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.
Get acquainted with various analysis and visualization tools such as Matplotlib and Seaborn
Understand the behavior of data;build significant models using concepts of Statistics Fundamentals
Learn the various Python libraries to manipulate data, like Numpy, Pandas, Scikit-Learn, Statsmodel
Use Python libraries and work on data manipulation, data preparation and data explorations
Use of Python graphics libraries like Matplotlib, Seaborn etc.
ANOVA, Linear Regression using OLS, Logistic Regression using MLE, KNN, Decision Trees
There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.
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Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.
Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.
Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.
Get reviews and feedback on your final projects from professional developers.
Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in data science.
Hands-on: No hands-on
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.
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.
Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario
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.
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.
Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
Work on a real- life Case Study with ARIMA.
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.
Project to be selected by candidates.
With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
This project involves building a classification model.
Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
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).
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-
Being a successful data scientist involves incorporating the following behavioral traits:
Data science as a career can be quite fruitful. Here are the 5 proven benefits of being a Data Scientist:
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:
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:
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:
Below are the right steps that you must follow in order to become a top-notch Data Scientist:
Here are some effective ways to help you kickstart your career as a data scientist:
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:
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:
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:
If you want to get a job in the field of Data Science, you need to follow this path:
The 5 important steps to prepare for the job as a Data Scientist involves:
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:
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.
The below mentioned organizations are the top professional organizations for Data Scientists in Ottawa, Canada:
You can create your network with other Data Scientists through the following:
There are several career options for a data scientist in Ottawa, Canada. These include –
There are some core skills that every company wants. Let’s find out what these skills are:
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;
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:
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:
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:
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:
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.
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:
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" Confirm if it is installed by typing: brew doctor
brew install python
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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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.
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
There are no restrictions but participants would benefit if they have basic programming knowledge and familiarity with statistics.
Yes, KnowledgeHut offers virtual training.
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.
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.
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