Online Classroom (Weekday)
Mar 30 - Apr 27 10:00 AM - 12:00 PM ( AEDT )
Online Classroom (Weekday)
Mar 30 - Apr 27 11:00 AM - 01:00 PM ( AEDT )
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 analyzing, 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 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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Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.
Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.
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).
Data Scientist was actually termed the ‘sexiest job in the 21st century’ in a 2012 survey conducted by the Harvard Business Review. User data is often collected by larger corporations so that they can sell it to advertising companies for profits. How else would companies know if you like dogs or cats? Doesn’t that explain how Amazon somehow always predicts what products you might be interested in or would like to buy based on previous purchases?
Melbourne enjoys being one of the most advanced cities in the world. They have a high standard of living. Melbourne is home to some of the most elite institutions offering data science and leading companies such as Amazon, Move 37, ANZ, Zendesk, EY, Envato, Capgemini, General Assembly, Aginic, Deloitte, etc. which hire data science professionals.
Other than this, there are many reasons why data science is becoming an increasingly popular profession in cities like Melbourne. Some of those are listed below:
It is highly beneficial for aspiring data science professionals to reside in Melbourne as it is home to some of the best institutes such as University of Melbourne, General Assembly Melbourne, La Trobe University, RMIT University, Melbourne City, United POP Melbourne, Genazzano FCJ College, etc.which offer data science courses. The following are the top 8 skills that you will need if you want to become a data scientist:
As a data scientist, you need these 5 traits to get hired in Melbourne-
As a data scientist, you’ll be working in a job that has been termed the ‘Sexiest job of the 21st century’ by Harvard Business review. Living in Melbourne will give you additional advantage as it is home to some of the eminent companies such as Amazon, Move 37, ANZ, Zendesk, EY, Envato, Capgemini, General Assembly, Aginic, Deloitte etc. Many benefits come with the job-
Below is the list of top business skills needed to become a data scientist:
One must also keep in mind that the above skills are essential irrespective of whether you are residing in Melbourne or New York.
You need to regularly brush up on your skills to become a successful Data Scientist. Here are five ways to do that:
Every shred of information ranging from medical data to browsing history is now considered data. In today’s world, data is extremely important. Many companies gather and deal with data to gain profits, and to provide better customer service. Melbourne is home to or has branches of several leading companies such as Amazon, Move 37, ANZ, Zendesk, EY, Envato, Capgemini, General Assembly, Aginic, Deloitte, etc. These companies are always in search of skilled data science professionals.
Different kinds of companies look for different types of data scientists:
Learning how to solve different types of problems is important to become a successful data scientist. Ranked in order of difficulty, these are suggestions for practicing your skills:
Below are the right steps to becoming a successful data scientist:
The first step is to get a proper education. Residing in Melbourne is beneficial as it is home to some of the known institutions such as the University of Melbourne, General Assembly Melbourne, La Trobe University, RMIT University, Melbourne City, United POP Melbourne, Genazzano FCJ College.
Here are some key skills you need to get started as a data scientist, “The Sexiest Job of the 21st Century”.
Almost 88% of data scientists have a Master’s degree while approximately 46% of all data scientists hold PhD degrees. University of Melbourne, General Assembly Melbourne, La Trobe University, RMIT University, Melbourne City, United POP Melbourne, Genazzano FCJ College, etc.are some of the most prominent universities which offer advanced courses in data science.
A degree is very important because of the following –
There is a very easy way to find out if you should get a Master’s degree. Read the scorecard below and if you get more than 6, you’ll know that you should consider a Master’s degree.
Having programming knowledge is one of the most important skills required to become a Data Scientist. Other than that, following are the reasons why you should definitely learn programming:
The annual pay for a Data Scientist in Melbourne is AU$121,209 on an average basis.
On an average, a data scientist in Melbourne earns AU$121,209, which is AU$7,598 more than that of Sydney.
A data scientist working in Melbourne earns AU$121,209 every year as opposed to the average annual income of a data scientist working in Brisbane, which is AU$103,716.
In Victoria, apart from Melbourne, data scientists can earn AU$91,489 per year in Docklands.
In Victoria, the demand for Data Scientist is quite high. There are several organizations looking for Data Scientists to join their teams.
The benefits of being a Data Scientist in Melbourne are mentioned below:
Data Scientist is a lucrative job that offers several perks and advantages. This includes:
Brightstar, ANZ Banking Group and Deloitte are among the companies hiring Data Scientists in Melbourne.
|1.||Python for Data Science||8 May, 2019 to 9 May, 2019|
BizData Head Office Level 9 278 Collins Street Melbourne, vic 3000 Australia
|2.||Accelerating Innovation with Data Science & Machine Learning||14 May, 2019||AWS Melbourne 8 Exhibition Street Melbourne, VIC 3000 Australia|
|3.||Citizen Science Discovery||May 19, 2019||Afton Street Conservation Park 58 Afton Street Essendon West, VIC 3040 Australia|
|4.||Launch into Data Analytics||4 May, 2019|
Academy Xi Melbourne 45 Exhibition Street #level 3 Melbourne, VIC 3000 Australia
DAMA Melbourne - Customer Master Data at Australia Post + AGM (8 May 2019)
|8 May, 2019|
0 Lonsdale Street Melbourne, VIC 3000 Australia
|6.||Free Webinar on Big Data with Scala & Spark||May 19, 2019||Melbourne, Australia|
|7.||Introduction to Python for Data Analysis: Melbourne, 22-23 May 2019||22 May, 2019 to 23 May, 2019||Saxons Training Facilities Level 8 500 Collins Street Melbourne, VIC 3000 Australia|
|8.||2019 3rd International Conference on Big Data and Internet of Things||22 Aug, 2019 to 24 Aug, 2019||La Trobe University/Plenty Rd Kingsbury, VIC 3083 Australia|
|9.||Melbourne Business Analytics Conference 2019||3 September, 2019|
Melbourne Convention and Exhibition Centre (MCEC) 1 Convention Centre Place South Wharf, VIC 3006 Australia
|10.||Free YOW! Developer Conference 2019 - Melbourne||12 Dec, 2019 to 12 Dec, 2019||Melbourne Convention Exhibition Centre 1 Convention Centre Place South Wharf, VIC 3006 Australia|
1. Python for Data Science, Melbourne
2. Accelerating Innovation with Data Science & Machine Learning, Melbourne
3. Citizen Science Discovery, Melbourne
4. Launch into Data Analytics, Melbourne
5. DAMA Melbourne - Customer Master Data at Australia Post + AGM (8 May 2019), Melbourne
6. Free Webinar on Big Data with Scala & Spark, Melbourne
7. Introduction to Python for Data Analysis, Melbourne
7. Introduction to Python for Data Analysis, Melbourne
8. 2019 3rd International Conference on Big Data and Internet of Things, Melbourne
9. Melbourne Business Analytics Conference 2019, Melbourne
10. Free YOW! Developer Conference 2019, Melbourne
|1.||Big Data & Analytics Innovation Summit||8-9 February, 2017||25 Collins S, Melbourne, VIC 3000|
|2.||Melbourne Data Science Week||May 29, 2017 - June 2, 2017|
|3.||Australia Sports Analytics Conference||August 4, 2017||Melbourne Park Function Centre Batman Avenue, Melbourne VIC 3000, Melbourne|
|4.||IAPA National Conference "Advancing Analytics"||Thursday, 18 October 2018||Bayview Eden 6 Queens Road, Melbourne|
|5.||ADMA Data Day||23 February, 2018|
Crown Promenade, Queensbridge, St & Whiteman St, Southbank VIC 3006
|6.||The 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'18).||3-6 June, 2018|
1. Big Data & Analytics Innovation Summit, Melbourne
2. Melbourne Data Science Week, Melbourne
3. Australia Sports Analytics Conference, Melbourne
4. IAPA National Conference "Advancing Analytics", Melbourne
5. ADMA Data Day, Melbourne
6. The 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'18), Melbourne
Here is the logical sequence of steps you should follow to get a job as a Data Scientist.
If you are thinking to apply for a data science job in Melbourne, the following steps will increase your chances of success:
Businesses hire data scientists because they need someone to handle all the data they have- structured or unstructured. Data is generated in mass quantities in the modern world and it is a potential goldmine for ideas. These are important so that Data Scientists can find these solutions and patterns and help businesses achieve their goals and make profits.
Data Scientist Roles & Responsibilities:
Melbourne is home to some of the leading companies such as Amazon, Move 37, ANZ, Zendesk, EY, Envato, Capgemini, General Assembly, Aginic, Deloitte. These companies are either directly based or have branches in Melbourne and are constantly in search of Data science professionals.
The salary range depends on two factors:
Take all the best qualities of a mathematician, a computer scientist, and a trend spotter and you get a data scientist. As part of his/her job, he/she must analyse large amounts of data and find relevant data to find solutions. A career path in the field of Data Science can be explained in the following ways:
Business Intelligence Analyst: A Business Intelligence Analyst figures out things about the business and analyses market trends. Data is analysed so that a data scientist can develop a clear picture of what the business needs and its stance in the industry.
Data Mining Engineer: A Data Mining Engineer examines data for the business and also does it to benefit the third party. They are also expected to create sophisticated algorithms for any further analysis of data.
Data Architect: A Data Architect works with system designers, developers, and other users to design blueprints for data management systems to protect data sources.
Data Scientist: Data scientists pursue business cases by analysing data, developing proper hypotheses, etc. This helps them understand the data and find patterns in it so that they can develop algorithms to properly use the data to help the business.
Senior Data Scientist: A Senior Data Scientist should be able to anticipate what the business needs or might need in the future. He/she must then tailor the projects and analysis to properly fit the business’ future needs.
Below are the top professional organizations for data scientists –
A referral substantially increases your chance of getting an interview or getting hired, as surveys suggest. To get referred, you must have a vast network. There are many ways to do that:
Melbourne is home to some of the eminent organizations which are always in search of skilled data science professionals. There are several career options for a data scientist –
Employers usually look for some eminent qualities while hiring a data scientist. Amazon, Move 37, ANZ, Zendesk, EY, Envato, Capgemini, General Assembly, Aginic, Deloitte etc. are some of the most renowned companies in Melbourne which are offering lucrative jobs in the data science field. We have listed some such qualities:
Data science is a field which deals with many different libraries which can be used for smooth functioning. Choosing an appropriate language is important:
Follow these steps to successfully install Python 3 on windows:
You can also install python using Anaconda as well. Check if python is installed by running the following command, you will be shown the version installed:
Python -m pip install -U pip
Note: You can install virtualenv to create isolated python environments and pipenv, which is a python dependency manager.
For a Mac OS X, you can go to the official website to install Python 3 using the .dmg package. Its better to use Homebrew to install it. For Python 3 installation on a Mac OS X, follow the steps below:
$ 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
It is also advised that you install virtualenv.
The content was sufficient and the trainer was well-versed in the subject. Not only did he ensure that we understood the logic behind every step, he always used real-life examples to make it easier for us to understand. Moreover, he spent additional time to let us consult him on Data Science-related matters outside the curriculum. He gave us advice and extra study materials to enhance our understanding. Thanks, KnowledgeHut!
Overall, the training session at KnowledgeHut was a great experience. I learnt many things. I especially appreciate the fact that KnowledgeHut offers so many modes of learning and I was able to choose what suited me best. My trainer covered all the topics with live examples. I'm glad that I invested in this training.
Everything was well organized. I would definitely refer their courses to my peers as well. The customer support was very interactive. As a small suggestion to the trainer, it will be better if we have discussions in the end like Q&A sessions.
I would like to extend my appreciation for the support given throughout the training. My special thanks to the trainer for his dedication, and leading us through a difficult topic. KnowledgeHut is a great place to learn the skills that are coveted in the industry.
I was totally impressed by the teaching methods followed by Knowledgehut. The trainer gave us tips and tricks throughout the training session. The training session gave me the confidence to do better in my job.
Knowledgehut is the best platform to gather new skills. Customer support here is very responsive. The trainer was very well experienced and helped me in clearing the doubts clearly with examples.
The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut. I really liked the way the trainer explained the concepts. He was very patient and well informed.
The course materials were designed very well with all the instructions. The training session gave me a lot of exposure to industry relevant topics and helped me grow in my career.
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