Data Science with Python Training in Melbourne, Australia

Get the ability to analyze data with Python using basic to advanced concepts

  • 40 hours of Instructor led Training
  • Interactive Statistical Learning with advanced Excel
  • Comprehensive Hands-on with Python
  • Covers Advanced Statistics and Predictive Modeling
  • Learn Supervised and Unsupervised Machine Learning Algorithms
Group Discount

Description

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.


What You Will Learn

Prerequisites

There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.

3 Months FREE Access to all our E-learning courses when you buy any course with us

Who should Attend?

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

KnowledgeHut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.

Curriculum

Learning Objectives:

Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in  data science.

Topics Covered:

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

Hands-on:  No hands-on

Learning Objectives:

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.

Topics Covered:

  • Python Basics
  • Data Structures in Python
  • Control & Loop Statements in Python
  • Functions & Classes in Python
  • Working with Data
  • Analyze Data using Pandas
  • Visualize Data 
  • Case Study

Hands-on:

  • Know how to install Python distribution like Anaconda and other libraries.
  • Write python code for defining your own functions,and also learn to write object oriented way of writing classes and objects. 
  • Write python code to import dataset into python notebook.
  • Write Python code to implement Data Manipulation, Preparation & Exploratory Data Analysis in a dataset.

Learning Objectives: 

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.

Topics Covered:

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

Hands-on:

Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario

Learning Objectives: 

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.

Topics Covered:

  • 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, you are required to build a regression model to predict the property prices.
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling.

Learning Objectives: 

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.

Topics Covered:

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

Hands-on: 

  • With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month. This can help the bank be proactive in collecting dues.
  • Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
  • Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Learning Objectives:

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

Topics Covered:

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

Hands-on:  

  • Write python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Write 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.
  • Write Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Dataset including features such as symbol, date, close, adj_close, volume of a stock. This data will exhibit characteristics of a time series data. We will use ARIMA to predict the stock prices.

Learning Objectives:

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.

Topics Covered:

  • Industry relevant capstone project under experienced industry-expert mentor

Hands-on:

 Project to be selected by candidates.

Projects

Predict House Price using Linear Regression

With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.

Predict credit card defaulter using Logistic Regression

This project involves building a classification model.

Read More

Predict chronic kidney disease using KNN

Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.

Predict quality of Wine using Decision Tree

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).

Note:These were the projects undertaken by students from previous batches. 

Data Science with Python

What is Data Science

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: 

  1. A trend of increasing demand for decision-making driven by data analysis.  
  2. A professional that is properly trained in data science will be given a pretty decent salary. Also, there is a shortage of such trained data scientists in Melbourne. 
  3. Today, businesses collect and use alarming amounts of data at great speeds. This means that this data needs to be analysed with similar vigour. This needs to be done because this data is required to make important situations and data scientists are specially equipped to help companies in this aspect.

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:

  1. Python Coding: Python coding is often the first choice for many data scientists since it is easy to use and can also handle large amounts of data. It is versatile and also gives you the option of creating data sets. 
  2. R Programming: While it is important to learn a coding language, a data scientist must also know a good analytical tool. This combination will help you become a master data scientist. R Programming makes problems easier to solve. 
  3. Hadoop Platform: Hadoop isn’t particularly necessary for learning data science but its a skill that looks very impressive on your social media profiles. LinkedIn studies even report that Hadoop is one of the leading skills that employers look for. 
  4. SQL database and coding: SQL helps data scientists work on data and also communicate better. It decreases the level of technical skills needed to perform proper operations on a database. 
  5. Machine Learning and Artificial Intelligence: Delving in Machine Learning (ML) and Artificial Intelligence helps a lot while seeking jobs as a data scientists in Melbourne. The two subjects deal with many models, like: 
    • Reinforcement Learning
    • Neural Network
    • Adversarial learning 
    • Decision trees
    • Machine Learning algorithms
    • Logistic regression etc.
  6. Apache Spark: Apache Stark is very similar to Hadoop except the fact that it is faster and uses the better technology of caches in system memory. It is also better since it runs algorithms for data science faster. There’s also a lower chance of losing data with Apache Spark. 
  7. Data Visualization: Data Scientists use tools like d3.js, Tableau, ggplot, and matplotlib to visualise data to make the results of analysis easier to understand. 
  8. Unstructured data: Data scientists need to be able to work with data that hasn’t been organized into simple databases. Unstructured data is usually present as social media posts, videos, audio files, reviews, etc. 

As a data scientist, you need these 5 traits to get hired in Melbourne-  

  • Curiosity – Every data scientist needs to have an insatiable thirst for knowledge so they can handle the large amount of data every day. 
  • Clarity – As a data scientists the companies in Melbourne that hire you will be relying on you to handle crucial data. So, you need to have a sense of clarity to be able to clean up data sets and write new codes.  
  • Creativity - There are always hidden patterns and relationships within data and a data scientist needs to find creative ways to visualise this to make sense of it. If you don’t know what is important, you won’t be able to prioritize what to keep. 
  • Skepticism – Data Scientists can be creative but they’ll be dealing with very real data and thus, need to be skeptical as well.

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-

  1. High Pay: You need very high qualifications to get a job as a data scientist and that means that the job comes with a great salary. The average pay for a Data Scientist in Melbourne is AU$100,149 per year.
  2. Good bonuses: Even though it is as part of their pay, data scientists get many incentives and perks as part of their job. 
  3. Education: To match the qualifications required for the job, people pursuing data science often complete Master’s or PhDs. Even if you don’t plan to pursue data science, you still have many other career options. 
  4. Mobility: These jobs are usually located in developed countries. So, getting a job in a city like Melbourne automatically brings you to a city with a high standard of living.
  5. Network: Networking is a huge part of data science since you’ll be dealing with many academic journals and research. You can then use these contacts for referrals.

Data Scientist Skills and Qualifications

Below is the list of top business skills needed to become a data scientist: 

  1. Analytic Problem-Solving – To be able to solve any data related problems presented to you, you’ll need to be able to analyse and understand it first.
  2. Communication Skills – For someone who isn’t trained in data science, it can be difficult to understand. A data scientist needs to have excellent communication skills to help businesses understand what needs to be conveyed.
  3. Intellectual Curiosity: To answer the problems posed to you, you must be ready to constantly ask ‘why’.
  4. Industry Knowledge – To choose what information needs to be retained, a thorough knowledge of the industry is required. 

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:

  • Boot camps: Go to boot camps around Melbourne to brush up on your Python skills. They last for 4-5 days and leave you with theoretical and hands-on experience. 
  • MOOC courses: MOOC courses help data scientists keep up with changing trends. The course and the assignments are updated regularly.
  • Certifications: Your CV looks great with additional certifications and that increases your chances of getting hired. There are some certifications that employers prefer: 
    • Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
    • Cloudera Certified Associate - Data Analyst
    • Cloudera Certified Professional: CCP Data Engineer
  • Projects: Projects help you explore new avenues and help you come up with innovative solutions to pre-answered questions as well.
  • Competitions: Online competitions like Kaggle are a great way to challenge yourself and improve your problem solving skills. 

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: 

  • Small companies use web searches, usually Google Analytics, for data analysis. They don’t handle much data and don’t need a lot of resources. 
  • Mid-range companies in Melbourne look for professionals who can apply ML techniques because they have some data to deal with.  
  • Bigger companies usually have data science teams already. They usually look for data scientists dealing with specializations, like Visualization, ML expert etc.

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:

  • Beginner Level
    • Iris Data Set: The Iris Data Set is the easiest data set to use to classify data easily. It has 4 columns and 50 rows. It is very resourceful when it comes to pattern recognition.Practice Problem: Predict the class of a flower on the basis of these parameters.  
    • Loan Prediction Data Set: The banking industry is one of the largest markets for data scientists and the  Loan Prediction data set gives the learner experience of working with banking and insurance, and related concepts. This data set has 13 columns and 615 rows and is a classification problem set.Practice Problem: Find out if the loan given will be approved by a bank or not.
    • Bigmart Sales Data Set: The Retail sector is the other market that uses data science for data analysis. They have many operations that have customizations, and inventory management, etc. The Bigmart Sales Data Set is especially used for Regression problems and has 12 variables and 8523 rows.Practice Problem: Find out how many sales a retail store can make.
  • Intermediate Level:
    • Black Friday Data Set: This data set deals with sales data from retail stories and combines engineering skills with data related to shoppers’ experiences on Black Friday. The Black Friday data set comprises of 12 columns and 550,069 rows. It’s a regression problem set. Practice Problem: Predict the total purchase made.
    • Human Activity Recognition Data Set: The Human Activity Data Set deals with data from 30 human subjects that are connected using smartphones with internal sensors. It consists of 561 columns and 10,299 rows.Practice Problem: Predict human activity categorically.
    • Text Mining Data Set: This data set deals with aviation safety reports. The Text Mining Data Set has 30,438 rows and 21,519 columns and is an example of a high dimensional and multi-classification problem.
      Practice Problem: Classify the documents based on how they’re labelled.
  • Advanced Level:
    • Urban Sound Classification: An Urban Sound Classification data set is used to help a Machine Learner deal with real world problems using audio clippings. This data set deals with 8,732 sound clippings of urban sounds that can be organized into 10 classes.
      Practice Problem: Classify the type of sound that is obtained from a particular audio.
    • Identify the digits data set: This data set has 7000 images with a 28x28 dimension ratio stored in 31MB of space. The images and their elements can then be studied and analysed.
      Practice Problem: Identify the digits present in a given image

    • Vox Celebrity Data Set: The Vox Celebrity Data Set is used for large scale speaker identification. Data scientists can use this data set to learn speech recognition and identification using voice clips of celebrities talking, taken from YouTube. The data set has a collection of 100,000 words spoken by 1,251 celebrities worldwide
      Practice Problem: Identify the celebrity using the given voice sample.

How to Become a Data Scientist in Melbourne, Australia

Below are the right steps to becoming a successful data scientist:

  1. Getting started: Choose the programming language that best suits you. Ideal choices would be Python and R languages. 
  2. Mathematics and statistics: Data scientists need to have a basic understanding of algebra and data statistics so they can analyse data properly. 
  3. Data visualization: To make data science concepts easier to understand, you need to be able to make technical data easier to understand for non-technical teams. This can be done using data visualization. 
  4. ML and Deep learning: Brush up on your deep learning skills and basic ML and update your CV so that employers are able to know your capabilities easily.

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”. 

  1. Degree/certificate: The field keeps changing and that is why Data Scientists have more PhDs than any other professional. Any study requires you to get necessary degrees which can be done by completing online or offline courses. They can teach you how to deal with cutting-edge tools and boost your career. 
  2. Unstructured data: You must be able to handle and manipulate data properly. A data scientist needs to be able to find patterns in unstructured data. 
  3. Software and Frameworks: Data scientists should be able to deal with large amounts of data. Knowledge of software and frameworks with a programming language like R is important.
    • Approximately, 43% data scientists use R language for analysis making it a very popular programming language.
    • Hadoop framework is used when there isn’t enough space to handle the amount of data available. Spark, however, is becoming more popular since it does the same work but faster. There is also less chance of losing data with Spark. 
    • Data scientists are expected to understand SQL queries properly. To do that, you must focus on database learning too.  
  4. Machine learning and Deep Learning: Deep learning is used to train the model to deal with the data available, so that the data can be analysed using the proper algorithms. 
  5. Data visualization: Data scientists are often asked to deal with large amounts of data and it becomes their job to analyse the data and provide answers to the business using graphs and charts. This is usually done by data analysis and visualization. Some of the tools used for this purpose are matplotlib, ggplot2 etc.

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 – 

  • Networking – You will meet a lot of people while you pursue a degree. Networking is a major asset.
  • Structured learning – Following a time table and keeping up with the curriculum is effective instead of impulse learning.
  • Internships –  An internship helps because it adds practical experience to the theory you’re learning.
  • Recognized academic qualifications for your résumé – A degree from a prestigious institution will add weightage to your CV and make you desirable for top jobs.

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.

  • You have a strong background in STEM (Science/ Technology/ Engineering/ Management): 0 points
  • You have a weak STEM background ( biochemistry/biology/ economics or another similar degree/diploma): 2 points
  • You are from a non-STEM background: 5 points
  • You have less than 1 year of experience in working with Python programming language: 3 points
  • You have never been part of a job that asked for regular coding activities: 3 points
  • You’re skeptical about your ability to learn independently: 4 points
  • You do not understand what we mean when we say that this scorecard is a regression algorithm: 1 point

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: 

  • Data sets: Data science involves analysis of large amounts of data, which are usually put into data sets. Programming knowledge is required to properly analyse these data sets.
  • Statistics: A knowledge of statistics isn’t of any use if a data scientist doesn’t know how to apply it properly. Learning to program actually helps you improve upon your statistical skills. 
  • Framework: Data scientists often build systems that help organizations easily run experiments, visualise data, and even handle data for larger businesses.

Data Scientist Salary in Melbourne, Australia

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:

  • High income
  • Multiple job opportunities
  • Job growth

Data Scientist is a lucrative job that offers several perks and advantages. This includes:

  • They get to connect with top management due to their work in delivering business insights after careful analysis of raw data.
  • They have the luxury to work in their field of interest. All the major players of all the fields are investing time and money in data science giving data scientists the opportunity to work in the field they like.

Brightstar, ANZ Banking Group and Deloitte are among the companies hiring Data Scientists in Melbourne. 

Data Science Conferences in Melbourne, Australia

S.NoConference nameDateVenue
1.Python for Data Science8 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 Learning14 May, 2019AWS Melbourne 8 Exhibition Street Melbourne, VIC 3000 Australia
3.Citizen Science DiscoveryMay 19, 2019Afton Street Conservation Park 58 Afton Street Essendon West, VIC 3040 Australia
4.Launch into Data Analytics4 May, 2019

Academy Xi Melbourne 45 Exhibition Street #level 3 Melbourne, VIC 3000 Australia

5.

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

  • About the conference: It is a two-day seminar that will focus on the fundamentals of Python and help you understand web-deployed machine learning.
  • Event Date: 8 May, 2019 to 9 May, 2019
  • Venue: BizData Head Office Level 9 278 Collins Street Melbourne, vic 3000 Australia
  • Days of Program: 2
  • Timings: Wed 08/05/2019, 9:00 am – Thu, 09/05/2019, 5:00 pm AEST
  • Purpose: The purpose of the seminar is to help the attendees learn the manipulation of data to build models and explore Microsoft AzureML Python libraries.
  • Registration cost: $1980
  • Who are the major sponsors: BizData

2. Accelerating Innovation with Data Science & Machine Learning, Melbourne

  • About the conference: The seminar will introduce you to the core concept of Data Science, Artificial Intelligence, and Machine Learning. Also, you will learn to use these tools and techniques in your business.
  • Event Date: 14 May, 2019
  • Venue: AWS Melbourne 8 Exhibition Street Melbourne, VIC 3000 Australia 
  • Days of Program: 1
  • Timings: 12:00 pm to 2:00 pm (AEST)
  • Purpose: The purpose of the seminar is to learn the real-world applications of Data Science, Machine Learning, and Artificial Intelligence. Also, you will learn about some common challenges, opportunities and misconceptions surrounding the services.
  • Speakers & Profile: Kale Temple, senior data scientist and co-founder of Intellify, Australia's leading machine learning and artificial intelligence consulting company. 
  • Whom can you Network with in this Conference: You will be able to network with members of other organizations, including IT managers, business managers, anyone involved with AI or ML project, looking to adopt these technologies.
  • Registration cost: Free
  • Who are the major sponsors: Intellify

3. Citizen Science Discovery, Melbourne

  • About the conference: This conference aims at educating the common citizens and helps them contribute to the environment by becoming a citizen scientist.
  • Event Date: May 19, 2019
  • Venue: Afton Street Conservation Park 58 Afton Street Essendon West, VIC 3040 Australia
  • Days of Program: 1
  • Timings: 10:00 AM – 12:00 PM AEST
  • Purpose: The purpose of the conference is to raise awareness and discover new facts about the native species.
  • Registration cost: Free
  • Who are the major sponsors: Moonee Valley City Council

4. Launch into Data Analytics, Melbourne

5. DAMA Melbourne - Customer Master Data at Australia Post + AGM (8 May 2019), Melbourne

  • About the conference: The conference aims at using master data to help businesses get embraced and leveraged value.
  • Event Date: 8 May, 2019
  • Venue: 50 Lonsdale Street Melbourne, VIC 3000 Australia
  • Days of Program: 1
  • Timings: 5:30 pm – 7:00 pm AEST
  • Purpose: The purpose of the conference is to share the experiences where master data helped the businesses make informed decisions.
  • Speakers & Profile: 
    • Chris Doyle (Information Management Specialist)
    • Cameron Towt (Data governance and Analytics leader)
  • Registration cost: Free
  • Who are the major sponsors: DAMA Melbourne

6. Free Webinar on Big Data with Scala & Spark, Melbourne

  • About the conference: This is an introductory session on Big Data where you will be able to learn and practice. At CloudxLab, we have a free Live Webinar on Big Data with Spark & Scala. This introductory session is for those who want to learn as well as for those who want to practice.
  • Event Date: May 19, 2019
  • Venue: Melbourne, Australia
  • Days of Program: 1
  • Timings: 11:30 AM – 2:30 PM AEST
  • Purpose: The purpose of the seminar is to deal with Big Data, its importance and applications and understanding the Spark Architecture.
  • Registration cost: Free
  • Who are the major sponsors: CloudxLab

7. Introduction to Python for Data Analysis, Melbourne

  • About the conference: The conference will provide you an opportunity to network with analysts and data scientists from across the globe and discuss data mining, visualization and statistical analysis.
  • Event Date: 22 May, 2019 to 23 May, 2019
  • Venue: Saxons Training Facilities Level 8 500 Collins Street Melbourne, VIC 3000 Australia
  • Days of Program: 1
  • Timings: Wed, 22/05/2019, 9:30 am – Thu, 23/05/2019, 5:00 pm AEST
  • Purpose: The purpose of the conference is to help the attendees use Python and R in the analysis pipelines and production environments.
  • Speakers & Profile: Courses are taught by Dr Eugene Dubossarsky and his hand-picked team of highly skilled instructors.
  • Registration cost: $2,112 – $2,640
  • Who are the major sponsors: Presciient

7. Introduction to Python for Data Analysis, Melbourne

  • About the conference: The conference will provide you an opportunity to network with analysts and data scientists from across the globe and discuss data mining, visualization and statistical analysis.
  • Event Date: 22 May, 2019 to 23 May, 2019
  • Venue: Saxons Training Facilities Level 8 500 Collins Street Melbourne, VIC 3000 Australia
  • Days of Program: 1
  • Timings: Wed, 22/05/2019, 9:30 am – Thu, 23/05/2019, 5:00 pm AEST
  • Purpose: The purpose of the conference is to help the attendees use Python and R in the analysis pipelines and production environments.
  • Speakers & Profile: Courses are taught by Dr Eugene Dubossarsky and his hand-picked team of highly skilled instructors.
  • Registration cost: $2,112 – $2,640
  • Who are the major sponsors: Presciient

 8. 2019 3rd International Conference on Big Data and Internet of Things, Melbourne

  • About the conference: The conference will have discussions on the mobile network, web based information creation, and software defined networking technology. It includes the use of information technology to sense, predict and control the physical world.
  • Event Date: 22 Aug, 2019 to 24 Aug, 2019
  • Venue: La Trobe University/Plenty Rd Kingsbury, VIC 3083 Australia
  • Days of Program: 1
  • Timings: Thu, Aug 22, 2019, 8:30 AM – Sat, Aug 24, 2019, 6:00 PM AEST
  • Purpose: The purpose of the conference is to empower the business process by using Internet of Things (IoT) to redesign the business models and processes.
  • Registration cost: Free
  • Who are the major sponsors: SAISE

9. Melbourne Business Analytics Conference 2019, Melbourne

  • About the conference: The conference will showcase the use of Data Science and Data Analytics to data-driven practitioners.
  • Event Date: 3 September, 2019
  • Venue: Melbourne Convention and Exhibition Centre (MCEC) 1 Convention Centre Place South Wharf, VIC 3006 Australia
  • Days of Program: 1
  • Timings: 8:00 am – 6:30 pm AEST
  • Purpose: The purpose of the conference is to provide a platform to senior executives, researchers and industry professionals to discuss the use of Data Science, Big Data, Machine Learning, AI and Advanced Analytics to make important business decisions.
  • Registration cost: $412.50 – $825
  • Who are the major sponsors: Melbourne Business School

10. Free YOW! Developer Conference 2019, Melbourne

  • About the conference: The conference is designed for developers and will have speakers which are international software authors, world experts, and thought leaders.
  • Event Date: 12 Dec, 2019 to 12 Dec, 2019
  • Venue: Melbourne Convention Exhibition Centre 1 Convention Centre Place South Wharf, VIC 3006 Australia
  • Days of Program: 1
  • Timings: Thu, 12/12/2019, 8:00 am – Fri, 13/12/2019, 6:00 pm AEDT
  • Purpose: The purpose of the conference is to bring together like-minded developers so that they can learn from world-class experts.
  • Registration cost: $900 – $1,195
  • Who are the major sponsors: YOW! Australia - Conferences & Workshops
S.NoConference nameDateVenue
1.Big Data & Analytics Innovation Summit8-9 February, 201725 Collins S, Melbourne, VIC 3000
2.Melbourne Data Science WeekMay 29, 2017 - June 2, 2017
3.Australia Sports Analytics ConferenceAugust 4, 2017Melbourne 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

  • About the conference: The Conference brought together around 25 speakers from top companies dealing in data science and analytics technologies, to discuss and share knowledge on latest and upcoming trends in Big Data and Analytics.
  • Event Date: 8-9 February 2017
  • Venue: 25 Collins S, Melbourne, VIC 3000
  • Days of Program: 2
  • Purpose: The purpose of the conference was to lay focus on important aspects of data science technology like Predictive Analytics, Advanced Analytics, Cloud Computing, Machine Learning & Algorithms, and many more.
  • Speakers & Profile:
    • Ai-Hua Kam - Head of Data & Technology, Compliance Program, Standard Chartered
    • Glen Ryman - Independent Data Science Consultant
    • Sveta Freidman - Director of Data Analytics & Science, Carsales
    • Dan Richardson - Head of Data & Targeting, Yahoo
    • Violetta Misiorek - Senior Manager, Data Science, Suncorp
    • David Scott - Head of Insights, Optus
    • Rachna Dhand - Senior Data Scientist, BHP
    • Darren Abbruzzese - General Manager, Technology Data
    • Alistair Dorans - Manager, Digital Insights & Data
  • Who were the major sponsors:
    • KDnuggets
    • Hortonworks
    • Mathworks
    • SAP
    • MARP
    • Cloudera

    2. Melbourne Data Science Week, Melbourne

    • About the conference: This event provided 4 days of tutorials on data science and discussion on ideas, applications and the latest tools and platforms used in data science.
    • Event Date: May 29, 2017 - June 2, 2017
    • Days of Program: 4
    • Timings: 8:00 am to 8:00 pm
    • Purpose: This event connected data specialists from Melbourne and had discussions on technologies related to data science and the challenges in the real world, in order to reverse brain drain in Australia.
    • Who were the major sponsors:
      • ANZ
      • Rubix
      • KPMG
      • iSelect

      3. Australia Sports Analytics Conference, Melbourne

      • About the conference: The conference highlighted the role of data analytics in global sports.
      • Event Date: August 4, 2017
      • Venue: Melbourne Park Function Centre Batman Avenue, Melbourne VIC 3000· Melbourne
      • Days of Program: 1
      • Timings: 8:15 AM to 6:30 PM
      • Purpose: The purpose of the conference was to provide a platform for the emerging innovators, startups, and media to showcase their work in the field of data science that can be applied in the sports industry. 
      • Registration cost: $250+GST
      • Who were the major sponsors:
        • KPMG
        • Catapult
        • Kinduct
        • Klip desk
        • stack sports

        4. IAPA National Conference "Advancing Analytics", Melbourne

        • Event Date: Thursday, 18 October 2018
        • Venue: Bayview Eden, 6 Queens Road, Melbourne
        • Days of Program: 1
        • Timings: 7:30am - 6:00pm
        • Purpose: The purpose of the conference was to interlink the data and analytics for enhanced and better future in business.
        • How many speakers: 22
        • Speakers & Profile:
          • Alan Eldridge - Director of Sales Engineering APJ, Snowflake Computing
          • David Bloch - GM Advanced Analytics, Fonterra
          • Genevieve Elliott - General Manager – Data, Analytics and Customer Strategy, Vicinity Centres
          • Kate Carnell AO - Ombudsman, Australian Small Business and Family Enterprise Ombudsman
          • Dr. Alex Gyani - Principal Advisor, The Behavioural Insights Team
          • Kathryn Gulifa - Chief Data & Analytics Officer, WorkSafe Victoria
          • Rayid Ghani - Director, Center for Data Science and Public Policy, University of Chicago
          • Amanda Fleming - Chief Transformation Officer, Super Retail Group
          • Michael Ilczynski - CEO, SEEK
          • Sandra Hogan - Group Head, Customer Analytics, Origin Energy
          • John Hawkins - Data Scientist, DataRobot
          • Kieran Hagan - Big Data and Analytics Technical Team Leader for Australia and New Zealand, IBM
          • Matt Kuperholz - Chief Data Scientist, PwC
          • Jamie McPhee - CEO, ME Bank
          • Tim Manns - Chief Data Officer & Co-Founder, PASCAL
          • Michelle Perugini - Co-Founder, Life Whisperer
          • Glen Rabie - CEO and Co-founder, Yellowfin
          • Bradley Scott -COO, FaceMe
          • Dr. Clair Sullivan - Chief Digital Health Officer, Metro North Hospital and Health Service
          • Dr. Brian Ruttenberg - Principal Scientist, NextDroid
          • Will Scully-Power - Chief Executive Officer & Co-Founder, PASCAL
          • Antony Ugoni - Director, Global Analytics and Artificial Intelligence, SEEK and Chair of IAPA

          •  Registration cost: 
            • Member: $580   Team of 10: $465 per ticket
            • Non-Member: $730  Team of 10: $586 per ticket
          • Who were the major sponsors:
            • Yellowfin
            • Snowflake
            • PASCAL

            5. ADMA Data Day, Melbourne 

            • About the conference: This conference helped its attendees to understand the latest and innovative technologies in the data industry and how it is applied to give a better customer experience. 
            • Event Date: 23 February, 2018 
            • Venue: Crown Promenade, Queensbridge St & Whiteman St, Southbank VIC 3006
            • Days of Program: 1
            • Purpose: The purpose of this conference was to help its attendees develop a better understanding of data-driven marketing, and develop skills and strategies to apply in the real world. 
            • How many speakers: 17
            • Speakers & Profile:
            • Vaughan Chandler - Executive Manager, Red Planet
              • Genevieve Elliott - General Manager of Data Science and Insights, Vicinity Centres
              • Emma Gray - Chief Data Officer, ANZ
              • Karen Giuliani - Head of Marketing, BT Financial Group
              • Everard Hunder - Group GM Marketing and Investor Relations, Monash IVF Group Limited
              • Sam Kline - Data & Analytics Tribe Lead, ANZ
              • Steve Lok - Head of Marketing Tech & Ops, The Economist
              • Ingrid Maes - Director of Loyalty, Data & Direct Media, Woolworths Food Group
              • Patrick McQuaid - General Manager Customer Data & Analytics, NAB
              • Liz Moore - Director of Research, Insights, and Analytics, Telstra
              • Haile Owusu - Chief Data Scientist, Ziff Davis
              • Willem Paling - Director,  Media and Technology, IAG
            • Who were the major sponsors:
              • Adobe
              • DOMO
              • Tealium
              • Sitecore
              • ANZ
              • Cheetah Digital
              • Smart Video
              • siteimprove
              • Rubin 8
              • Engage Australia

              6. The 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'18), Melbourne

              • About the conference: It was an international conference in KDD (Knowledge Discovery and Data mining), that provided a forum for researchers and practitioners to showcase their original research works, and share their ideas related to KDD. 
              • Event Date: 3-6 June, 2018
              • Days of Program: 6
              • Purpose: The purpose of the conference was to enhance development in KDD related areas like machine learning, data mining, artificial intelligence, visualization, data mining, and other related technologies.

              Data Scientist Jobs in Melbourne, Australia

              Here is the logical sequence of steps you should follow to get a job as a Data Scientist.

              1. Getting started: Choose a programming language that you are comfortable with. Most data scientists choose Python or R language. Also, try and understand the terms and responsibilities associated with your job.
              2. Mathematics: A data scientist is presented with large amounts of data which he must then analyse and find patterns in so that the data can be presented properly. So, we’ve compiled a few topics you should focus on, especially with respect to mathematics and statistics:
                1. Descriptive statistics
                2. Probability
                3. Linear algebra
                4. Inferential statistics
              3. Libraries: A data scientist handles a lot of  activities, ranging from data preprocessing to plotting of structured data. He/she must also know how to apply ML algorithms. Some of the famous libraries are:
                • Scikit-learn
                • SciPy
                • NumPy
                • Pandas
                • Ggplot2
                • Matplotlib
              4. Data visualization: Data visualisation is important so that data scientists can make the technical data easier to understand by finding patterns in data. There are various libraries that can be used for this task:
                • Matplotlib - Python
                • Ggplot2 - R
              5. Data preprocessing: There is so much unstructured data and that is why it is important that data scientists preprocess data to make it easier to analyse. Preprocessing is done with feature engineering and variable selection. Preprocessing leaves you with structured data so that an ML tool can then be used for analysis.
              6. ML and Deep learning: ML skills always look good on your CV but you can increase your CV’s weightage by adding deep learning as well since these algorithms are specially designed for heavy data. You should, thus, spend time on topics like CNN, RNN, and neutral networks.
              7. Natural Language processing: Every data scientist should be an  NLP expert to be able to properly process text data and classify it. 
              8. Polishing skills: Competitions like Kaggle etc. provide some of the best platforms to exhibit your data science skills. To add to that, you can continue experimenting with new topics in the field.

              If you are thinking to apply for a data science job in Melbourne, the following steps will increase your chances of success:

              • Study: Cover all relevant and important topics before an interview, including-
                • Probability
                • Statistics
                • Statistical models
                • Machine Learning
                • Understanding of neural networks
              • Meetups and conferences: Data science conferences and tech meetups are great places to start expanding your network. 
              • Competitions: Implement, test and keep polishing your skills by participating in online competitions like Kaggle. 
              • Referral: Recent surveys shows that referrals are usually important to get interviews to data science companies. Keep your LinkedIn profile updated.  
              • Interview: If you feel ready for interviews, go ahead and go for it. If there are questions that you can’t answer, practice those answers for the next time. 

              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:

              • Find relevant data that can be used by the business by going through all the data. This data can be structured or unstructured.
              • Organization and analysis of the data extracted from all the data given
              • Use ML and other programs and tools to make sense of the data given. 
              • Perform statistical analysis for relevant data and predict future outcomes from it.

              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:

              • Type of company
                • Startups: Highest pay 
                • Public: Medium pay 
                • Governmental & Education sector: Lowest pay 

              • Roles and responsibilities
                • Data scientist: AU$100,149/yr
                • Data analyst: AU$69,477/yr
                • Database Administrator: AU$72,676/yr

              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.

              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: 

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

              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 – 

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

              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:

              • Education: The industry for data science is always changing and that is why a data scientist needs to be constantly studying. Also, it’s always beneficial to have a degree and a few certifications.
              • Programming: Python is the preferred programming language of many companies. So, it is helpful if you learn Python Basics before you delve into other data science libraries. 
              • Machine Learning: Make sure that you learn Deep Learning to make your data analysis more effective by finding patterns and relationships in data. Having an ML certificate is also a must. 
              • Projects: Practice with real world projects of your own so you can strengthen your portfolio. 

              Data Science with Python Melbourne, Australia

              • Python is a multi paradigm programming language - The functions that come with the Python language are the most compatible with Data science and related fields. It has many libraries and useful packages and is a structured and object oriented programming language.
              • The inherent simplicity and readability of Python as a programming language makes it very popular. It has many analytical libraries and packages that deal with data science. So, Python stands out as the ideal choice for data scientists.  
              • Python comes with a diverse range of resources that can be used whenever a data scientist gets stuck in some problem.
              • The vast Python community is another big advantage that Python has over other programming languages. If a data scientists ever encounters a problem, they can easily find a solution since the large community is always willing to help. If the problem has been solved earlier, you’ll be able to solve your problem but if it hasn’t, the community can work together to find one.

              Data science is a field which deals with many different libraries which can be used for smooth functioning. Choosing an appropriate language is important:

              • R: It has a steep learning curve but it comes with its advantages:
                • R has high quality open source packages since its a large open source community.
                • It can effectively handle matrix operations and also has loads of statistical functions.
                • R is an immensely effective data visualization tool which uses ggplot3. 
              • Python: Python is becoming increasingly popular even though it has fewer packages when compared to R. 
                • Pandas, scikit-learn, and tensorflow have the most libraries required for data science operations.
                • Easy to learn and implement it.
                • It has a big open-source community as well.
              • SQL: SQL is a structured query language which works upon relational databases.
                • Its syntax is easy to read.
                • Efficient at updating. It is easy to manipulate and look into data in relational databases.
              • Java: Java’s verbosity limits its potential and it also has fewer libraries. Yet, it has many advantages:
                • Compatibility. There are many Java coded systems at the backend and it can be easily integrated into many projects.
                • It is a high-performance, general purpose, and a compiled language.
              • Scala: Scala has a complex syntax and uses JVM to run. However, it is still popular in the data science domain because:
                • Scala’s compatibility with JVM means that it can run on Java as well. 
                • If it is used with Apache Spark, it gives us high--performance cluster computing.

              Follow these steps to successfully install Python 3 on windows:

              • Download and setup: Go to the download page and setup your python on your windows via GUI installer. There is a checkbox below that asks you to add the Python 3.x to PATH, check it. This will allow you to use Python’s functionalities from the terminal. 

              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 --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: 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:

              1. Install xcode: An Apple Xcode package is needed to install brew. Start with the following command and follow through it:

              $ xcode-select --install

              • Install brew: Install Homebrew, a package manager for Apple, using the following 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

              It is also advised that you install virtualenv.

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              Computer Network Architect.
              Attended PMP® Certification workshop in May 2018
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              My special thanks to the trainer for his dedication, learned many things from him. I would also thank for the support team for their patience. It is well-organised, great work Knowledgehut team!

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              Network Systems Administrator
              Attended Certified ScrumMaster®(CSM) workshop in May 2018
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              KnowledgeHut is a great platform for beginners as well as the experienced person who wants to get into a data science job. Trainers are well experienced and we get more detailed ideas and the concepts.

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              Software Developer.
              Attended PMP® Certification workshop in May 2018
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              I am really happy with the trainer because the training session went beyond expectation. Trainer has got in-depth knowledge and excellent communication skills. This training actually made me prepared for my future projects.

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              Prinicipal Consultant
              Attended Agile and Scrum workshop in May 2018
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              I feel Knowledgehut is one of the best training providers. Our trainer was a very knowledgeable person who cleared all our doubts with the best examples. He was kind and cooperative. The courseware was designed excellently covering all aspects. Initially, I just had a basic knowledge of the subject but now I know each and every aspect clearly and got a good job offer as well. Thanks to Knowledgehut.

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              FAQs

              The Course

              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.

              • Get advanced knowledge of data science and how to use them in real life business
              • Understand the statistics and probability of Data science
              • Get an understanding of data collection, data mining and machine learning
              • Learn tools like Python

              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

              • Python
              • MS Excel

              There are no restrictions but participants would benefit if they have basic programming knowledge and familiarity with statistics.

              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. 

              Finance Related

              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.

              KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

              The Remote Experience

              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

              Have More Questions?

              Data Science with Python Certification Course in Melbourne

              KnowledgeHut offers courses in some of the most education-friendly regions of the world, and topping that list is Melbourne-Australia. With its unique blend of modernism woven into the traditional, Melbourne is the leading financial center in Australia. Boasting of a wonderful oceanic climate, it has been voted as the most livable city in the world several times over. People from all over the world call Melbourne their home and it is a melting pot of culture, diversity, and humanity. It is a center of education and several prominent schools and colleges are based here. It also has a diverse economy with thriving industries in finance, manufacturing, research, IT, logistics and transport sectors. Therefore professionals armed with certifications such as PRINCE2, PMP, PMI-ACP, CSM, CEH and practical knowledge of domains such as Big Data, Hadoop, Python, Data Analysis, Android Development do exceptionally well, carving out a niche for themselves. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.