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).
Dubbed as the Sexiest Job of the 21st century by the Harvard review in 2012, the job of data scientist has become the talk of the town. The reason behind this is Data and how it has benefitted leading companies across the world.
Have you ever wondered how do companies like Amazon and Flipkart are able to recommend you products without you asking? This is because companies like Facebook and Google collect data from users based on their online activities and sell them to ad companies to earn profits.
Sydney is one of the most elite cities in the World. This city is not only technologically advanced but also enjoys a high standard of living. Advanced universities and leading companies are situated in Sydney.
Here are some other reasons which make Data Scientist such a popular career choice:
Living in Sydney has numerous benefits as it is has many universities famous for data science degree, such as St. Paul’s College, University of Sydney, University of Technology, etc.The top skills that are needed to become a data scientist include the following:
The top 5 essential behavioral traits of a top-notch data scientists are:
Being a data scientist comes with many benefits. These are not only limited to Sydney but to every major city:
There are 4 must-have business skills needed to become a successful data scientist irrespective of whether you are in Sydney or London. These skills include:
If you are looking to brush up your data science skills, you can try one of the following:
Some companies in Sydney collect data for their own use while others do it to sell it to other companies. These companies include Metigy, Opus RS, BCG Digital Ventures, ASIC, Morgan McKinley etc. Overall, the following kind of companies employs Data Scientist:
If you want to practice your data science skills using datasets, here are some that are categorized according to their difficulty and your expertise level:
The right steps to becoming a top-notch Data scientist are:
Sydney is home to some of the most recognized universities in the field of Data Science such as St. Paul’s College, University of Sydney, University of Technology, etc and has leading tech companies, such as Microsoft, Mashable, Bitglass, etc. Here are some of the key skills & steps required that will help you start a career as a data scientist.
Getting a degree is very important in Data Science. About 88% of Data Scientists have a Master's degree while 46% of them have a Ph.D. Sydney offers a huge opportunity for the aspiring data scientists as it is home to several universities such St. Paul’s College, University of Sydney, University of Technology, etc which provide advanced courses in Data science.
A degree is very important because of the following –
St. Paul’s College, University of Sydney, University of Technology, etc offer advanced degrees in Data science. These institutions are what make Sydney such a great place to be in for an aspiring data scientist. Below is a scorecard that will help you in determining if you should get a Master’s degree or not. If your total is more than 6 points, a Master’s degree is recommended:
Knowledge of programming is the most fundamental and important skill required to become a data scientist. Here is why:
The median salary of a Data Scientist in Sydney is AU$128,798 per year.
The difference in the annual salary of a Data Scientist in Sydney and Melbourne is AU$7,598.
The average annual income of data scientist in Sydney is AU$128,798, which is more than the average income of AU$138,702 in Brisbane.
A data scientist working in Sydney earns AU$128,798 every year as opposed to the average annual income of a data scientist working in Melbourne, which is AU$91,140.
The average income in North Ryde is AU$89,487 which is significantly lower than AU$128,798 earned by a data scientist in Sydney.
In New South Wales, the demand for Data Scientist is increasing by the hour. There are several listings on different portals that prove that Data Scientist is the hottest job right now.
The benefits of being a Data Scientist in Sydney are as follows:
Data Scientist is one of the hottest jobs right now. It is in great demand and has the potential for tremendous job growth. There are some perks and advantages to being a data scientist other than the obvious handsome salary. This includes the freedom to work in their field of choice. All the key organizations in almost every field are entering the world of data science. This gives data scientists, the opportunity to work in any field they like. Also, data scientists get to deal with top-level executives in their enterprise.
Asic, Clayton, Westpac Group and Deloitte are among the companies hiring Data Scientists in Sydney.
|1.||Data Science Summit||30 April, 2019 – 3 May, 2019|
Rydges World Square Hotel 389 Pitt Street Sydney, NSW 2000 Australia
|2.||Data Science Masterclass: Customer Analytics||8 May, 2019||Coder Academy 118 Walker Street #Level 3 North Sydney, NSW 2060 Australia|
|3.||Data transfer and Research Data Storage (RDS) for HPC||10 May, 2019||ABS Seminar Room 3110 Abercrombie Business School The University of Sydney, NSW 2006 Australia|
|4.||Predictive Analytics, Machine Learning, Data Science and AI - Sydney||3 June, 2019 to 4 June, 2019||Level 4, 60 York Street Sydney, NSW 2000 Australia|
|5.||Introduction to Data Science: Sydney, 26-27 June 2019||26 June, 2019 to 27 June, 2019||Level 4 60 York Street Sydney, NSW 2000 Australia|
|6.||NSW Exploration Data Workshop||7 May, 2019|
Saxons Training Facilities - Sydney Level 10, 10 Barrack Street Sydney, NSW 2000 Australia
|7.||Free Astronomical Data Archives Meeting 2019||05th August, 2019 – 08th August, 2019|
105 Delhi Road North Ryde, NSW 2113 Australia
|8.||Advanced Data Strategy for Software Engineers and Data Scientists||31 May, 2019|
WeWork, Branson Room Level 13, 50 Carrington Street Sydney, NSW 2000, Australia
|9.||YOW! Data 2019||06 May, 2019 - 07 May, 2019|
Wesley Conference Centre 220 Pitt Street Sydney, NSW 2000 Australia
|10.||Data governance for startups||11 June, 2019|
Club York (formerly Bowlers Club) York Street Sydney, New South Wales 2000 95-99 York Street Sydney, NSW 2000 Australia
1. Data Science Summit, Sydney
2. Data Science Masterclass: Customer Analytics, Sydney
3. Data transfer and Research Data Storage (RDS) for HPC, Sydney
4. Predictive Analytics, Machine Learning, Data Science and AI, Sydney
5. Introduction to Data Science, Sydney
6. NSW Exploration Data Workshop, Sydney
7. Free Astronomical Data Archives Meeting 2019, Sydney
8. Advanced Data Strategy for Software Engineers and Data Scientists, Sydney
9. YOW! Data 2019, Sydney
10. Data governance for startups, Sydney
|1.||ADMA Data Day||26 February, 2018|
Sofitel Sydney Wentworth, 61-101 Phillip St, Sydney NSW 2000
|2.||Chief Data & Analytics Officer|
20 - 22 March, 2018
The Balcony Level Cockle Bay Wharf Darling, Harbour Sydney, NSW 2000 Australia
|3.||Big Data & AI Leaders Summit||26-27 April, 2018||InterContinental Double Bay, Sydney |
|4.||Australian Data Summit||19 - 21 November, 2018|
Novotel Sydney Central 169-179 Thomas Street Sydney NSW, 2000, Australia
|5.||Future of Mining, covering also IoT, AI, and Big Data||14-15 May, 2018||279 Castlereagh Street, Sydney, 2000|
|6.||Alteryx Data + Analytics Revolution Summit||29 August, 2018|
Primus Hotel, 339, Pitt St, Sydney, NSW 2000, Australia
|7.||ICML 2017: 34th International Conference on Machine Learning||6-11 August, 2017||International Convention Centre, Sydney|
|8.||Big Data and Analytics for Retail Summit||21-22 September, 2017||161 Elizabeth Street, Sydney, NSW 2000|
1. ADMA Data Day, Sydney
2. Chief Data & Analytics Officer, Sydney
3. Big Data & AI Leaders Summit, Sydney
4. Australian Data Summit, Sydney
5. Future of Mining, covering also IoT, AI, and Big Data, Sydney
6. Alteryx Data + Analytics Revolution Summit, Sydney
7. ICML 2017: 34th International Conference on Machine Learning, Sydney
8. Big Data and Analytics for Retail Summit, Sydney
Here is the logical sequence of steps you need to follow to get a job as a Data Science professional:
The 5 important steps to prepare for Data Scientist jobs are:
A data scientist is required for analyzing the huge amount of data, discover patterns and relationships and inference information that is required to meet the goals and needs of the business.
We generate tons of data every day. This data is available in the structured as well as unstructured form. This has made the job of a data scientist all the more important. The data is a goldmine of ideas that can advance the business. It is the job of a data scientist to extract the information in order to benefit the business:
Data Scientist Roles & Responsibilities:
The base salary of a data scientist is about 36% higher than any other predictive analytics professional. Sydney is home to several leading companies such as Metigy, Opus RS, BCG Digital Ventures, ASIC, Morgan McKinley etc. which offer high salary. The average pay for a Data Scientist in Sydney, New South Wales is AU$100,149 per year.
A data scientist is one who has a good grasp over mathematics, computer science, and trend spotting. These abilities are required for deciphering large datasets, mining relevant data, and analyzing this data to make future predictions for similar data.
Here is how the career path in the field of data science can be explained:
Business Intelligence Analyst: A Business Intelligence Analyst is responsible for figuring out the business and market trends. They perform the analysis of the data to understand exactly where the business stands in the market.
Data Mining Engineer: The role of a Data Mining Engineer is to examine the data according to the needs of the business. More often than not, they are hired as a third party by an organization. They are also responsible for creating sophisticated algorithms that are required for data analysis.
Data Architect: The role of Data Architect is to work with system developers, designers, and users for the creation of blueprints used by data management systems for the integration, centralization, maintenance, and protection of the data sources.
Data Scientist: A Data Scientist pursues a business case by analyzing, creating hypotheses, developing an understanding of the data, and exploring patterns in this data. They are also responsible for creating systems and algorithms that help in using this data in a productive manner and further the interests of the organization.
Senior Data Scientist: It is the responsibility of a Senior Data Scientist to anticipate the needs of the business in the future and shape the current projects according to it. They make sure that the analysis and the systems are suited to meet the needs of the business.
Meeting and networking with other data scientist is very important because it can help you with referrals, which are an effective way of finding a job. Here are some of the ways to network with other data scientists:
Sydney is home to several leading companies such as Metigy, Opus RS, BCG Digital Ventures, ASIC, Morgan McKinley etc. which offer high salaries and demand efficiency.
Employers prefer data scientists to have mastery of the following:
Choosing a programming language is one of the most important steps in building a data science model because you would need multiple libraries to work together smoothly. Here are the 5 most popular programming languages used in the field of data science:
Here are the steps you need to follow to download and install Python 3 on Windows:
You can try using Anaconda to install python as well.
To check if python is installed on the system, you can try the following command that will show you the version of the language installed:
python -m pip install -U pip
Note: For creating isolated python environments and pipenv, python dependency manager, you can install virtaulenv.
You can either install Python 3 using a .dmg package from their official website or use Homebrew for installing the language and its dependencies. The Homebrew method is recommended. Here is how you do it:
$ 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
If you want to create isolated places where you can run different projects and use different python versions in each one of them, you can try installing virtualenv.
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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