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).
Have you ever thought how Amazon recommends you something even without asking you anything about it? It is because of the data (based on your online activities) collected by companies like Google and Facebook that they sell to the ad companies to earn major profits. According to the Harvard Business Review 2012, Data Scientist is the sexiest job of the 21st century. Moreover, Canada has one of the most powerful economies in the world, and Canadians possess a high standard of living, as well as a globally distinguished university system. Here are some other reasons why Data Scientist is such a popular job and why there is a huge demand for Data Scientists in 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:
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.
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 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 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.
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.
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.
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:
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:
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:
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:
Here is what you need to do to brush up your Data Science skills and get a job as a Data Scientist:
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:
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:
Becoming a successful data scientist involved following the below-mentioned steps:
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 -
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:
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:
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:
Here is what you need to learn to get a job as a Data Scientist:
Follow the below 5 steps to prepare for the job of a Data Scientist:
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:
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:
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:
The top professional associations and groups for data scientists in Canada include the following –
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:
The top 8 Data Science career opportunities in 2019 are:
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:
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:
This is what you need to do to download and install Python 3 on Windows:
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 -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:
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
Type “brew doctor” to confirm the installation.
If you want to run different projects in isolated spaces, you can install virtualenv that can run on different versions of python.
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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