data in the future. The Data Science career path can be explained in the following way:

• Business Intelligence Analyst: A Business Intelligence Analyst’s job is to figure out the latest trends of the business and the market. This can be done by analyzing the data to get a clear picture of where their business stands in the market. 
• Data Mining Engineer: The job of a Data Mining Engineer is to examine the needs of the business by studying the data. They also do the job as a third party. Apart from this, a Data Mining Engineer is also responsible for aiding in the data analysis by creating a sophisticated algorithm. 
• Data Architect: A Data Architect is responsible for working alongside system developers, designers, and users for creating blueprints. These blueprints are then used by the data management system for integrating, maintaining, centralizing and protecting the data sources. 
• Data Scientist: A Data Scientist’s job is the pursuit of a business case after analyzing the data, developing hypotheses and an understanding of data required for exploring patterns. Next, they also create the system and the algorithm for the productive use of the data. This furthers the interests of the business. 
• Senior Data Scientist: The role of a Senior Data Scientist is anticipating the needs of the business in the future. They are also responsible for shaping the system, data analysis and the projects to suit the needs of the business. 

The top professional associations and groups for data scientists in Canada include the following – 

• Getting the most out of your Data Scientist
• Power Systems Greater Toronto Area Meetup
• Enterprise Data Science at Scale
• Cognitive, AI & Data Science Meetup
• Data Science: Classification Algorithms in Python

Networking with other data scientist is very essential as referrals will be very effective when you are looking for a job. Here is how you can connect with potential Data Scientist employees:

• Data science conference
• Social gatherings like Meetup 
• Online platform like LinkedIn

The top 8 Data Science career opportunities in 2019 are:

1. Data Scientist
2. Data Analyst
3. Data/Analytics Manager
4. Data Administrator
5. Data Architect
6. Marketing Analyst
7. Business Analyst
8. Business Intelligence Manager

Toronto is home to many leading tech companies, including Shopify, Oracle, Cisco, etc. These companies need data scientists to make sense of data. When an employer hires Data Scientists, they look for the following:

• Education: Getting a degree is very beneficial because all Data Scientists are supposed to have PhDs. You can also try getting various certifications that will be added to your qualification. 
• Programming: Python is one of the most common programming languages used by Data Scientists. So, before you start to learn any Data Science libraries, you need to learn Python basics. 
• Machine Learning: Having Machine Learning skills is a must because once the data is prepared, deep learning will be used for the analysis of patterns and finding a relationship. 
• Projects: The more projects you can do, the stronger your portfolio will be. Practice with real-world projects.
  

• Multi paradigm programming language – Being a multi paradigm programming language means that there are different facets of Python that are suitable for working in Data Science. It is an object oriented programming language that is structured and contains packages and libraries that can be used for fulfilling the purpose of Data Science. 
• Simplicity and readability – Python is a simple and readable language that makes it the most preferred language by Data Scientists. There are a number of packages and analytical libraries that are tailor-made for Data Science projects.  
• Diverse range of resources – There is a broad range of resources available for Data Scientists that help them get out of a situation where they are stuck while creating  a Python program or developing a Data Science model. 
• The vast Python community - There are millions of developers working on the same programming language trying to deal with the same problem. The vast community makes it easy for the developer to resolve their problems. Even if no solution is available for your problem, the Python community will try their best to help their fellow programmer. 

Data Science consists of multiple libraries and if you want these libraries to work together smoothly, you must select an appropriate programming language. Here are the 5 most popular programming languages used in the field of Data Science:

• R: Despite the steep learning curve, R language has the following advantages:
• R comes along with high-quality open source packages created by the open source community. 
• It is capable of handling matrix operations and statistical functions. 
• With the help of ggplot2, R acts as a great data visualization tool.
• Python: It is one of the most popular and sought after languages used in the field of Data Science. Even though it has fewer packages than R, it offers the following advantages that make up for it:
• Most of the libraries needed in Data Science are provided by Pandas, scikit-learn, and tensorflow 
• It is very easy to learn, understand, and implement. 
• It also comes with a big open-source community.
• SQL: SQL stands for structured query language. It works on relational databases.
• The syntax of SQL is easy to learn and understand.
• Updating, querying, and manipulating data is very efficient using SQL.
• Java: Despite the verbosity limit of Java and the less number of libraries that can be used for Data Science, the language offers the following advantages:
• Compatibility. It is very easy to integrate java in data science projects as there are systems pre-coded in Java. 
• Overall, Java is a general purpose, compiled, high-performance language. 
• Scala: It is one of the most preferred languages in Data Science despite of its complex syntax and running on JVM. It is because of the following reasons:
• Scala program can run on Java too as it runs on JVM as well. 
• High-performance cluster computing can be achieved by using Scala with Apache Spark. 

This is what you need to do to download and install Python 3 on Windows:

• Download and setup: Follow the link to download page and use the GUI installer to set up python on your windows. When you are installing, you will be asked if you want to add Python 3 .x to PATH that is your classpath. Select this checkbox to allow the usage of python’s functionalities from the terminal. 

You can try using Anaconda to install python as well. First, make sure if Python is already installed in the system or not. You can do this by running the following command:

Python –version

• Update and install setuptools and pip: For installing and updating setup tools and pp, you need to use the following command:

python -m pip install -U pip

Note: If you wish to create isolated python environments and pipenv, you can install a python dependency manager, virtualenv. 

Python 3 can be installed using a .dmg package from their official website. However, we recommend that you use Homebrew for the installation of Python and its dependencies. Here is what you need to do to install Python 3 on Mac OS X:

• Install xcode: Use Apple's Xcode package to install brew. You can start with the following command and then follow through it: $ xcode-select –install.
• Install brew: Use the following command to install the package manager for Apple, Homebrew: 

/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" 

Type “brew doctor” to confirm the installation.

• Install python 3: Next, for installing the latest version of Python, type “brew install python”. For confirming the version, type “python –version”. 

If you want to run different projects in isolated spaces, you can install virtualenv that can run on different versions of python.