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).
The job of a Data Scientist has been dubbed as the ‘Sexiest Job of the 21st Century’ by Harvard Business Review in 2012. Brisbane is known to be home to some of the leading companies such as Michael page, Davidson, Paxus, Hays IT solutions, CSIRO, Accenture, Everledger, etc. These companies are always in search of data science professionals. This is what makes data science such a popular choice in Brisbane.
Some major reasons why data science is important are:
Due to these reasons, there is a high demand of Data Scientists in the tech world lately.
Brisbane enjoys a high standard of life due to the quality of education and service it offers. The University of Queensland, Christian Heritage College, QUT Gardens Point are some of the eminent universities offering data science courses. The top skills that are needed to become a data scientist include the following:
Below are the top 4 behavioral traits of a successful Data Scientist -
These are the 5 proven benefits of being a Data Scientist –
High Pay – As the qualification bar has been set quite high, the pay is also at par with it. As the demand of Data Scientists is way more than the supply, this job pays handsomely.
Good Bonuses – Data Scientists can surely expect great year end bonuses as well as equity shares and signing perks.
Education – The field of Data Science demands knowledge which means that by the time a person gets senior positions, he/she would have a high educational qualification. This will help them get great job opportunities in government as well as private organizations.
Mobility – Most firms and organizations who hire data scientists are located in developed countries which offer great salaries and have good living standards.
Network – As this field has constant development, it is a great place to build contacts and develop your network. Numerous conferences, workshops and meet-ups are organized on topics related to the field.
These are the business skills one must have to become a Data Scientist -
Below are the best ways to brush up your data science skills for data scientist jobs:
Brisbane, Australia is home to some of the prominent companies which constantly deal with data and therefore are always in need of data science professionals. Michael page, Davidson, Paxus, Hays IT solutions, CSIRO, Accenture, Everledger, etc are some of the companies. Data is collected by companies for their own benefit. This data is then used by companies to target specific audiences and improve customer experience. Data Scientists are employed by different types of companies -
Big companies have a specialized team of Data Scientists who handle the large amount of data.
It is easy to master the art of Data Science by extensively practicing and working your way through data related problems. Data Sets are a great way to do so. These sets are divided according to the knowledge level of the person attempting the set -
Iris Data Set: One of the most popular, versatile, easy and resourceful data set, the Iris Data Set works by identifying and recognizing patterns. The set has 50 rows and 4 columns. Practice Problem: Predict the class of a flower on the basis of these parameters.Loan Prediction Data Set: The banking sector uses data analytics and data science methodologies. The Loan Prediction data set works along with the concepts related to the industry. The data set has 615 rows and 13 columns. It is a classification problem data set. Practice Problem: Predict if a given loan will be approved by the bank or not.
Bigmart Sales Data Set: The retail sector is another industry which uses data analytics. Data Science makes management easier and efficient. The Bigmart Sales Data Set has 8523 rows and 12 variables. Practice Problem: Predict the sales of a retail store.
Black Friday Data Set: This data set includes sales transactions that have been captured from a retail store. It helps in understanding the shopping experiences of millions of customers. The set has 550,069 rows and 12 columns. Practice Problem: Predict the amount of the total purchase made.
Human Activity Recognition Data Set: This set works by using a collection of data of 30 human subjects. This data has been recorded using smartphones. Human Activity Recognition Data Set has 10,299 rows and 561 columns. Practice Problem: Predict the human activity category.
Text Mining Data Set: This data set has safety reports which mentions that problems faced on flights. The set contains 21,519 columns and 30,438 rows. Practice Problem: Classify the documents on the basis of their labels.
Urban Sound Classification: The Urban Sound Classification helps in finding solutions to concepts of Machine Learning. The data set has 8732 sound clippings which are categorized in 10 classes. It helps in processing various audio files. Practice Problem: Classify the type of sound that is obtained from a particular audio.
Identify the digits data set: With 7000 images, with dimensions 28x28, this data set helps in studying, analyzing and recognizing the numerous elements in an image.
Practice Problem: Identify the digits present in a given image
Vox Celebrity Data Set: This is another data set that deals in audio processing. This set is meant for speaker identification on a large scale. It contains100000 spoken words.
Practice Problem: Identify the celebrity that a given voice belongs to.
The very first step is to ensure that you get a proper education. Being in Brisbane, Australia is an added advantage as the city has some prominent institutions such as The University of Queensland, Christian Heritage College, QUT Gardens Point etc. which are well recognized for data science.
Mentioned below are the steps required to become a successful data scientist:
You must make sure that you are properly trained in the data science courses and master the concepts. This is where being in Brisbane is an advantage as the city has some prominent institutions such as The University of Queensland, Christian Heritage College, QUT Gardens Point etc. which are well recognized for data science.
Statistically, almost 88% of data scientists hold a Master’s degree while 46% of all data scientists are PhD degree holders. Brisbane has some prominent institutions such as The University of Queensland, Christian Heritage College, QUT Gardens Point etc. which are well recognized for data science.
A degree in this field is quite important for the following reasons -
Networking – A degree will help you in building friends and contacts in the industry. Having a good network is a great asset in every field.
Structured learning – A proper and structured scheduled while learning something new helps in better learning. It is better than doing things which are unstructured and unplanned.Internships – Internships are a great way to learn the practical side of any field.
Recognized academic qualifications for your résumé – A degree from a good and reputed institute will look great on your resume and will ensure great job opportunities.
Follow this to know whether you need a master’s degree. If your total is more than 6 by the end, you will need a masters degree for better job prospects.
One of the most important and basic skills that an aspiring data scientist must have is perhaps the knowledge of programming. There are some other reasons also which explain why knowledge in programming is essential:
A Data Scientist based in Brisbane can expect an average annual income of AU$103,716.
The average annual income of data scientist in Sydney is AU$121,209, which is AU$17,493 more than the average income in Brisbane.
In Adelaide, the average annual income of data scientist is AU$64,585, which is lower than the salary of a data scientist working in Brisbane, which is AU$103,716.
A data scientist working in Brisbane earns AU$103,716 every year as opposed to the average annual income of a data scientist working in Melbourne, which is AU$91,140.
Apart from Brisbane, cities like Pullenvale of Queensland have an average salary of AU$90,821 per year for data scientists.
The demand for Data Scientists in Brisbane is quite high. The entry of several firms in the field of data science has provided data scientists with an opportunity of tremendous job growth.
A data scientist in Brisbane enjoys a lot of benefits. The number of opportunities for a data scientist in Brisbane is quite high. This also allows them to make tremendous growth in their career fast. They also get to gain the attention of top executives.
Being a data scientist offers the following perks and advantages over other jobs:
In Brisbane, companies hiring Data Scientists in Brisbane include Suncorp Group, Cook Medical and Fugro.
|1.||QLD | Transport & Infrastructure Series: The Future of Mobility - A Data-Led Revolution||30 April, 2019|
Deloitte Riverside Centre Level 23, 123 Eagle Street Brisbane City, QLD 4000 Australia
|2.||Azure Superpowers Tour - Brisbane||22 October, 2019|
SSW Brisbane Level 1 471 Adelaide Street Brisbane, QLD 4000 Australia
|3.||YOW! Developer Conference 2019 - Brisbane||9 Dec, 2019 to 10 Dec, 2019|
Brisbane Convention and Exhibition Centre Merivale Street South Brisbane, QLD 4101 Australia
|4.||Oracle Performance Diagnostics and Tuning Seminar with Richard Foote||29 July, 2019 to 30 July, 2019|
Brisbane Training Choice Ground Floor, 50 Queen Street Brisbane, QLD 3000 Australia
|5.||Easy Spatial Technologies for Yrs 7-10 Geography in QLD||14 June, 2019|
Maida Lilley Community Centre 5 Green Square Close Fortitude Valley, QLD 4006 Australia
|6.||USQ Software Carpentry with Python 2019 Workshop||2 Oct, 2019 to 3 Oct, 2019|
B207 A+B University of Southern Queensland Building B, 37 Sinnathamby Boulevard Springfield Central, QLD 4300 Australia
|7.||Visualisation Day|Brisbane||September 19, 2019|
Pacific Hotel Brisbane 345 Wickham Terrace, Spring Hill QLD 4000, Australia Australia
|8.||Data synchronisation and implementation overview - Brisbane||9 July, 2019||TBA (Brisbane CBD) Australia|
|9.||SIDRA ADVANCED Two-Day Workshop // Brisbane [TE044]||18 Sept, 2019 to 19 Sept, 2019||Saxons Training Facilities Level 11, 300 Adelaide Street Brisbane, QLD 4000 Australia|
|10.||Technology in Retail - Data Analytics | Digital Business Workshop |||5 June, 2019||NRA Offices Level 3, 33 Park Road Milton, Queensland 4064 Australia|
1. QLD | Transport & Infrastructure Series: The Future of Mobility - A Data-Led Revolution, Brisbane
2. Azure Superpowers Tour - Brisbane
3. YOW! Developer Conference 2019 - Brisbane
4. Oracle Performance Diagnostics and Tuning Seminar with Richard Foote, Brisbane
5. Easy Spatial Technologies for Yrs 7-10 Geography in QLD, Brisbane
6. USQ Software Carpentry with Python 2019 Workshop, Brisbane
7. Visualisation Day, Brisbane
8. Data synchronisation and implementation overview, Brisbane
9. SIDRA ADVANCED Two-Day Workshop, Brisbane
10. Technology in Retail - Data Analytics | Digital Business Workshop, Brisbane
|1.||International Conference on Machine Learning in Computational Biology||3-4th April, 2017|
Mercure Hotels Brisbane 85–87 North Quay, Brisbane QLD 4003 Australia
|2.||Conference City: Brisbane, Australia|
|3.||Yow! Conference Brisbane||9-10 December, 2018||Brisbane Convention and Exhibition Centre, Merivale Street, South Brisbane, QLD 4101|
|4.||ICRA 2018||21-25 May, 2018||Brisbane Convention and Exhibition Center|
|5.||SIMPAR 2018||16-19 May, 2018|
Hilton Brisbane, 190 Elizabeth Street, Brisbane, Queensland, 4000, Australia
1. International Conference on Machine Learning in Computational Biology, Brisbane
2. Yow! Conference, Brisbane
3. ICRA 2018, Brisbane
4. SIMPAR 2018, Brisbane
Steps to follow to get a job as a Data Scientist.
For a data science job, follow the below steps to prepare:
A data scientist is responsible for discovering patterns and inferencing information from vast amounts of structured as well as unstructured data, and present them in a friendly and effective way, in order to meet the business goals and needs.
As modern businesses generate tons of data every day, the role and responsibilities of a Data Scientist are becoming extremely important. The reason behind this is that the data gathered is an essential part of the advancement of a business.
Data Scientist Roles & Responsibilities:
They are also expected to perform statistical analysis of relevant data and predict future outcome for the business.
The average salary for a Data Scientist is $103,716 per year in Brisbane.
A Data Scientist should have the ability of a mathematician as well as a scientist. He/she should be comfortable in handling and deciphering large amounts of data, mine relevant parts of the data and then perform an in-depth analysis to make predictions. A career path in the field of Data Science can be explained in the following ways:
Business Intelligence Analyst: The job of a Business Intelligence Analyst includes figuring out business as well as market trends. Their main role and responsibility includes analyzing data to get a clear picture of where the business stands.
Data Mining Engineer: A data mining engineer is responsible for examining data for the business as well as for the third-party clients. Their job description also includes creating algorithms for better data analysis. Data Architect: A data architect has to work with system designers, developers and users. They create blueprints which are used to integrate, protect and maintain the large amount of data.
Data Scientist: A data scientist is responsible for pursuing case by case analysis. His/her job also includes understanding and exploring data patterns. They also develop algorithms for data analysis. Senior Data Scientist: The role of a senior data scientist is to anticipate future business needs. They are responsible for shaping future projects, performing data analysis and reporting business needs for the future.
Below are the top professional organizations for data scientists –
There are several career options for a data scientist –
Companie like Michael page, Davidson, Paxus, Hays IT solutions, CSIRO, Accenture, Everledger, operate from Brisbane, Australia and offer lucrative jobs but also demand mastery in the field of data science. This is a list of the key points which the employers generally look for while hiring data scientists:
Python is a multi-paradigm programming language. It is most suited for Data Science as it is structured and object oriented. It has several libraries and packages which are extremely useful while working with large amount of data. It is simple and readable which makes it a preferred language. The libraries and packages are specifically made for Data Science. This is the reason why Python is preferred over any other language. It offers a diverse range of resources which makes it the first choice of Data Scientists. These resources are readily available for use and are easy to understand and operate. If a Data Scientist gets stuck at a particular point or problem while developing a Python program or model for Data Science, it extremely easy to find a solution.
As data science is a huge field and involves multiple libraries to work together in a smooth way, it is essential that you choose an appropriate programming language.
Follow these steps to successfully install Python 3 on windows:
An alternate way to install Python is via Anaconda. To check whether Python is installed, run the following command –
Update and install setuptools and pip: Use below command to install and update 2 of most crucial libraries (3rd party):
python -m pip install -U pip
Note: Virtualenv, a python dependency manager can also be installed to created isolated python environment.
A .dmg package is available on the website through which Python can be installed directly. Follow these steps:
$ xcode-select --install
/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
Virtualenv can be installed to run isolated python versions
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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