Data Science with Python Training in Brisbane, Australia

Get hands-on Python skills and accelerate your data science career

  • Learn Python, analyze and visualize data with Pandas, Matplotlib and Scikit.
  • Create robust predictive models with advanced statistics.
  • Leverage hypothesis testing and inferential statistics for sound decision-making.
  • 250,000 + Professionals Trained
  • 55,000 + Programmers upskilled
  • 70 + Countries and counting

Grow your Data Science skills

This four-week course takes you from the fundamentals of Data Science to an advanced level. Get hands-on programming experience in Python that you'll be able to immediately apply in the real world. Equip yourself with the skills you need to work with large data sets, build predictive models and tell a compelling story to stakeholders.

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Highlights

  • 42 Hours of Live Instructor-Led Sessions

  • 60 Hours of Assignments and MCQs

  • 36 Hours of Hands-On Practice

  • 6 Real-World Live Projects

  • Fundamentals to Advanced Learning

  • Code Reviews by Professionals

Why Become a Data Scientist?

Data Science has bagged the top spot in LinkedIn’s Emerging Jobs Report for the last three years. Thousands of companies need team members who can transform data sets into strategic forecasts. Acquire in-demand data science and Python skills and meet that need.

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The KnowledgeHut Edge

Learn by Doing

Our immersive learning approach lets you learn by doing and acquire immediately applicable skills hands-on.

Real-World Focus

Learn theory backed by real-world practical case studies and exercises. Skill up and get productive from the get-go.

Industry Experts

Get trained by leading practitioners who share best practices from their experience across industries.

Curriculum Designed by the Best

Our Data Science advisory board regularly curates best practices to emphasize real-world relevance.

Exclusive Post-Training Sessions

Practical one-to-one guidance from mentors: project review and evaluation, guidance on work challenges.

Continual Learning Support

Webinars, e-books, tutorials, articles, and interview questions - we're right by you in your learning journey!

Prerequisites

Prerequisites for the Data Science with Python training program

  • There are no prerequisites to attend this course.
  • Elementary programming knowledge will be useful.

Who should attend this course?

Anyone interested in the field of data science

Anyone looking for a more robust, structured Python learning program

Anyone looking to use Python for effective analysis of large datasets

Software or data engineers interested in quantitative analysis with Python

Data analysts, economists or researchers

Data Science with Python Course Schedules

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What you will learn in the Data Science with Python course

1

Python Distribution

Anaconda, basic data types, strings, regular expressions, data structures, loops, and control statements.

2

User-defined functions in Python

Lambda function and the object-oriented way of writing classes and objects.

3

Datasets and manipulation

Importing datasets into Python, writing outputs and data analysis using Pandas library.

4

Probability and Statistics

Data values, data distribution, conditional probability, and hypothesis testing.

5

Advanced Statistics

Analysis of variance, linear regression, model building, dimensionality reduction techniques.

6

Predictive Modelling

Evaluation of model parameters, model performance, and classification problems.

7

Time Series Forecasting

Time Series data, its components and tools.

Skill you will gain with the Data Science with Python course

Python programming skills

Manipulating and analysing data using Pandas library

Data visualization with Matplotlib, Seaborn, ggplot

Data distribution: variance, standard deviation, more

Calculating conditional probability via hypothesis testing

Analysis of Variance (ANOVA)

Building linear regression models

Using Dimensionality Reduction Technique

Building Binomial Logistic Regression models

Building KNN algorithm models to find the optimum value of K

Building Decision Tree models for regression and classification

Visualizing Time Series data and components

Exponential smoothing

Evaluating model parameters

Measuring performance metrics

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Learning objectives

Understand the basics of Data Science and gauge the current landscape and opportunities. Get acquainted with various analysis and visualization tools used in data science.


Topics

  • What is Data Science?
  • Data Analytics Landscape
  • Life Cycle of a Data Science Project
  • Data Science Tools and Technologies 

Learning objectives

The Python module will equip you with a wide range of Python skills. You will learn to:

  • To Install Python Distribution - Anaconda, basic data types, strings, and regular expressions, data structures and loops, and control statements that are used in Python
  • To write user-defined functions in Python
  • About Lambda function and the object-oriented way of writing classes and objects 
  • How to import datasets into Python
  • How to write output into files from Python, manipulate and analyse data using Pandas library
  • Use Python libraries like Matplotlib, Seaborn, and ggplot for data visualization

Topics

  • Python Basics
  • Data Structures in Python 
  • Control and Loop Statements in Python
  • Functions and Classes in Python
  • Working with Data
  • Data Analysis using Pandas
  • Data Visualisation
  • Case Study

Hands-on

  • How to install Python distribution such as Anaconda and other libraries
  • To write python code for defining as well as executing your own functions
  • The object-oriented way of writing classes and objects
  • How to write python code to import dataset into python notebook
  • How to write Python code to implement Data Manipulation, Preparation, and Exploratory Data Analysis in a dataset

Learning objectives

In the Probability and Statistics module you will learn:

  • Basics of data-driven values - mean, median, and mode
  • Distribution of data in terms of variance, standard deviation, interquartile range
  • Basic summaries of data and measures and simple graphical analysis
  • Basics of probability with real-time examples
  • Marginal probability, and its crucial role in data science
  • Bayes’ theorem and how to use it to calculate conditional probability via Hypothesis Testing
  • Alternate and Null hypothesis - Type1 error, Type2 error, Statistical Power, and p-value

Topics

  • Measures of Central Tendency
  • Measures of Dispersion 
  • Descriptive Statistics 
  • Probability Basics
  • Marginal Probability
  • Bayes Theorem
  • Probability Distributions
  • Hypothesis Testing

Hands-on

  • How to write Python code to formulate Hypothesis
  • How to perform Hypothesis Testing on an existent production plant scenario

Learning objectives

Explore the various approaches to predictive modelling and dive deep into advanced statistics:

  • Analysis of Variance (ANOVA) and its practicality
  • Linear Regression with Ordinary Least Square Estimate to predict a continuous variable
  • Model building, evaluating model parameters, and measuring performance metrics on Test and Validation set
  • How to enhance model performance by means of various steps via processes such as feature engineering, and regularisation
  • Linear Regression through a real-life case study
  • Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis
  • Various techniques to find the optimum number of components or factors using screen plot and one-eigenvalue criterion, in addition to a real-Life case study with PCA and FA.

Topics

  • Analysis of Variance (ANOVA)
  • Linear Regression (OLS)
  • Case Study: Linear Regression
  • Principal Component Analysis
  • Factor Analysis
  • Case Study: PCA/FA

Hands-on

  • With attributes describing various aspect of residential homes for which you are required to build a regression model to predict the property prices
  • Reducing Dimensionality of a House Attribute Dataset to achieve more insights and better modelling

Learning objectives

Take your advanced statistics and predictive modelling skills to the next level in this advanced module covering:

  • Binomial Logistic Regression for Binomial Classification Problems
  • Evaluation of model parameters
  • Model performance using various metrics like sensitivity, specificity, precision, recall, ROC Curve, AUC, KS-Statistics, and Kappa Value
  • Binomial Logistic Regression with a real-life case Study
  • KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K
  • KNN through a real-life case study
  • Decision Trees - for both regression & classification problem
  • Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID
  • Using Decision Tree with real-life Case Study

Topics

  • Logistic Regression
  • Case Study: Logistic Regression
  • K-Nearest Neighbour Algorithm
  • Case Study: K-Nearest Neighbour Algorithm
  • Decision Tree
  • Case Study: Decision Tree

Hands-on

  • Building a classification model to predict which customer is likely to default a credit card payment next month, based on various customer attributes describing customer characteristics
  • Predicting if a patient is likely to get any chronic kidney disease depending on the health metrics
  • Building a model to predict the Wine Quality using Decision Tree based on the ingredients’ composition

Learning objectives

All you need to know to work with time series data with practical case studies and hands-on exercises. You will:

  • Understand Time Series Data and its components - Level Data, Trend Data, and Seasonal Data
  • Work on a real-life Case Study with ARIMA.

Topics

  • Understand Time Series Data
  • Visualizing Time Series Components
  • Exponential Smoothing
  • Holt's Model
  • Holt-Winter's Model
  • ARIMA
  • Case Study: Time Series Modelling on Stock Price

Hands-on

  • Writing python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Writing python code to Use Holt's model when your data has Constant Data, Trend Data and Seasonal Data. How to select the right smoothing constants.
  • Writing Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Use ARIMA to predict the stock prices based on the dataset including features such as symbol, date, close, adjusted closing, and volume of a stock.

Learning objectives

This industry-relevant capstone project under the experienced guidance of an industry expert is the cornerstone of this Data Science with Python course. In this immersive learning mentor-guided live group project, you will go about executing the data science project as you would any business problem in the real-world.


Hands-on

  • Project to be selected by candidates.

Frequently Asked Questions

Data Science with Python Training

The Data Science with Python course has been thoughtfully designed to make you a dependable Data Scientist ready to take on significant roles in top tech companies. At the end of the course, you will be able to:

  • Build Python programs: distribution, user-defined functions, importing datasets and more
  • Manipulate and analyse data using Pandas library
  • Data visualization with Python libraries: Matplotlib, Seaborn, and ggplot
  • Distribution of data: variance, standard deviation, interquartile range
  • Calculating conditional probability via Hypothesis Testing
  • Analysis of Variance (ANOVA)
  • Building linear regression models, evaluating model parameters, and measuring performance metrics
  • Using Dimensionality Reduction Technique
  • Building Binomial Logistic Regression models, evaluating model parameters, and measuring performance metrics
  • Building KNN algorithm models to find the optimum value of K
  • Building Decision Tree models for both regression and classification problems
  • Build Python programs: distribution, user-defined functions, importing datasets and more
  • Manipulate and analyse data using Pandas library
  • Visualize data with Python libraries: Matplotlib, Seaborn, and ggplot
  • Build data distribution models: variance, standard deviation, interquartile range
  • Calculate conditional probability via Hypothesis Testing
  • Perform analysis of variance (ANOVA)
  • Build linear regression models, evaluate model parameters, and measure performance metrics
  • Use Dimensionality Reduction
  • Build Logistic Regression models, evaluate model parameters, and measure performance metrics
  • Perform K-means Clustering and Hierarchical Clustering
  • Build KNN algorithm models to find the optimum value of K
  • Build Decision Tree models for both regression and classification problems
  • Build data visualization models for Time Series data and components
  • Perform exponential smoothing

The program is designed to suit all levels of Data Science expertise. From the fundamentals to the advanced concepts in Data Science, the course covers everything you need to know, whether you’re a novice or an expert. To facilitate development of immediately applicable skills, the training adopts an applied learning approach with instructor-led training, hands-on exercises, projects, and activities.

Yes, our Data Science with Python course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job.

In addition to the training hours, we recommend spending about 2 hours every day, for the duration of course.

The Data Science with Python course is ideal for:

  • Anyone Interested in the field of data science
  • Anyone looking for a more robust, structured Python learning program
  • Anyone looking to use Python for effective analysis of large datasets
  • Software or Data Engineers interested in quantitative analysis with Python
  • Data Analysts, Economists or Researcher

There are no prerequisites for attending this course, however prior knowledge of elementary programming, preferably using Python, would prove to be handy.

To attend the Data Science with Python training program, the basic hardware and software requirements are as mentioned below -

Hardware requirements

  • Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular Linux flavors
  • 4 GB RAM
  • 10 GB of free space

Software Requirements

  • Web browser such as Google Chrome, Microsoft Edge, or Firefox

System Requirements

  • 32 or 64-bit Operating System
  • 8 GB of RAM

On adequately completing all aspects of the Data Science with Python course, you will be offered a course completion certificate from KnowledgeHut.

In addition, you will get to showcase your newly acquired data-handling and programming skills by working on live projects, thus, adding value to your portfolio. The assignments and module-level projects further enrich your learning experience. You also get the opportunity to practice your new knowledge and skillset on independent capstone projects.

By the end of the course, you will have the opportunity to work on a capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a data science project in the real business world.

Data Science with Python Workshop

The Data Science with Python workshop at KnowledgeHut is delivered through PRISM, our immersive learning experience platform, via live and interactive instructor-led training sessions.

Listen, learn, ask questions, and get all your doubts clarified from your instructor, who is an experienced Data Science and Machine Learning industry expert.

The Data Science with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the live, interactive training sessions. The instructors are industry-recognized experts with over 10 years of experience in Data Science. 

The instructors will not only impart conceptual knowledge but end-to-end mentorship too, with hands-on guidance on the real-world projects.

Our Date Science course focuses on engaging interaction. Most class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, all facilitated by an instructor who is an industry expert. The focus is on developing immediately applicable skills to real-world problems.

Such a workshop structure enables us to deliver an applied learning experience. This reputable workshop structure has worked well with thousands of engineers, whom we have helped upskill, over the years. 

Our Data Science with Python workshops are currently held online. So, anyone with a stable internet, from anywhere across the world, can access the course and benefit from it.

Schedules for our upcoming workshops in Data Science with Python can be found here.

We currently use the Zoom platform for video conferencing. We will also be adding more integrations with Webex and Microsoft Teams. However, all the sessions and recordings will be available right from within our learning platform. Learners will not have to wait for any notifications or links or install any additional software.

You will receive a registration link from PRISM to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience platform and start your educational journey.

Yes, there are other participants who actively participate in the class. They remotely attend online training from office, home, or any place of their choosing.

In case of any queries, our support team is available to you 24/7 via the Help and Support section on PRISM. You can also reach out to your workshop manager via group messenger.

If you miss a class, you can access the class recordings from PRISM at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.

Should you have any more questions, please raise a ticket or email us at support@knowledgehut.com and we will be happy to get back to you.

What Learners Are Saying

Ong Chu Feng

Ong Chu Feng

Data Analyst

4/5

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 un View More

Attended Data Science with Python Certification workshop in January 2020

Astrid Corduas

Astrid Corduas

Senior Web Administrator

5/5

The skills I gained from KnowledgeHut's training session has helped me become a better manager. I learned not just technical skills but even people skills. I must say the course helped in my overall development View More

Attended PMP® Certification workshop in April 2020

Lauritz Behan

Lauritz Behan

Computer Network Architect.

5/5

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 suit View More

Attended PMP® Certification workshop in May 2020

Rosabelle Artuso

Rosabelle Artuso

.NET Developer

5/5

The course which I took from Knowledgehut was very useful and helped me to achieve my goal. The course was designed with advanced concepts and the tasks during the course given by the trainer helped me to step View More

Attended PMP® Certification workshop in August 2020

Yancey Rosenkrantz

Yancey Rosenkrantz

Senior Network System Administrator

5/5

The customer support was very interactive. The trainer took a very practical oriented session which is supporting me in my daily work. I learned many things in that session. Because of these training sessions, View More

Attended Agile and Scrum workshop in April 2020

Astrid Corduas

Astrid Corduas

Telecommunications Specialist

5/5

The instructor was very knowledgeable, the course was structured very well. I would like to sincerely thank the customer support team for extending their support at every step. They were always ready to help an View More

Attended Agile and Scrum workshop in June 2020

Sherm Rimbach

Sherm Rimbach

Senior Network Architect

5/5

Trainer really was helpful and completed the syllabus covering each and every concept with examples on time. Knowledgehut staff was friendly and open to all questions.

Attended Certified ScrumMaster (CSM)® workshop in February 2020

Goldina Wei

Goldina Wei

Java Developer

5/5

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.

Attended Agile and Scrum workshop in June 2020

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Data Science with Python

What is Data Science

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:

  1. There has been an increase in demand of data-driven decision making. 
  2. Professionals who are trained in Data Science get the highest salary in the tech world because there is a lack of well-trained professionals. 
  3. As the data collection process is conducted at an extremely high rate, the analysis of this data also needs to be done quickly and efficiently. With the help of Data Scientists, companies can take crucial marketing decisions. 

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:

  1. Python Coding
  2. Hadoop Platform
  3. R Programming
  4. Machine Learning and Artificial Intelligence 
  5. SQL database and coding
  6. Data Visualization
  7. Apache Spark
  8. Unstructured data

  • Python Coding – One of the most common and popularly used programming languages, Python is a simple and versatile, it helps in processing data which is in different formats. The language also allows Data Scientists to create Datasets which further help in the study and analysis of the data.
  • Hadoop Platform – Although it is not a requirement for this field, it is a preferred skill to have. Employers in this industry like to hire professionals who have knowledge of additional programs such as Hadoop
  • R Programming – R is a programming language that is essential for anyone to become a professional Data Scientist. It makes data science problems easier to solve. 
  • Machine Learning and Artificial Intelligence – A career in Data Science cannot be complete without the knowledge of Machine Learning and Artificial Intelligence. 
  • SQL database and coding – This language is specifically designed to access and communicate with different databases. It also helps by gaining structure of a database. 
  • Data Visualization – The job of a Data Scientist is incomplete without visualization of data. Tools like d3.js, Tableau, ggplot and matplotlib are used to visualize data. 
  • Apache Spark – Apache Spark is used to share data across the globe. It is a fast, safe and efficient way to share large amounts of data. 
  • Unstructured data – Not all data is structured therefore it is important for all data scientists to be comfortable in handling unstructured data. 

Below are the top 4 behavioral traits of a successful Data Scientist -

  • Curiosity – As a Data Scientist deals with large amounts of data on a daily basis, traits such as curiosity and hunger for knowledge are a must.
  • Clarity – It is important for a data scientist to ask the questions “why” “how” and “so what”. These questions help them in gaining clarity of the data they are handling.
  • Creativity – Data Science requires finding innovative ways to handle data, developing new tools and methods to analyze data and to visualize data. All these things require a fair amount of creativity. 
  • Skepticism – Along with creativity, some skepticism is also needed. The balance between the two ensures that the work is both innovative and practical. 

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. 

Data Scientist Skills and Qualifications

These are the business skills one must have to become a Data Scientist - 

  • Analytic Problem-Solving
  • Communication Skills
  • Intellectual Curiosity
  • Industry Knowledge
    • Analytic Problem-Solving – Before solving any problem, it is essential to understand the problem. Awareness of the field as well as a clear perspective is required to solve the problems efficiently.
    • Communication Skills – One of the key responsibilities of a Data Scientist is to effectively communicate customer analytics and deep business to companies and clients. 
    • Intellectual Curiosity: Curiosity is a desired trait in a Data Scientist. A person needs to be curious and ask the question “why” to solve problems and find efficient solutions.
    • Industry Knowledge – This is one of the most important skills required in the field of Data Science. Good knowledge of the field will help in differentiating between what is important and what needs to be ignored.

Below are the best ways to brush up your data science skills for data scientist jobs:

  • Boot camps: Brushing up on previous knowledge is important in this field. Boot camps are a great way to brush up Python basics. These 4-5 days camps offer theoretical as well as practical knowledge.
  • MOOC courses: The online courses, which are taught by industry professionals, include the latest trends in the industry.
  • Certifications: Certificate courses are a great way to learn new things and improve your resume. These are a few famous Data Science Certifications available - 
    • Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
    • Cloudera Certified Associate - Data Analyst
    • Cloudera Certified Professional: CCP Data Engineer
  • Projects: Projects are a great way to get practical experience of the field. They help in finding new and innovative solutions to problems. 
  • Competitions: Competitions like Kaggle can be really beneficial for people who like to improve their skills with the thrill of a competition which is time bound. 

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 -

  • Google Analytics is used by small companies to analyze their data as they have limited resources and data. 
  • Machine Learning tools and techniques are used by mid-size companies to track and analyze data. 

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 - 

  • Beginner Level

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.

  • Intermediate Level:

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.

  • Advanced Level:

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.

How to Become a Data Scientist in Brisbane, Australia

 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:

  1. Getting started: One should start their journey by choosing a programming language that they are comfortable with such as Python or R.
  2. Mathematics and statistics: Mathematics and Statistics play an important role in the field of Data Science. These tools help in dealing with data that can be numerical, textual or even an image. The field requires good knowledge of algebra and statistics. 
  3. Data visualization: Visualization of data is one of the most important parts of the learning path of Data Science. It helps in making things simpler and easier. Data Visualization also helps in facilitating better communication. 
  4. ML and Deep learning: Having deep learning skills to go along with basic ML skills on the CV is a must for every data scientist as it is through deep learning and ML techniques that you will be able to analyze the data given to you.

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. 

  1. Degree/certificate: A basic course which covers the fundamentals of the field is an essential part of the process of becoming a Data Scientist. This will help by teaching you the tools and techniques used and will also help in boosting your career. 
  2. Unstructured data: A major part of the job of Data Scientist is to find patterns in data. Most of the times, the data they get is unstructured. Therefore, it must be structured for it to fit into a database and to make it useful. 
  3. Software and Frameworks: As the amount of unstructured data is huge, various software and frameworks are used to structure this data. - R is a programming language used to solve statistical problems. - Hadoop and Spark are also used by Data Scientists when the amount of data exceeds the memory at hand. The difference between the two is that Spark is faster and more efficient that Hadoop. - In-depth knowledge of databases is extremely important in Data Science. Data Scientists should be proficient in SQL queries.
  4. Machine learning and Deep Learning: Machine Learning and Deep Learning is used to apply algorithms to structured data for better analysis.
  5. Data visualization: Data Visualization helps Data Scientists make informed decisions. It helps in figuring out the huge amounts of data.

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. 

  • 0 points – Strong STEM (Science/Technology/Engineering/Management) background.
  • 2 points - Weak STEM background ( biochemistry/biology/ economics or another similar degree/diploma)
  • 5 points - Non-STEM background
  • 3 points - Less than 1 year of experience in working with Python programming language.
  • 3 points - Never been part of a job that requires you to code on a regular basis.
  • 4 points - Not good at independent learning.
  • 1 point - You do not understand when we tell you that this scorecard is a regression algorithm. 

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: 

  • Data sets: Data science require the skills to work with large amounts of data sets and knowledge of programming helps a data scientist to analyze large data sets in more scientific way. 
  • Statistics:  The ability of data scientist to work with statistics is enhanced a lot with the help of knowledge of programming. A data scientist having a knowledge of statistics cannot use it very effectively without knowing programming and has no idea about the effective implementation this knowledge, due to which the knowledge of statistics will not have much use and cannot be used in his/her application of data science in his/her field of work.
  • Framework: The knowledge of programming and ability of a data scientist to use it properly and efficiently, also enables him/her to perform and utilize data science in a proper and efficient manner. This knowledge also enables a data scientist to build robust systems that can be used by an organization to create frameworks to analyse experiments, visualize data as well as manage the data pipeline at a large organization, efficiently and effectively, so that the data can be accessed by and be available to the right person at the right time.

Data Scientist Salary in Brisbane, Australia

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:

  • Opportunity to gain attention of the upper level management
  • Increased demand leads to multiple job opportunities and growth
  • Ability to work in the field of their interest

In Brisbane, companies hiring Data Scientists in Brisbane include Suncorp Group, Cook Medical and Fugro.

Data Science Conference in Brisbane, Australia

S.NoConference nameDateVenue
1.QLD | Transport & Infrastructure Series: The Future of Mobility - A Data-Led Revolution30 April, 2019

Deloitte Riverside Centre Level 23, 123 Eagle Street Brisbane City, QLD 4000 Australia

2.Azure Superpowers Tour - Brisbane22 October, 2019

SSW Brisbane Level 1 471 Adelaide Street Brisbane, QLD 4000 Australia

3.YOW! Developer Conference 2019 - Brisbane9 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 Foote29 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 QLD14 June, 2019

Maida Lilley Community Centre 5 Green Square Close Fortitude Valley, QLD 4006 Australia

6.USQ Software Carpentry with Python 2019 Workshop2 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

  • About the conference: The first Transport & Infrastructure event of the year will provide insights on the relation between transport and analytics and cognitive technologies.
  • Event Date: 30 April, 2019
  • Venue: Deloitte Riverside Centre Level 23, 123 Eagle Street Brisbane City, QLD 4000 Australia
  • Days of Program: 1
  • Timings: 6:00 pm – 8:30 pm AEST
  • Purpose: The purpose of the conference is to redefine mobility using analytics and cognitive technologies.
  • How many speakers: 4
  • Speakers & Profile:
    • Dr. Kellie Nuttall (National Lead Partner, Analytics & AI Consulting – Deloitte)
    • Laurent Offroy (Chief Operating Officer – Keolis Downer Bus)
    • Devina Hassanaly (ANZ Smart Mobility lead – SYSTRA)
    • Andry Rakotonirainy (Professor of Intelligent Transport Systems – QUT
  • Registration cost: $0 – $84.61
  • Who are the major sponsors: The French-Australian Chamber of Commerce and Industry Queensland Chapter

2. Azure Superpowers Tour - Brisbane

  • About the conference: In this one-day workshop, developers will learn to build cloud-based applications using Microsoft Azure. They will be introduced to all the new features and benefits of Microsoft Azure.
  • Event Date: 22 October, 2019
  • Venue: SSW Brisbane Level 1 471 Adelaide Street Brisbane, QLD 4000 Australia
  • Days of Program: 1
  • Timings: 9:00 am – 5:00 pm AEST
  • Purpose: The aim of the workshop is to help the developers implement these technologies in their projects.
  • Registration cost: $99
  • Who are the major sponsors: SSW

3. YOW! Developer Conference 2019 - Brisbane

  • About the conference: The two-day conference is made up of impressive lineups of international software authors, leaders and world experts. About 500 IT professionals are expected to attend the conference in Brisbane this year.
  • Event Date: 9 Dec, 2019 to 10 Dec, 2019
  • Venue: Brisbane Convention and Exhibition Centre Merivale Street South Brisbane, QLD 4101 Australia
  • Days of Program: 2
  • Timings: 8:00 AM to 6:00 PM
  • Purpose: The purpose of the conference is to help developers learn from the best and network with other like-minded developers.
  • Registration cost: $900 – $1,195
  • Who are the major sponsors: YOW! Australia - Conferences & Workshops

4. Oracle Performance Diagnostics and Tuning Seminar with Richard Foote, Brisbane

  • About the conference: The seminar is aimed at DBAs and Developers of Oracle who want to try their hands at Performance Tuning. This will help them maximize the performances of database and the applications associated with it.
  • Event Date: 29 July, 2019 to 30 July, 2019
  • Venue: Brisbane Training Choice Ground Floor, 50 Queen Street Brisbane, QLD 3000 Australia
  • Days of Program: 2
  • Timings: 9:00 AM to 5:00 PM
  • Purpose: The purpose of the seminar is to help the attendees diagnose and address performance-related issues effectively and efficiently.
  • Registration cost: $1,760 – $1,980
  • Who are the major sponsors: Richard Foote Consulting Pty Ltd

5. Easy Spatial Technologies for Yrs 7-10 Geography in QLD, Brisbane

  • About the conference: The workshop will cover spatial technologies and how to embed these tools into secondary Geography units. All the attendees will perform some hands-on activities that will help them use the practice spatial tools better.
  • Event Date: 14 June, 2019
  • Venue: Maida Lilley Community Centre 5 Green Square Close Fortitude Valley, QLD 4006 Australia
  • Days of Program: 1
  • Timings: 9:00 am – 3:00 pm AEST
  • Purpose: The purpose of the workshop is to help the attendees know how to use spatial technologies. 
  • Registration cost: $212.09 – $264.84
  • Who are the major sponsors: Contour Education

6. USQ Software Carpentry with Python 2019 Workshop, Brisbane

  • About the conference: In this workshop, attendees will deal with the basic concepts of research computing including version control, program design, task automation, and data management.
  • Event Date: 2 Oct, 2019 to 3 Oct, 2019
  • Venue: B207 A+B University of Southern Queensland Building B, 37 Sinnathamby Boulevard Springfield Central, QLD 4300 Australia
  • Days of Program: 2
  • Timings: 9:00 AM to 5:00 PM
  • Purpose: The aim of the program is to help the students and research staff in research and get things done in less time.
  • Registration cost: Free
  • Who are the major sponsors: University of Southern Queensland, Research and Innovation Division

7. Visualisation Day, Brisbane

  • About the conference: The conference will cover the methodology used to uncover the business insights from a real data set. This will include different types of visualization analytics and how to use them effectively.  
  • Event Date: September 19, 2019
  • Venue: Pacific Hotel Brisbane 345 Wickham Terrace, Spring Hill QLD 4000, Australia
  • Days of Program: 1
  • Timings: 8:45 PM – 11:45 PM AEST
  • Purpose: The purpose of the conference is to understand the fundamentals of visualization, infographics, and analytics. The conference also discusses the implementation of these visualization and analytics to support the growth of e-commerce.
  • How many speakers: 1
  • Speakers & Profile: Archana Akhaury, CTO and Founder, 1.21GWS
  • Registration cost: A$412.54 – A$685.78
  • Who are the major sponsors: 1.21 GWS 

8. Data synchronisation and implementation overview, Brisbane

  • About the conference: The session will cover data synchronization and how it will benefit your business.
  • Event Date: 9 July, 2019
  • Venue: TBA (Brisbane CBD) Australia
  • Days of Program: 1
  • Timings: 9:00 am – 5:00 pm AEST
  • Purpose: The purpose of the session is to cover the basics of data synchronization, measure the products to get the information that you need.
  • Registration cost: $385
  • Who are the major sponsors: GS1 Australia

9. SIDRA ADVANCED Two-Day Workshop, Brisbane 

  • About the conference: The workshop will include sessions and instructions on SIDRA INTERSECTION where you will learn to solve advance network issues. Also, all attendees must bring a USB key to get the SIDRA INTERSECTION project files.
  • Event Date: 18 Sept, 2019 to 19 Sept, 2019
  • Venue: Saxons Training Facilities Level 11, 300 Adelaide Street Brisbane, QLD 4000 Australia
  • Days of Program: 2
  • Timings: 8:30 AM to 4:30 PM
  • Purpose: The purpose of the workshop is to gain the complete knowledge of latest SIDRA INTERSECTION version. The workshop will help you understand the network modeling.
  • How many speakers: 2
  • Speakers & Profile:
    • Mark Besley (Scientist and Software Specialist)
    • David Nash (SIDRA INTERSECTION trainer)
  • Registration cost: $2,178 – $2,420
  • Who are the major sponsors: SIDRA SOLUTIONS

10. Technology in Retail - Data Analytics | Digital Business Workshop, Brisbane

  • About the conference: The workshop is free for Queensland businesses and helps them start using technology in retail. Also, in the end, every attendee will leave with their very own digital action plan.
  • Event Date: 5 June, 2019
  • Venue: NRA Offices Level 3 33 Park Road Milton, Queensland 4064 Australia
  • Days of Program: 1
  • Timings: 9:00 am – 2:00 pm AEST
  • Purpose: The purpose of the workshop is to discover the current data science trends and how they might impact your business by making better business decisions.
  • Registration cost: Free
  • Who are the major sponsors: National Retail Association - Queensland Government
S.NoConference nameDateVenue
1.International Conference on Machine Learning in Computational Biology3-4th April, 2017

Mercure Hotels Brisbane 85–87 North Quay, Brisbane QLD 4003 Australia

2.Conference City: Brisbane, Australia
3.Yow! Conference Brisbane9-10 December, 2018Brisbane Convention and Exhibition Centre, Merivale Street, South Brisbane, QLD 4101
4.ICRA 201821-25 May, 2018Brisbane 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

  • About the conference: National Conference on Data Science and Intelligence Information Technology (NCDSIIT) was a flagship gathering for Data-driven intelligent analysis, sensor networking, telecommunications, health-care and cloud research analysts. 
  • Event Date: 3-4th April, 2017
  • Venue: Mercure Hotels Brisbane 85–87 North Quay, Brisbane QLD 4003 Australia
  • Days of Program: Two
  • Timings: 10:00 AM onwards 
  • Purpose: It provided a platform for researchers, scientists, and research scholars to share and exchange their knowledge and results obtained through research in the field of Machine Learning in Computational Biology.
  • Registration cost:
    • Non-Student Oral/Poster Presenter Registration: 500 €
    • Student Oral/Poster Presenter Registration: 400 €
    • Listener Registration: 300 €

    2. Yow! Conference, Brisbane

    3. ICRA 2018, Brisbane

    • About the conference: The International Conference on Robotics and Automation (ICRA) was a platform for researchers working in the field of robotics and related areas to showcase their work.
    • Event Date: 21-25 May, 2018
    • Venue: Brisbane Convention and Exhibition Centre, Merivale Street, South Brisbane, QLD 4101
    • Days of Program: Five
    • Timings: 8:00 AM onwards 
    • Purpose: The conference brought together leading innovators and researchers in the field of robotics and automation to showcase their work through presentations and discussions and contribute to science and technology in robotics and automation.

    4. SIMPAR 2018, Brisbane

    • About the conference: The 2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR 2018) held in Brisbane was the leading conference which allowed discussions and presentations in robotics and automation.
    • Date: 16-19 May, 2018
    • Venue: Hilton Brisbane, 190 Elizabeth Street, Brisbane, Queensland, 4000, Australia
    • Days of program: Four 
    • Purpose: The purpose of this conference was to bring together researchers and scientists working in the field of robotics and automation to showcase their work through presentations and discussions.

    Data Scientist Jobs in Brisbane, Australia

     Steps to follow to get a job as a Data Scientist.

    1. Getting started
    2. Mathematics
    3. Libraries
    4. Data visualization
    5. Data preprocessing
    6. Machine Learning and Deep Learning
    7. Natural Language processing
    8. Polishing skills
    • Getting started: Select a programming language like Python or R language that you know and feel comfortable to work and understand the data science and roles and responsibilities.
    • Mathematics: Good command over both mathematics as well as statistics and analysis of raw data, finding patterns and relationships between them and representing them in a more scientific and logical manner is required. Following topics needs your attention:
      • Descriptive statistics
      • Probability
      • Linear algebra
      • Inferential statistics
    • Libraries: Preprocessing the data and plotting the structured data and finally applying to ML algorithms are part of efficient use of Data science, like:
      • Scikit-learn
      • SciPy
      • NumPy
      • Pandas
      • ggplot2
      • Matplotlib
    • Data visualization: Making use and analyzing the data given, by finding patterns and graph is the most popular, effective and easy way to visualize and understand data. Following are used for this task:
    1. Matplotlib - Python
    2. Ggplot2 - R

    • Data preprocessing: The unstructured and raw data form and requires preprocessing and structuring in order to enable it analysis-ready. After preprocessing, with the help of tools the data would be structured and systematic and can be injected into ML tool for analysis.
    • ML and Deep learning: A deep learning skills to work, along with basic ML skills to work with a huge set of data and knowledge on topics like neural networks, CNN, and RNN is required.
    • Natural Language processing: Every data scientist should be an expert in NLP, to process text form of data and its classification as well. 
    • Polishing skills: Competitions like Kaggle etc. provide platform to exhibit your data science skills

    For a data science job, follow the below steps to prepare:

    • Study: To prepare for an interview, cover all important topics, including-
      • Probability
      • Statistics
      • Statistical models
      • Machine Learning
      • Understanding of neural networks
    • Meetups and conferences: Tech meetups and data science conferences help in building and expanding your network and your professional connections.
    • Competitions: Participate in online competitions like Kaggle to implement, test and polish your skills. 
    • Referral: A recent survey has found that referrals are the primary source of interviews in data science companies. The best way to go about this is to keep your LinkedIn profile updated.
    • Interview: Sit for interviews if you are prepared for them and learn from the unanswered questions and study them for the next interview.

     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:

    • A data scientist is required to fetch relevant data from the huge amount of data that is available. This data may be structured as well as unstructured. 
    • The data that has been extracted should be organized and analyzed. 
    • A data scientist is required to create tools, techniques and programs related to Machine Learning to make sense of the data.

    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 – 

    • Brisbane Data Science Meetup
    • Brisbane Data Driven Customer Engagement Meetup
    • Project Data Analytics Community - Brisbane Meetup Group
    • Brisbane Data Analytics Meetup
    • Queensland Power BI User Group

    Referrals are the most effective way to get hired. Some of the other ways to network with data scientists are:

    • Social gatherings like Meetup 
    • Online platform like LinkedIn
    • Data science conference

    There are several career options for a data scientist – 

    1. Data Scientist
    2. Data Analyst
    3. Data Administrator
    4. Data Architect
    5. Marketing Analyst
    6. Business Intelligence Manager 
    7. Business Analyst
    8. Data/Analytics Manager

    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:

    • Education: As this is such a specialized field, a degree is important to get a job in the industry. Additional certification also helps in increasing your chances.
    • Programming: As the work of a Data Scientist requires extensive programming, this is a major requirement for the employers. Languages like Python and R are used the most in the field.
    • Machine Learning: Machine Learning is used to analyze patterns in data. As this constitutes about 50% of the work done by Data Scientists, it is a highly required tool.
    • Projects: Projects are a great way to build your portfolio. They show that you have practical experience in the field.

    Data Science with Python Brisbane, Australia

    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.

    • R: R programming language has a steep learning curve but it has numerous advantages -
      • High quality open source packages are provided by the R open source community.
      • The language has various statistical functions and can smoothly handle matrix operations.
      • R provides great data visualization tools.
    • Python: Even though Python has fewer packages than R, it is still one of the most popular languages used in the field of Data Science. It provides the Data Scientists with the most number of libraries. Along with this it is easy to learn and implement. Python also has a big open source community. 
    • SQL: SQL is a structured query language. It has a readable syntax and it is efficient in updating and manipulating data. 
    • Java: As compared to Python and R, Java has fewer libraries, but it is compatible which means that most systems are coded in Java. This results in easier integration.
    • Scala: Scala is a programming language which is quite complex and runs on JVM. Due to this, it can also run on Java which makes it a preferred language. A high performance cluster computing is obtained when used with Apache Spark.

    Follow these steps to successfully install Python 3 on windows:

    • Download and setup: Visit the download page to setup Python. During the installation process, select the option of adding Python 3.x to PATH. This will allow you to use the functionality offered by Python from terminal.

    An alternate way to install Python is via Anaconda. To check whether Python is installed, run the following command – 

    Python --version

    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:

    • Install xcode: Follow this command to install brew (you need Apple’s Xcode package) - 

    $ xcode-select --install

    • Install brew: Homebrew which is a package manager for Apple can be installed using this command - 

    /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

    Confirm if it is installed by typing: brew doctor

    • Install python 3: To install the latest version of python, use:

    brew install python

      • To confirm its version, use: python --version

    Virtualenv can be installed to run isolated python versions

     

    Data Science with Python Certification Course in Brisbane

    If you are a student aspiring to study and work in a foreign country, what characteristics would you look for? You may look for a safe city with good infrastructure, friendly people, diverse ethnicity, and a place that promotes world class education. A place that provides all this and much more is Brisbane. This populous city located in the Australian state of Queensland while being a thriving business and commercial center also promotes art and artisans. It has seen a steady economic growth due to the presence of several national and international conglomerates. Industries include IT, manufacturing, health care, and financial companies. Major home grown companies include Sunsuper, Credit Union Australia, Donut King, Virgin Australia, Krome Studios, and several others. These companies are always on the lookout for talented professionals and give particular importance to trends like the PMI suite of certifications, Agile and Scrum certifications, and financial certifications. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.

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