Data Science with Python Certification Training in Ottawa, Canada

Get the ability to analyze data with Python using basic to advanced concepts

  • 40 hours of Instructor led Training
  • Interactive Statistical Learning with advanced Excel
  • Comprehensive Hands-on with Python
  • Covers Advanced Statistics and Predictive Modeling
  • Learn Supervised and Unsupervised Machine Learning Algorithms

Description

Rapid technological advances in Data Science have been reshaping global businesses and putting performances on overdrive. As yet, companies are able to capture only a fraction of the potential locked in data, and data scientists who are able to reimagine business models by working with Python are in great demand.

Python is one of the most popular programming languages for high level data processing, due to its simple syntax, easy readability, and easy comprehension. Python’s learning curve is low, and due to its many data structures, classes, nested functions and iterators, besides the extensive libraries, this language is the first choice of data scientists for analysing, extracting information and making informed business decisions through big data.

This Data science for Python programming course is an umbrella course covering major Data Science concepts like exploratory data analysis, statistics fundamentals, hypothesis testing, regression classification modeling techniques and machine learning algorithms.Extensive hands-on labs and an interview prep will help you land lucrative jobs.

What You Will Learn

Prerequisites

There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.

3 Months FREE Access to all our E-learning courses when you buy any course with us

Who should Attend?

  • Those Interested in the field of data science
  • Those looking for a more robust, structured Python learning program
  • Those wanting to use Python for effective analysis of large datasets
  • Software or Data Engineers interested in quantitative analysis with Python
  • Data Analysts, Economists or Researchers

KnowledgeHut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.

Curriculum

Learning Objectives:

Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in  data science.

Topics Covered:

  • What is Data Science?
  • Analytics Landscape
  • Life Cycle of a Data Science Project
  • Data Science Tools & Technologies

Hands-on:  No hands-on

Learning Objectives:

In this module you will learn how to install Python distribution - Anaconda,  basic data types, strings & regular expressions, data structures and loops and control statements that are used in Python. You will write user-defined functions in Python and learn about Lambda function and the object oriented way of writing classes & objects. Also learn how to import datasets into Python, how to write output into files from Python, manipulate & analyze data using Pandas library and generate insights from your data. You will learn to use various magnificent libraries in Python like Matplotlib, Seaborn & ggplot for data visualization and also have a hands-on session on a real-life case study.

Topics Covered:

  • Python Basics
  • Data Structures in Python
  • Control & Loop Statements in Python
  • Functions & Classes in Python
  • Working with Data
  • Analyze Data using Pandas
  • Visualize Data 
  • Case Study

Hands-on:

  • Know how to install Python distribution like Anaconda and other libraries.
  • Write python code for defining your own functions,and also learn to write object oriented way of writing classes and objects. 
  • Write python code to import dataset into python notebook.
  • Write Python code to implement Data Manipulation, Preparation & Exploratory Data Analysis in a dataset.

Learning Objectives: 

Visit basics like mean (expected value), median and mode. Understand distribution of data in terms of variance, standard deviation and interquartile range and the basic summaries about data and measures. Learn about simple graphics analysis, the basics of probability with daily life examples along with marginal probability and its importance with respective to data science. Also learn Baye's theorem and conditional probability and the alternate and null hypothesis, Type1 error, Type2 error, power of the test, p-value.

Topics Covered:

  • Measures of Central Tendency
  • Measures of Dispersion
  • Descriptive Statistics
  • Probability Basics
  • Marginal Probability
  • Bayes Theorem
  • Probability Distributions
  • Hypothesis Testing 

Hands-on:

Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario

Learning Objectives: 

In this module you will learn analysis of Variance and its practical use, Linear Regression with Ordinary Least Square Estimate to predict a continuous variable along with model building, evaluating model parameters, and measuring performance metrics on Test and Validation set. Further it covers enhancing model performance by means of various steps like feature engineering & regularization.

You will be introduced to a real Life Case Study with Linear Regression. You will learn the Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. It also covers techniques to find the optimum number of components/factors using screen plot, one-eigenvalue criterion and a real-Life case study with PCA & FA.

Topics Covered:

  • ANOVA
  • Linear Regression (OLS)
  • Case Study: Linear Regression
  • Principal Component Analysis
  • Factor Analysis
  • Case Study: PCA/FA

Hands-on: 

  • With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling.

Learning Objectives: 

Learn Binomial Logistic Regression for Binomial Classification Problems. Covers evaluation of model parameters, model performance using various metrics like sensitivity, specificity, precision, recall, ROC Cuve, AUC, KS-Statistics, Kappa Value. Understand Binomial Logistic Regression with a real life case Study.

Learn about KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K. Understand KNN through a real life case study. Understand Decision Trees - for both regression & classification problem. Understand Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID. Use a real Life Case Study to understand Decision Tree.

Topics Covered:

  • Logistic Regression
  • Case Study: Logistic Regression
  • K-Nearest Neighbor Algorithm
  • Case Study: K-Nearest Neighbor Algorithm
  • Decision Tree
  • Case Study: Decision Tree

Hands-on: 

  • With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month. This can help the bank be proactive in collecting dues.
  • Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
  • Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Learning Objectives:

Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
Work on a real- life Case Study with ARIMA.

Topics Covered:

  • Understand Time Series Data
  • Visualizing Time Series Components
  • Exponential Smoothing
  • Holt's Model
  • Holt-Winter's Model
  • ARIMA
  • Case Study: Time Series Modeling on Stock Price

Hands-on:  

  • Write python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Write python code to Use Holt's model when your data has Constant Data, Trend Data and Seasonal Data. How to select the right smoothing constants.
  • Write Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Dataset including features such as symbol, date, close, adj_close, volume of a stock. This data will exhibit characteristics of a time series data. We will use ARIMA to predict the stock prices.

Learning Objectives:

A mentor guided, real-life group project. You will go about it the same way you would execute a data science project in any business problem.

Topics Covered:

  • Industry relevant capstone project under experienced industry-expert mentor

Hands-on:

 Project to be selected by candidates.

Meet your instructors

Become an Instructor
Biswanath

Biswanath Banerjee

Trainer

Provide Corporate training on Big Data and Data Science with Python, Machine Learning and Artificial Intelligence (AI) for International and India based Corporates.
Consultant for Spark projects and Machine Learning projects for several clients

View Profile

Projects

Predict House Price using Linear Regression

With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.

Predict credit card defaulter using Logistic Regression

This project involves building a classification model.

Read More

Predict chronic kidney disease using KNN

Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.

Predict quality of Wine using Decision Tree

Wine comes in various styles. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Note:These were the projects undertaken by students from previous batches. 

Data Science with Python

What is Data Science

Been termed as ‘the sexiest job of the 21st century’ by Harvard, it hardly comes as a shock that Data Science has gained huge demands across the globe. In the present times, where data practically controls nearly every sector, and where technological advancements have revolutionized the way business is conducted, Data Science has grown to become the favourite amongst the IT graduates in Ottawa as well. With the growth in demand of Data Science, Ottawa is now home to various leading tech companies like Hydro Ottawa, TITUS, Shopify, Novella Clinical, etc.

Data science is not an easy field, it requires an in-depth understanding of coding, theoretical knowledge and hands-on practical experience. Below are some top technical skills you need to become a data scientist in Ottawa, Canada-

  1. Python Coding: Python is the simplest and most well-known platform among programmers. Known for its intuitive interface, versatile tools, and advanced features, Python is suitable for beginners and professionals alike.
  2. R Programming: R Programming has a steeper learning curve when compared to Python. But the platform is great for arranging and analyzing data sets, integrating algorithms and getting insights.
  3. Hadoop Platform: It is not mandatory to learn Hadoop. The platform is an additional perk that will boost your chances in the industry. It not only allows you to store all forms of data but also provides modules like Pig and Hive for analysis of large scale data.
  4. SQL database and coding: Structured Query Language or SQL is a database language which is used to manage data held in relational database management systems.
  5. Machine Learning and Artificial Intelligence: Data science, ML and AI go hand in hand. Here are some topics that you must be familiar with as a data science professional:
    • Neural Networks
    • Decision trees
    • Reinforcement learning
    • Logistic regression
    • Adversarial learning
    • Machine learning algorithms, etc.
  6. Apache Spark: Apache Spark is one of the most popular data science platforms for scientists and coders. It creates cache files for its work and is hence a lot faster than other contemporary platforms. It integrates large datasets effectively, helps disseminate data processing and is overall a more convenient option for beginners.
  7. Data Visualization: Data visualization tools allow you to arrange and sort through the unstructured and unfiltered data via platforms like matplotlib and ggplot. It converts complex data sets into a more readable and comprehensible format which makes it easier for data scientists to analyze the given data and get valuable insights from it.
  8. Unstructured data: Data is usually available in a compound, unstructured form. It is neither categorized nor organized into database values. Your job will be to arrange this data, label it accordingly and make it more understandable.

Being a successful data scientist involves incorporating the following behavioral traits:

  • Ingenuity: A data scientist must be inventive, innovative and creative enough to come up with new and unique solutions to problems. There may be times when you will be facing complicated situations, something that you weren’t prepared for. This requires some out of the box thinking.
  • Passion: Data science is not an easy field, it requires hours of practice and technical studies. And only a candidate who is passionate enough about his/her work will be able to survive such stressful conditions.
  • Patience: Data scientists have to be very patient and persevering. This is because your job involves a lot of trial and error, one has to experiment and explore the various algorithms and apply them to the situation.
  • Creativity: There will be times when one has to develop new queries and algorithms for a particular complicated situation. This requires creativity to come up with new plans and alternatives.
  • Curiosity: Data science deals with an enormous amount of data every day. A data scientist must be inquisitive and have a never-ending hunger for information. Otherwise, it can get too hard too soon.

Data science as a career can be quite fruitful. Here are the 5 proven benefits of being a Data Scientist:

  1. Data scientists are among the highest-paid professionals in the industry today. The demand for Data Science jobs is at an all-time high, especially when compared to other career options.
  2. Data science professionals also enjoy a wide scope for exploring different career opportunities. Companies often reward good work with perks like signing bonus, equity shares, and impressive year-end bonuses.
  3. Data science also requires an in-depth knowledge of coding, programming and academic credentials like a Master’s or a Ph.D. Having a degree opens up alternative career options for people in the academic field as well. You can work as a researcher or a lecturer in a government or a private institution.
  4. As a data scientist, you will have the opportunity to travel the world. There is a special demand for data science professionals in developed countries around the world.

Data Scientist Skills and Qualifications

Data scientists are not merely coders or IT professionals. Your job includes the multiple roles of a business analyst, a software engineer, a marketer, a coder, and a good manager. Business skills are hence extremely necessary if you want to be a successful data scientist. It is important to have the following business skills if you want to become a successful data scientist:

  • Analytical Skills: First and foremost, you should be able to analyze a problem and arrive at the crux of the situation quickly and effectively. This requires great analytical abilities and observational skills.
  • Communication Skills: In data science, communication is critical.  You should be able to communicate deep business and customer analytics to the organization.
  • Thirst for Knowledge: Data science professionals should have an open mind and a curiosity to learn about the latest trends and new concepts of the industry.

Data scientists are in demand and the right candidates are rewarded with a future-proofed and lucrative career. Career in data science requires one to be highly knowledgeable, focused and passionate about data and have advanced analytical skills. One has to always be attentive and updated with the latest trends in the industry, keep up with the undercurrents of the market and constantly be ready to absorb and analyze information. Below are the best ways to brush up your data science skills for data scientist jobs:

  • Boot camps: Boot camps are arranged for data science specialists who need to brush up their programming skills. These boot camps also help students garner interest from recruiters through partnerships with businesses.
  • MOOC courses: MOOCs are online courses where one can access lectures and sign up for classes held by data science experts and industry professionals. These classes are live-streamed online and include practical exercises and modules. It is great for tech enthusiasts and beginners.
  • Certifications: Look for certificate courses online that can add substantial value to your CV. These courses are sponsored by trustworthy institutions and hence are a good investment.
  • Projects: There are online projects and assignments that will be a great addition to your CV. Most data science courses offer project coursework for a practical experience of the industry.

With today’s makerket being fueled by AI and Big Data, the demand for Data Scientists has grown and continues to grow exponentially. However, the demand is not met as the number of skilled applicants are not available at the same pace. As the demand grows, Ottawa has become home to diverse leading companies and startups which employ Data Scientists. To name a few of these companies are Mozilla, Hydro Ottawa, TITUS, Shopify, Privacy Analytics, Lixar IT, Kelly Services, Novella Clinical, Wind River.

Data science requires constant practice and mix of theory and technical expertise. Here, we have categorized different problems according to their difficulty level and your expertise level:

  • Beginner Level
    • Iris Data Set: It is applied in the field of pattern recognition and lets you integrate diverse learning methods. If you are a beginner in the field of data science, this dataset is the best for you. This dataset has 4 rows and 50 columns.
    • Practice Problem: The problem is using these parameters to predict the class of the flowers.
    • Loan Prediction Data Set: This data set is applied to the banking sector and involves a thorough knowledge of the market and its various trends. It is a classification problem dataset with 13 columns and 615 rows.
    • Practice Problem: The problem is to predict if the loan will be approved or not.
    • Bigmart Sales Data Set: This data set is created for retail, allowing developers a compact and comprehensible way to get market insights, design sales strategies, and advertising campaigns. This dataset is a regression problem with 12 columns and 8523 rows.
    • Practice Problem: The problem is predicting the sales of the retail store.
  • Intermediate Level:
    • Black Friday Data Set: Collected from a retail store, this dataset, gives the developer an understanding of the everyday shopping experience of customers. It is a regression problem with 12 columns and 550,069 rows.
    • Practice Problem: The problem is predicting the total amount of purchase.
    • Human Activity Recognition Data Set: This dataset applies initial sensors to collect the call log of its customers. It is ideal for the communication and broadcasting industry. It is a collection of 30 human subjects. The dataset consists of 561 columns and 10,299 rows.
    • Practice Problem: The problem is the prediction of the category of human activity.
    • Text Mining Data Set: The data set is applied to the aviation industry. It is a multi-classification, high dimension problem with 30,438 rows and 21,519 columns.
    • Practice Problem: The problem is the classification of documents based on their labels.
  • Advanced Level:
    • Urban Sound Classification: This data set applies machine learning notions to everyday problems. Consisting of 10 classes with 8,732, this problem introduces the developer to the audio processing in the real-world scenarios of classification.
    • Practice Problem: The problem is the classification of the sound obtained from specific audio.
    • Identify the digits data set: This is ideal for the image processing and photography sector. The data set comes with 7000 images of 31 MB and 28X28 dimensions and allows developers to analyze and identify the different aspects of the image.
    • Practice Problem: The problem is identifying the digits present in an image.
    • Vox Celebrity Data Set: This dataset is used for large scale speaker identification. It uses YouTube videos to extract the words spoken by celebrities. It contains 100,000 words spoken by 1,251 celebrities.
    • Practice Problem: The problem is the identification of the voice of a celebrity.

How to Become a Data Scientist in Ottawa, Canada

Below are the right steps that you must follow in order to become a top-notch Data Scientist:

  1. Choose a programming language that you’re comfortable using. R and Python are the most preferred languages
  2. Now that the programming platform is decided, the next step is to learn about the basics of stats and algebra. In data science, you have to deal with data that can be textual, numerical or image.
  3. Data visualization makes the content clear and comprehensible for the non-technical members of the team. It aids in communication and simplifies the concepts for end-users.
  4. Every data scientist must have at least a basic understanding of Machine Learning skills for quick and in-depth analysis.

Here are some effective ways to help you kickstart your career as a data scientist:

  1. Get a degree: Data scientists have more PhDs than any of the other job titles. Apply for a degree or certificate course to get the necessary academic credentials required for the job.
  2. Hands-on experience: Get some hands-on experience in live projects. Participate in contests, complete projects, and get a data science internship etc.
  3. Unstructured data: Data scientists have to generally work with unstructured data which has to be labeled, sorted and analyzed. A data scientist is responsible to understand this unstructured data and manipulate it to get optimum results
  4. Programming languages: R and Python are widely used by data miners and data scientists. It is important to know how to code.You need to have the knowledge of programming languages like Python, Perl, C/C++, SQL and Java.
  5. ML and AI: A thorough understanding of ML and AI is also required for data scientists to analyze data and get accurate results from it
  6. Data visualization: With data visualization tools, one can make the data set simpler and accessible even for those who don’t know anything about data science. Data scientists convert this raw data into graphs and charts. There are several tools that can be used for visualization like ggplot2, matplotlib, etc.

Primarily, it is very crucial for candidates to  attain a degree in Data Science. About 88% of data scientists have a master’s degree while about 46% have a Ph.D. Ottawa, ON provides students with a wide range of educational institutes where you can apply for data science courses. Acquiring a degree in Data Science will help you in the following areas:

  • Networking: It allows beginners to expand their network, make more contacts and even get a chance to interact with experts. This networking will benefit you a lot in the long run as this industry works on referrals.
  • Structured learning: It offers candidates a structured and comprehensive training of the basic and advanced concepts of data science. This is more beneficial and effective than studying without any planning.
  • Internship: A degree also qualifies the candidate for an internship at a corporate firm. This internship can be both paid and unpaid.
  • Recognized qualifications: Last but not least, a degree in data science is a great boost to the CV.

A masters degree is the basic requirement for candidates who want to apply for a job in data science. If you are having trouble in deciding whether you should go for a Master’s degree, you can try grading yourself on the basis of the below scorecard. If your score is more than 6 points, you should get a Master’s degree:

  • A strong STEM (Science/Technology/Engineering/Management) background: 0 point
  • A weak STEM background (biochemistry/biology/economics or another similar degree/diploma): 2 points
  • A non-STEM background: 5 points
  • Less than 1 year of experience in Python: 3 points
  • No experience of a job that requires regular coding: 3 points
  • Independent learning is not your cup of tea: 4 points
  • Cannot understand that this scorecard is a regression algorithm: 1 point

It is vital that one master the basic programming languages like Python and R. It is the most fundamental skill for anyone in the IT field, irrespective of your location. Below are some reasons why a programming language is required to become a data scientist:

  • Data sets: Data sets form the fundamental aspects of the job. Knowledge of a programming language is a must to analyze this large dataset.
  • Statistics: Analyzing statistics will be an important part of your job. You need the ability to program to implement statistics.
  • Framework: With coding, you will be able to create a framework that will not only analyze data sets but will also manage the data visualization process.

Data Scientist Jobs in Ottawa, Canada

If you want to get a job in the field of Data Science, you need to follow this path:

  • Getting started: Enrol for a degree or certification in Data Science. Next, you need to learn at least one programming language, we would recommend R or Python.
  • Mathematics: You need to have a good knowledge of mathematics and statistics. Data science involves a lot of charts and tables and graphs as well. Algebra, permutation and combination, probability are some other topics to pay attention to.
  • Libraries: Get to know about the various processes in data science. Several libraries can be used like Pandas, Matplotlib, SciPy, Scikit-learn, NumPy, ggplot2, etc.
  • Data visualization: Learn to work with raw data and organize it. Find relevant patterns with data visualization tools. The libraries used for this task are ggplot2 and matplotlib.
  • Data preprocessing: The next step is to pre-process the data and engineer it in a more comprehensible format.
  • ML and Deep learning: You need to have Machine learning and deep learning skills in your CV to get a job as a data scientist.You need to have a tight grasp on topics like CNN, RNN, Neural networks, etc.
  • Natural Language processing: Natural language processing comprises processing and cataloging textual data. Every data scientist must be skilled in NLP.

The 5 important steps to prepare for the job as a Data Scientist involves:

  • Study: This involves understanding the technical concepts of data science. Cover all the basic and important topics like statistics, statistical models, probability, neural networks, machine learning, etc.
  • Meetups and conferences: You need to increase your corporate connections and build your own network. Conferences, tech talks, meet-ups, etc. allow you to do so.
  • Competitions: After learning the concepts, one needs to implement it as well. Keep practicing, applying, improving, and testing your skills through online competitions like Kaggle.
  • Referral: According to a survey, the primary source of interviews in companies is a referral. Keep your LinkedIn profile updated.
  • Interview: The interview is where you finally get to interact with the corporate firm. Be confident and composed in answering the questions asked. Most importantly don't lose hope after a bad interview. Instead, study the questions you weren't able to answer.

The primary goal of a data scientist is to explore raw data and look for patterns. Then once the pattern is set, he/she has to infer information from it. This data can be present in the form of structured as well as unstructured data.

Data Scientist Roles & Responsibilities:

  • The first and the most important role of a data scientist is to get the data that is pertinent to the organization. Often you will be given a pile of unstructured data to sort and label through.
  • Next, you have to organize and analyze the data into categories.
  • Once you have examined the data, you need to generate machine learning techniques to identify patterns in the data and make sense out of it.
  • Lastly, you need to statistically analyze the data to foresee future results.

The average salary for a Data Scientist in Ottawa, Canada is CA $102,000 per year.

A data scientist is expected to fill in several roles, such as a software engineer, a vendor, a trendsetter and a statistician. He has to toil with huge data sets, filter what’s applicable and then gather insights that can be used to predict customer trends as accurately as possible.

Data Analyst: Data Analyst collects information from various sources and interpret patterns and trends and turns it into information which can offer ways to improve a business.

Data Scientist: A data scientist is someone who interprets and manages data to help shape or meet business goals. As a data scientist, your job will involve tasks like assessing enormous volumes of data, figure out patterns, develop procedures based on the same.

Data Engineer: As a data engineer, your job involves assembling data sets, reviewing the business requirements, collaborating with third-parties, creating algorithms and collecting data sets. This information will help developers to estimate market trends as precisely as possible.

Data Architect: A data architect has to team up with data scientists and engineers to produce elaborate plans for the company. The data architect is responsible for executing the plan.

You can create your network with other Data Scientists through the following:

  • LinkedIn
  • Data Science conferences
  • Meetups

There are several career options for a data scientist in Ottawa, Canada. These include – 

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

There are some core skills that every company wants. Let’s find out what these skills are:

  • General Skills: General skills are the essential theoretical skills and academic qualifications essential to be a data scientist. Most data scientists have a Ph.D., a degree in Machine Learning and AI and a few research papers.
  • Technical Skills: Technical skills include a thorough knowledge of programming languages like Python, R Programming, SQL, Hadoop, Spark, JAVA, SAS, Hive, Jsp.net, C++, NSQL, AWL, Scala and more.
  • Practical Skills: You need to try your hands on real-world projects to improve your skills and build your portfolio.

Data Science with Python Ottawa, Canada

  1. Python is perhaps the easiest programming platform for a data scientist. It doesn’t matter if you are a newbie or a professional, it is ideally suited for everyone.
  2. Python is a diverse, extensive and flexible open-source programming language. It runs on the OOPS format and offers a selection of packages and libraries that can be beneficial in the field of Data Science.
  3. It comes with some of the best analytical and developing tools and data science resources. These tools help you out of the most complex situation and help you find an answer.
  4. Python has a very far-reaching and intricate community of developers, software engineers and technical experts who are always there to guide you through a tough spot.

Here are the 5 most popular programming languages used in the Data Science field:

R Programming: R is open-source software, which is used to compute huge data sets, get statistical insights, create customizable graphics, etc. The platform though a bit advanced for beginners is pretty efficient once you figure the core concepts. It includes;

  • Advanced data packages, statistical models, and easy to edit templates,
  • Connectivity to diverse networks, over 8000 for better visibility
  • Viva GGPLOT, Visual tools for smooth matrix handling

Python: Python is a handy data tool ideal for examining, positioning and assimilating data into intricate data sets and generating advanced algorithms. It is among the most desirable platforms by data scientists. It is because of the following advantages that it offers:

  • An open-source platform for better elasticity and customization options
  • Comes with special features like Scikit learn, sensor flow and Pandas for quick and effective data analysis

SQL: SQL or structured query language allows users to assemble data, manage the unstructured data, design relational databases and more. It allows retrieve old data sets, and gain quick and immediate insights. Other benefits include:

  • Versatile, flexible, time-efficient and easy to handle
  • Great for multitasking

Java: JAVA runs on the JVM or Java Virtual Machine Platform. It is the preferred platform for nearly every industry. Developers can develop backend systems and applications. Some advantages of using Java are:

  • Java works on OOPS and is compatible with all platforms
  • Users can edit and design codes for both frontend and backend applications
  • Plus, it is easy to compile data using Java

Scala: Scala is based on JVM and hence preferred by data scientists for running huge data sets. The coding interface, powerful tools, and a flexible static tape framework adds on to the platform reliability. Some other benefits are:

  • Scala supports Java and other OOPS platforms
  • Can be integrated with Apache Spark and other high-performance programming languages.

Follow these steps to download the latest version of Python 3 on Windows:

Download and setup: First and foremost, you have to visit the download page to set up Python on your windows using the GI Installer. Ensure that the pathway is selected in the checkbox, this allows one to decide where the Python 3.x is to be installed.


  • An alternative way is to opt for Anaconda to install Python. If you want to check if Python is installed, you can try using the following command that will show the current version of Python installed:

python --version

  • Update and install setup tools and pip: for installing and updating the crucial libraries, you can use the following command:

python -m pip install -U pip

Note: You can create isolated Python environments and pipenv using virtualenv. Pipenv is a Python dependency manager. 

There are two ways by which one can install Python 3 on Mac OS X. You can either install the programming language from the official website using a .dg package. The second method is to pick the Homebrew python version or its alternatives. Here are the steps you need to follow:

  • Install Xcode: First, you need to install Xcode. You will need the Xcode package of Apple/ Start using the following command: $ xcode-select --install
  • Install brew: Next, you have to install Homebrew which is a package manager for Apple. Start with the following command: 

/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" Confirm if it is installed by typing: brew doctor

  • Install python 3: Lastly, to install python, use the following command: 

brew install python

  • If you want to confirm the version of python, use the command: python --version

You should install virtualenv that will generate separate spaces for you to run diverse projects and can even run multiple versions of Python on different projects. 

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Attended PMP® Certification workshop in May 2018
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I would like to thank the KnowledgeHut team for the overall experience. I loved our trainer so much. Trainers at KnowledgeHut are well experienced and really helpful. They completed the syllabus on time, also helped me with live examples.

Elyssa Taber

IT Manager.
Attended Agile and Scrum workshop in May 2018
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The customer support was very interactive. The trainer took a practical session which is supporting me in my daily work. I learned many things in that session. Because of these training sessions, I would be able to sit for the exam with confidence.

Yancey Rosenkrantz

Senior Network System Administrator
Attended Agile and Scrum workshop in May 2018
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The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut. I really liked the way the trainer explained the concepts. He is very patient.

Anabel Bavaro

Senior Engineer
Attended Certified ScrumMaster®(CSM) workshop in May 2018
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I was totally surprised by the teaching methods followed by Knowledgehut. The trainer gave us tips and tricks throughout the training session. Training session changed my way of life.

Matteo Vanderlaan

System Architect
Attended Certified ScrumMaster®(CSM) workshop in May 2018
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I liked the way KnowledgeHut course got structured. My trainer took really interesting sessions which helped me to understand the concepts clearly. I would like to thank my trainer for his guidance.

Barton Fonseka

Information Security Analyst.
Attended PMP® Certification workshop in May 2018
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Trainer really was helpful and completed the syllabus covering each and every concept with examples on time. Knowledgehut also got good customer support to handle people like me.

Sherm Rimbach

Senior Network Architect
Attended Certified ScrumMaster®(CSM) workshop in May 2018
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Trainer at KnowledgeHut made sure to address all my doubts clearly. I was really impressed with the training and I was able to learn a lot of new things. It was a great platform to learn.

Meg Gomes casseres

Database Administrator.
Attended PMP® Certification workshop in May 2018

FAQs

The Course

Python is a rapidly growing high-level programming language which enables clear programs on small and large scales. Its advantage over other programming languages such as R is in its smooth learning curve, easy readability and easy to understand syntax. With the right training Python can be mastered quick enough and in this age where there is a need to extract relevant information from tons of Big Data, learning to use Python for data extraction is a great career choice.

 Our course will introduce you to all the fundamentals of Python and on course completion you will know how to use it competently for data research and analysis. Payscale.com puts the median salary for a data scientist with Python skills at close to $100,000; a figure that is sure to grow in leaps and bounds in the next few years as demand for Python experts continues to rise.

  • Get advanced knowledge of data science and how to use them in real life business
  • Understand the statistics and probability of Data science
  • Get an understanding of data collection, data mining and machine learning
  • Learn tools like Python

By the end of this course, you would have gained knowledge on the use of data science techniques and the Python language to build applications on data statistics. This will help you land jobs as a data analyst.

Tools and Technologies used for this course are

  • Python
  • MS Excel

There are no restrictions but participants would benefit if they have basic programming knowledge and familiarity with statistics.

On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.

Your instructors are Python and data science experts who have years of industry experience. 

Finance Related

Any registration canceled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of a written request for refund. Kindly go through our Refund Policy for more details.

KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

The Remote Experience

In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor

Have More Questions?

Data Science with Python Certification Course in Ottawa

The political and technological seat of the country, Ottawa contributes significantly to Canada?s finances. Its high standards of living along with a multi-ethnic population, attention to education and availability of jobs make it a great place to live. No wonder it has been consistently listed as among the best cities in the world. The city?s economy is driven by the government sector, healthcare, technology and telecommunications sector. While the Federal Government is the largest employer, companies such as Adobe Systems, Bell Canada, IBM, HP, and others also offer substantial employment. Its cultural diversity is well reflected in the local?s day to day life including the cuisine, art and music. These can be sampled in the wonderful eating establishments that range from street vendors to classy restaurants. Its wonderful conglomeration of museums, art houses and inspiring architecture make this city a tourist hot spot. Professionals who want to experience living here can pursue one of KnowledgeHut?s several courses such as PRINCE2, PMP, PMI-ACP, CSM, CEH, CSPO, Scrum & Agile, MS courses, Big Data Analysis, Apache Hadoop, SAFe Practitioner, Agile User Stories, CASQ, CMMI-DEV and others. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.