Data Science with Python Training in Sydney, 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

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.

Meet your instructors

Biswanath

Biswanath Banerjee

Trainer

Provide Corporate training on Big Data and Data Science with Python, Machine Learning and Artificial Intelligence (AI) for International and India based Corporates.
Consultant for Spark projects and Machine Learning projects for several clients

View Profile

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

Dubbed as the Sexiest Job of the 21st century by the Harvard review in 2012, the job of data scientist has become the talk of the town. The reason behind this is Data and how it has benefitted leading companies across the world. 

Have you ever wondered how do companies like Amazon and Flipkart are able to recommend you products without you asking? This is because companies like Facebook and Google collect data from users based on their online activities and sell them to ad companies to earn profits.

Sydney is one of the most elite cities in the World. This city is not only technologically advanced but also enjoys a high standard of living. Advanced universities and leading companies are situated in Sydney.

 Here are some other reasons which make Data Scientist such a popular career choice:

  1. More and more companies are shifting towards decision-making driven by data.
  2. There is so much data and not enough well-trained data scientists, making this one of the highest paying jobs in the IT industry.
  3. Data is generated at a high rate. So, the same amount of effort is required to make the analysis. It’s the job of a data scientist to aid in the crucial marketing decisions of the company by gaining insights from the raw data.

Living in Sydney has numerous benefits as it is has many universities famous for data science degree, such as St. Paul’s College, University of Sydney, University of Technology, etc.The top skills that are needed to become a data scientist include the following:

  1. Python Coding: Python is the most commonly used programming language in the Data Science field. It’s because of its simplicity, versatility, ability to take different data formats and aid in the data processing. Also, with python, data scientists can easily create datasets and perform operations on them.
  2. R Programming: R is another popular programming language used by the data scientist. To become a master data scientist, knowledge of an analytical tool is a must to make the data science problem easy to solve.
  3. Hadoop Platform: Hadoop is not a requirement for data science, but since it is used in so many data science projects, it is preferred. 
  4. SQL database and coding: SQL or Structured Query Language is used for accessing, working, and communicating data as well as working on the structure and formation of a database. MySQL is another such language that significantly reduces the time and the level of technical skills required.
  5. Machine Learning and Artificial Intelligence: To be a data scientist, proficiency in machine learning and artificial intelligence is a must. Some of the topics that you need to make yourself familiar with include logistic regression, adversarial learning, reinforcement learning, decision trees, neural networks, machine learning algorithms, etc.
  6. Apache Spark: Apache Spark is a data sharing technology used for big computation. It is quite similar to Hadoop except that it is faster than Apache Spark. This is because Hadoop reads and writes to the disk. Spark, on the other hand, uses system memory to make a cache of its computation. This helps in running data science algorithms faster. Also while dealing with large datasets, it helps in disseminating the data processing as well as handles the complex unstructured dataset. The ease and speed with which it operates help a data scientist carry out the project. Unlike Hadoop, it can prevent data loss.
  7. Data Visualization: Visualization tools like Tableau, matplotlib, d3.js, and ggplot are used by the data scientists to help visualize the data. Once the processed data is converted to obtain a complex result, it is converted into a format easily comprehended and understood. 
  8. Unstructured data: Most of the data that is generated today is in an unstructured form that is unlabelled and cannot be organized into database values. Some of the examples include blogs, audios, videos, customer reviews, social media posts, etc.

The top 5 essential behavioral traits of a top-notch data scientists are:

  • Curiosity – A good data scientist is supposed to be curious. To deal with the huge amount of data one must have an undying hunger for knowledge.
  • Clarity – While writing code or cleaning up data, you must be clear about what you are doing and why you are doing it. You need to keep asking questions to yourself like ‘why’ or ‘so what’.
  • Creativity – The responsibility of data scientists include the development of tools and modeling features, and visualizing data in an innovative way. For this, creativity is required.

  • Skepticism – To keep your creativity, skepticism is also required in a data scientist. A data scientist should not get carried away with creativity and should stay focussed.

Being a data scientist comes with many benefits. These are not only limited to Sydney but to every major city:

  1. High Pay: Since the qualification bar for being a data scientist is set high, the salary of a data scientist is also high. Also, the high demand and low supply issue have made the job of a data scientist as one of the highest paying jobs in the tech world. The average salary for data scientist in Sydney is $100,149/yr.
  2. Good bonuses: When you join a company as a data scientist, you will enjoy many perks like signing bonus, impressive bonuses, and equity shares.
  3. Education: To be a data scientist, you need to have a master's degree or a Ph.D. With such an education, you can also apply to work as a lecturer or researcher in a private or government institution.
  4. Mobility: Since most of the companies that collect data are located in developed countries, a data scientist job will get you a handsome salary and an improved standard of living.
  5. Network: As a data scientist, you will get the opportunity to network with other professionals through tech talks, meetups, conferences, etc. This will help you a lot for referral purposes.

Data Scientist Skills & Qualifications

There are 4 must-have business skills needed to become a successful data scientist irrespective of whether you are in Sydney or London. These skills include:

  1. Analytic Problem-Solving – A good data scientist must be able to understand and analyze the problem. Only after you have a clear perspective of the problem, you will be able to create the right strategies needed to find a solution to the problem.
  2. Communication Skills – One of the key responsibilities of a data scientist is to help companies communicate deep business and customer analytics.
  3. Intellectual Curiosity: If you want to produce great value to your organization, you need to deliver results. For this, you need to be curious and have a thirst for knowledge. If you are not curious to get the answers, this field is not for you. 
  4. Industry Knowledge – This is one of the most important business skills you can have as a data scientist. With solid industry knowledge, you will know what needs attention and what does not. This will help you a lot during your analysis.

If you are looking to brush up your data science skills, you can try one of the following:

  • Boot camps: If you want to brush up your python basics, boot camps are the perfect way to go. Lasting for about 4-5 days, these boot camps offer theoretical knowledge and hands-on experience.
  • MOOC courses: MOOCs are the online courses taught by data science experts that help you implement your skills through assignments and stay up-to-date with the latest trends of the industry.
  • Certifications: With certifications, you would have added a skill to your CV. For data science, you can go for the following certifications:
    • Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
    • Cloudera Certified Associate - Data Analyst
    • Cloudera Certified Professional: CCP Data Engineer
  • Projects: The more projects you work on, more refined your skills and thinking will be. You need to find new solutions to already solved problems while following the project constraints.

  • Competitions: Online competitions like Kaggle help you improve your problem-solving skills. During the competition, you will have to find an optimum solution to a problem with certain restraints and satisfy all the requirements.

Some companies in Sydney collect data for their own use while others do it to sell it to other companies. These companies include Metigy, Opus RS, BCG Digital Ventures, ASIC, Morgan McKinley etc. Overall, the following kind of companies employs Data Scientist:

  • Small-sized companies use tools like Google Analytics for data analysis as they have fewer data and fewer resources to work with.
  • Medium-sized companies need data scientists to apply machine learning techniques to their data.
  • Large companies have a team of data scientists with specializations like ML expert, visualization expert, etc. They also have a huge amount of data to analyze.

If you want to practice your data science skills using datasets, here are some that are categorized according to their difficulty and your expertise level:

  • Beginner Level
    • Iris Data Set: When it comes to pattern recognition, the iris dataset can be an easy and versatile dataset to start with. With this dataset, you will be able to learn different classification techniques. It consists of 4 columns and 50 rows.Practice Problem: The problem is using the given parameters for determining the class of the flower.
    • Loan Prediction Data Set: The banking domain is one of the industries that heavily rely on data analysis and data science methodologies for its working. While working with this dataset, you will get familiar with the concepts used in the banking and the insurance domain like the implementation of strategies, the variables that affect the outcome, the challenges faced, etc. It is a classification problem dataset consisting of 13 columns and 615 rows.
      Practice Problem: Predicting of the bank will approve a certain loan or not.
  • Intermediate Level:
    • Black Friday Data Set: Consisting of sales transactions made in a retail store, the Black Friday Dataset is the best if you want to understand the daily shopping experience of millions of customers and explore and expand your data science skills. It is a regression problem with 12 columns and 550,069 rows.
      Practice Problem: The problem is to predict the total purchase amount.
    • Human Activity Recognition Data Set: This dataset consists of activity information collected from about 30 human subjects. The recordings were collected using smartphones embedded with inertial sensors. This dataset consists of 561 columns and 10,299 rows.
      Practice Problem: The problem is predicting the category of human activity.
  • Advanced Level:
    • Urban Sound Classification: This dataset will help you implement concepts of machine learning to real-world problems. As a developer, you will have to use audio processing for different scenarios of classification. It consists of 8,732 urban sounds categorized into 10 classes.
      Practice Problem: The problem is classifying the type of sound.
    • Vox Celebrity Data Set: Another dataset that includes the use of audio processing is Vox Celebrity Dataset. It is a large scale speaker identification problem that isolates and identifies speech. It contains words spoken by celebrities and extracted from YouTube videos. This data set contains 100,000 words spoken by 1,251 celebrities.
      Practice Problem: The problem is identifying the voice of a celebrity.

How to Become a Data Scientist in Sydney, Australia

The right steps to becoming a top-notch Data scientist are:

  1. Getting started: Select a programming language that you are comfortable working in. Python and R are recommended.
  2. Mathematics and statistics: The major part of Data Science includes dealing with data (textual, numerical or image), and discovering patterns and relationships between them. To do this, you need to have a good grasp over basic algebra and statistics.
  3. Data visualization: This is one of the most important steps in the Data Science learning path. This is done so that the non-technical members can also understand its concepts. It also helps you communicate better with the end users. 
  4. ML and Deep learning: You need to have Machine Learning and Deep Learning skills on your CV as well. It is required for the analysis of the data.

Sydney is home to some of the most recognized universities in the field of Data Science such as St. Paul’s College, University of Sydney, University of Technology, etc and has leading tech companies, such as Microsoft, Mashable, Bitglass, etc. Here are some of the key skills & steps required that will help you start a career as a data scientist.

  1. Degree/certificate: You need to take a basic course that will cover all the fundamentals of Data Science. This can either be an offline or an online course. This will give you a career boost as you will learn to apply the latest cutting-edge tools. The field of data science requires continuous learning due to the rapid advancements in the field.
  2. Unstructured data: When it comes to the main aim of a data scientist, it is safe to say that it is to discover patterns in data. However, most of this data is in an unstructured form that cannot be fit into the database. To structure the data and make it useful, a lot of work is required. It is your job as a data scientist to understand and manipulate the unstructured data. 
  3. Software and Frameworks: While working in the field of data science, you will come across some of the most popular software and framework that will go along with your programming language- preferably R or Python.
    • R is considered a difficult language because of its steep learning curve. However, it is one of the most commonly used programming languages for solving statistical problems. About 43% of data scientists prefer R for data analysis.
    • When the amount of data is very large as compared to the available memory, data scientists use a framework named Hadoop. It is used to convey the data to different parts of the machine. Another popular framework is Spark. It is also used for computational work but it is faster than Hadoop. Unlike Hadoop, it can prevent the loss of data.
    • Once you have mastered the programming language and the framework, you need to get an in-depth knowledge of databases. A data scientist must be able to understand and write SQL queries.
  4. Machine learning and Deep Learning: Once the data has been collected and preprocessed, a data scientist applies algorithms to it for analysis. Through deep learning and machine learning, we train our data science model to work with the data it is provided.
  5. Data visualization: Data visualization is an important part of Data Science. The complex results obtained after analysis of the data is made simple by visualizing it using graphs and charts. There are many tools available for data visualization including ggplot2, matplotlib, etc.

Getting a degree is very important in Data Science. About 88% of Data Scientists have a Master's degree while 46% of them have a Ph.D. Sydney offers a huge opportunity for the aspiring data scientists as it is home to several universities such St. Paul’s College, University of Sydney, University of Technology, etc which provide advanced courses in Data science.

A degree is very important because of the following – 

  • Networking – You will get to make friends and acquaintances while pursuing your degree. This will help you build your network which will be a huge asset in the future.
  • Structured learning – If you can’t do independent learning, getting a degree is a must for you. You will have to keep up with the curriculum and follow a tight schedule that will be beneficial and effective.
  • Internships – During the degree, you will have to get an internship that will provide you with practical hands-on experience.
  • Recognized academic qualifications for your résumé – If you want to get a head start in the race for getting a job as a data scientist, a degree from a prestigious institution will do it for you.

St. Paul’s College, University of Sydney, University of Technology, etc offer advanced degrees in Data science. These institutions are what make Sydney such a great place to be in for an aspiring data scientist. Below is a scorecard that will help you in determining if you should get a Master’s degree or not. If your total is more than 6 points, a Master’s degree is recommended:

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

Knowledge of programming is the most fundamental and important skill required to become a data scientist. Here is why:

  • Data sets: Working in data science involves dealing with huge volumes of datasets. To analyze these datasets, knowledge of programming is essential.
  • Statistics: Knowledge of statistics is a must to analyze the data. But what good would be this knowledge if you don’t have the programming knowledge to implement it? 

  • Framework: If you are proficient in programming, you will be able to build systems and frameworks that can help the organization automatically analyze experiments. This is also required to maintain the data pipeline and visualizing the data.

Data Scientist Salary in Sydney, Australia

The median salary of a Data Scientist in  Sydney is AU$128,798 per year. 

The difference in the annual salary of a Data Scientist in  Sydney and Melbourne is AU$7,598.

The average annual income of data scientist in Sydney is AU$128,798, which is more than the average income of AU$138,702 in Brisbane.

A data scientist working in Sydney earns AU$128,798 every year as opposed to the average annual income of a data scientist working in Melbourne, which is AU$91,140. 

The average income in North Ryde is AU$89,487 which is significantly lower than AU$128,798 earned by a data scientist in Sydney.

In New South Wales, the demand for Data Scientist is increasing by the hour. There are several listings on different portals that prove that Data Scientist is the hottest job right now.

The benefits of being a Data Scientist in Sydney are as follows:

  1. Better income
  2. Opportunity to connect with other data scientists
  3. Chance to gain attention of top executives
  4. Multiple job opportunities
  5. Tremendous job growth

Data Scientist is one of the hottest jobs right now. It is in great demand and has the potential for tremendous job growth. There are some perks and advantages to being a data scientist other than the obvious handsome salary. This includes the freedom to work in their field of choice. All the key organizations in almost every field are entering the world of data science. This gives data scientists, the opportunity to work in any field they like. Also, data scientists get to deal with top-level executives in their enterprise.

Asic, Clayton, Westpac Group and Deloitte are among the companies hiring Data Scientists in Sydney. 

Data Science Conferences in Sydney, Australia

S.NoConference nameDateVenue
1.Data Science Summit30 April, 2019 – 3 May, 2019

Rydges World Square Hotel 389 Pitt Street Sydney, NSW 2000 Australia

2.Data Science Masterclass: Customer Analytics8 May, 2019Coder Academy 118 Walker Street #Level 3 North Sydney, NSW 2060 Australia
3.Data transfer and Research Data Storage (RDS) for HPC
10 May, 2019
ABS Seminar Room 3110 Abercrombie Business School The University of Sydney, NSW 2006 Australia
4.Predictive Analytics, Machine Learning, Data Science and AI - Sydney
3 June, 2019 to 4 June, 2019
Level 4, 60 York Street Sydney, NSW 2000 Australia
5.Introduction to Data Science: Sydney, 26-27 June 2019
26 June, 2019 to 27 June, 2019
Level 4 60 York Street Sydney, NSW 2000 Australia
6.NSW Exploration Data Workshop
7 May, 2019

Saxons Training Facilities - Sydney Level 10, 10 Barrack Street Sydney, NSW 2000 Australia

7.Free Astronomical Data Archives Meeting 2019
05th August, 2019 – 08th August, 2019

105 Delhi Road North Ryde, NSW 2113 Australia

8.Advanced Data Strategy for Software Engineers and Data Scientists
31 May, 2019

WeWork, Branson Room Level 13, 50 Carrington Street Sydney, NSW 2000, Australia

9.YOW! Data 2019
06 May, 2019 - 07 May, 2019

Wesley Conference Centre 220 Pitt Street Sydney, NSW 2000 Australia

10.Data governance for startups
11 June, 2019

Club York (formerly Bowlers Club) York Street Sydney, New South Wales 2000 95-99 York Street Sydney, NSW 2000 Australia

1. Data Science Summit, Sydney

  • About the conference: The Summit will have experts from universities, government agencies and private organizations that will help you explore the skills required to understand the value of data science in your organization.
  • Event Date: 30 April, 2019 – 3 May, 2019
  • Venue: Rydges World Square Hotel 389 Pitt Street Sydney, NSW 2000 Australia
  • Days of Program: 5
  • Timings: 9:00 AM to 4:30 PM EST
  • Purpose: The purpose of the summit is to help you optimize your data streams and uncover estimable insights.
  • Registration cost: $1,534.50 – $4,504.50
  • Who are the major sponsors:  Liquid Learning Group

2. Data Science Masterclass: Customer Analytics, Sydney

  • About the conference: The course will help you answer your most basic questions like What is Big Data? What is Machine Learning and Artificial Intelligence and how can I use it at my workplace?
  • Event Date: 8 May, 2019
  • Venue: Coder Academy 118 Walker Street #Level 3 North Sydney, NSW 2060 Australia
  • Days of Program: 1
  • Timings: 6:00 pm – 9:00 pm AEST
  • Purpose: The purpose of the course is to help you solve problems using data which includes data analysis, predictive modeling and application development.
  • How many speakers: 1
  • Speakers & Profile: Kshira Saagar (Director of Data Science and Analytics)
  • Whom can you Network with in this Conference: You will be able to network with people from the field of Fashion industry with a basic understanding of Python.
  • Registration cost: $116.59
  • Who are the major sponsors: Coder Academy

3. Data transfer and Research Data Storage (RDS) for HPC, Sydney

4. Predictive Analytics, Machine Learning, Data Science and AI, Sydney

  • About the conference: The course has helped managers, entrepreneurs, key stakeholders, and sponsors understand the basics of machine learning and data science. It is basically an introductory course that does not require any previous coding experience.
  • Event Date: 3 June, 2019 to 4 June, 2019
  • Venue: Level 4, 60 York Street Sydney, NSW 2000 Australia 
  • Days of Program: 2
  • Timings: 9:30 AM to 05:00 PM AEST
  • Purpose:  The purpose of the course is helping the attendees get a deep understanding of the skills, central concepts, and common practices used in Machine Learning and Data Science.
  • How many speakers: 1
  • Speakers & Profile: Dr Eugene Dubossarsky
  • Registration cost: $1,920.00 to $2,400.00
  • Who are the major sponsors: AlphaZetta Academy Australia

5. Introduction to Data Science, Sydney

  • About the conference:  This course will help you get started with Data Science and Machine Learning. Main focus will be on the key skills, central concepts covering the foundation of Data Science, and some advanced tools used in the field.
  • Event Date: 26 June, 2019 to 27 June, 2019
  • Venue: City Desktop Level 4 60 York Street Sydney, NSW 2000 Australia
  • Days of Program: 2
  • Timings: 9:30 AM to 5:00 PM AEST
  • Purpose: The purpose of this course is to help the beginners understand the data science and machine learning practices and also get further learning directions.
  • How many speakers: 1
  • Speakers & Profile: Dr Eugene Dubossarsky
  • Registration cost: $2,112 – $2,860
  • Who are the major sponsors: AlphaZetta Academy Australia

6. NSW Exploration Data Workshop, Sydney

  • About the conference: The half-day workshop covers the geological datasets created by the Geological survey of NSW.
  • Event Date: 7 May, 2019
  • Venue: Saxons Training Facilities - Sydney Level 10, 10 Barrack Street Sydney, NSW 2000 Australia
  • Days of Program: 1
  • Timings: 1:30 pm – 5:30 pm AEST
  • Purpose: The purpose of this workshop is to help attendees explore the data and the online system. All the equipment required for the workshop will be provided.
  • Registration cost: Free
  • Who are the major sponsors: Geological Survey of NSW, Department of Planning and Environment

7. Free Astronomical Data Archives Meeting 2019, Sydney

  • About the conference: The conference will include discussions on astronomical data archives, different technologies for data storage and querying. There will be a series of discussions, workshops, and networking sessions regarding Data Science.
  • Event Date: 05th August, 2019 – 08th August, 2019
  • Venue: 105 Delhi Road North Ryde, NSW 2113 Australia
  • Days of Program: 4
  • Timings: 9 AM to 5 PM AEST
  • Purpose: The purpose of the conference is to understand what lies beyond data storage, different user interfaces and all the tools required for the job
  • Registration cost: $250 – $300

8. Advanced Data Strategy for Software Engineers and Data Scientists, Sydney

  • About the conference: The workshop will cover 4 modules that will explains the role of a data team, DataOps and Engineering, North Star metrics, and steering the business.
  • Event Date: 31 May, 2019
  • Venue: WeWork, Branson Room Level 13, 50 Carrington Street Sydney, NSW 2000 Australia
  • Days of Program: 1
  • Timings: 9:00 am – 5:00 pm AEST
  • Purpose: This workshop’s purpose is to use Data Science to emphasize on the use of Data Science in business.
  • How many speakers: 1
  • Speakers & Profile: Tim Garnsey (Data, Machine Learning and AI strategist – Atlassian)
  • Whom can you Network with in this Conference: You will be able to network with software engineers, data and analytic engineers, and data consultants eager to learn the current techniques in Data Science.
  • Registration cost: $561.73 – $766.13
  • Who are the major sponsors: Zambesi 

9. YOW! Data 2019, Sydney

  • About the conference: The two-day conference is primarily focused on covering the current and upcoming technologies in the field of Analytics, Big Data, and Machine Learning. With an impressive lineup of speakers, the conference expects to cover up all the major topics under Data Science.
  • Event Date: 06 May, 2019 -07 May, 2019
  • Venue: Wesley Conference Centre 220 Pitt Street Sydney, NSW 2000 Australia
  • Days of Program: 2
  • Timings: 8 AM – 5:30 PM
  • Purpose: The purpose of the conference is to provide gather, handle, and analyze data using smart solutions. The conference aims to bring researchers and practitioners together and work on data-driven applications and technologies.
  • How many speakers: 44
  • Speakers & Profile: Some of the Speakers include -
    • Agustinus Nalwan (AI & Machine Learning Technical Development Manager -  Carsales.com)
    • Alistair Reid (Senior Research Engineer – Gradient Institute)
    • Ananth Gundabattula (Senior Architect – Commonwealth Bank of Australia)
    • Anthony I Joseph (Chief Technology Officer – My House Geek Pty Ltd)
    • Antoine Desmet (Analytics Manager – Komatsu)
    • Brad Urani (Staff Engineer – Procore)
    • Brendan Hosking (Solutions Engineer – CSIRO)
    • Dana Ma (Senior Software Engineer – Zendesk)
    • Daniel Deng (Senior Data Engineer – AirTasker)
    • Diana Mozo – Anderson (Marketing Science Lead – VGW)
  • Registration cost: $340 – $450

10. Data governance for startups, Sydney

S.NoConference nameDateVenue
1.ADMA Data Day26 February, 2018

Sofitel Sydney Wentworth, 61-101 Phillip St, Sydney NSW 2000

2.Chief Data & Analytics Officer

20 - 22 March, 2018

The Balcony Level Cockle Bay Wharf Darling, Harbour Sydney, NSW 2000 Australia

3.Big Data & AI Leaders Summit
26-27 April, 2018
InterContinental Double Bay, Sydney 
4.Australian Data Summit
19 - 21 November, 2018

Novotel Sydney Central 169-179 Thomas Street Sydney NSW, 2000, Australia

5.Future of Mining, covering also IoT, AI, and Big Data
14-15 May, 2018
279 Castlereagh Street, Sydney, 2000
6.Alteryx Data + Analytics Revolution Summit
29 August, 2018

Primus Hotel, 339, Pitt St, Sydney, NSW 2000, Australia

7.ICML 2017: 34th International Conference on Machine Learning
6-11 August, 2017
International Convention Centre, Sydney
8.Big Data and Analytics for Retail Summit
21-22 September, 2017
161 Elizabeth Street, Sydney, NSW 2000

1. ADMA Data Day, Sydney

  • About the conference: This conference helped its attendees understand the latest and innovative technologies in the data industry. 
  • Event Date: 26 February, 2018
  • Venue: Sofitel Sydney Wentworth, 61-101 Phillip St, Sydney NSW 2000
  • 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

    2. Chief Data & Analytics Officer, Sydney

    • About the conference: It helped its attendees learn the different strategies and technologies used by Chief Data and Analytics Officer to transform their organization and become analytically enhanced.  
    • Event Date: 20 - 22 March 2018
    • Venue: The Balcony Level, Cockle Bay Wharf, Darling Harbour, Sydney NSW 2000 Australia
    • Purpose: The purpose was to connect experts from the field of data, to share their expertise on the innovation, privacy, culture, governance, and leadership required to effectively use data and discover solutions for the challenges faced by the data industry.
    • Speakers & Profile:
      • Abhi Seth - Senior Director, Data Science & Analytics, Honeywell Aerospace USA
      • Emma Gray - Chief Data Officer, ANZ
      • Dr. Anthony Rea - Chief Data Officer, Bureau of Meteorology
      • Gareth Tomlin - General Manager Data and Analytics, Network 10
      • Chris Day - Principal Solution Architect, ANZ, Denodo
      • Glen Rabie - CEO, Yellowfin Business Intelligence
      • Amit Bansal - Managing Director, Analytics Delivery Lead APAC & Artificial Intelligence Delivery Leader, Accenture
      • Simone Roberts - Director, Data Science & Analytics, Optus

      3. Big Data & AI Leaders Summit, Sydney

      • About the conference: It helped its attendees explore the latest and upcoming technologies in global data and identify and discuss new prospects to improve digitalization and business analysis.
      • Event Date: 26-27 April, 2018
      • Venue: InterContinental Double Bay, Sydney
      • Days of Program: 2
      • Purpose: The purpose of this conference was to enhance and strengthen the professional development of its attendees by developing a better understanding of Artificial Intelligence and Data Analytics strategies.
      • How many speakers: 17
      • Speakers & Profile:
        • Eric Charran - Chief Architect, Microsoft
        • David Garvin - Global Head of Quantitative Analysis, Commonwealth Bank
        • Ashok Nair - Head of Data & Analytics, QBE
        • Usman Shahbaz - Head of Data Sciences & Data Analytics, Canon Australia
        • Warwick Graco - Senior Director, Data Science, Australian Taxation Office
        • Scott Thomson - Lead, Customer Solutions, and Innovation, APAC, Google
        • Ric Clarke - Director, Emerging Data and Methods, Australian Bureau of Statistics
        • Tony Gruebner - GM Analytics, Insights and Modeling, Sportsbet
        • Karthik Murugan - Head of Customer Analytics, Capgemini

        4. Australian Data Summit, Sydney

        • About the conference: This conference aimed to optimize business value through experts who share their strategies and insights using case studies.
        • Event Date: 19 - 21 November, 2018
        • Venue: Novotel Sydney Central, 169-179 Thomas Street Sydney NSW 2000, Australia
        • Days of Program: 3
        • Timings: 09:00 AM-06:00 PM
        • Purpose: The purpose of the conference was to connect analytics experts to explore various aspects of data management, through case studies, strategies, and insights.

        5. Future of Mining, covering also IoT, AI, and Big Data, Sydney

        • About the conference: The attendees developed a deep understanding of big data, artificial intelligence, and crowdsourcing and its application in the current world.
        • Event Date: 14-15 May, 2018
        • Venue: 279 Castlereagh Street, Sydney, 2000
        • Days of Program: 2
        • Timings: 8 A.M. to 8 P.M.
        • Purpose: The purpose of the conference was to explore the latest innovations and technologies in data mining and the prospects of AI.
        • Who are the major sponsors:
          • CAT
          • Komatsu
          • FLSmidth
          • Huawei
          • Epiroc
          • Inmarsat

          6. Alteryx Data + Analytics Revolution Summit, Sydney

          • About the conference: This conference brought together analytics leaders from leading organizations to dive deep into future prospects of data analytics.
          • Event Date: 29 August, 2018
          • Venue: Primus Hotel, 339 Pitt St, Sydney NSW 2000, Australia
          • Days of Program: 1
          • Timings: 1:00 PM – 6:00 PM AEST
          • Purpose: The purpose of this conference was to understand and learn about the future of analytics, and the different challenges faced in this field.
          • Speakers & Profile:
            • Ashley Kramer - VP, Product Management, Alteryx
            • Mac Bylra - APAC Technology Evangelist, Tableau
            • Babar Jan-Haleem - Asia-Pacific Head: Big Data Analytics | AI | ML Segment, Amazon Web Services
            • Alan Eldridge - Director of Sales Engineering APAC, Snowflake
            • Fiona Gordon - Executive Manager Customer Insights & Analytics, Commonwealth Bank
            • Annette Slunjski - General Manager, Institute of Analytics Professionals of Australia (IAPA)
            • Chris Choi - General Manager, Strategic Forecasting & Advanced Analytics, Telstra
            • Stephen Wayne - National Practice Director, RXP
          • Who are the major sponsors:
            • Trident
            • Snowflake
            • RXP

            7. ICML 2017: 34th International Conference on Machine Learning, Sydney

            • About the conference: The Conference invited research work in Machine Learning from around the world, and the 6 days event was divided into tutorials, conference sessions, and workshops.
            • Event Date: 6-11 August, 2017
            • Venue: International Convention Centre, Sydney 
            • Days of Program: 6
            • Purpose: The purpose of the conference was to allow the exchange of knowledge through workshops and seminars among the data enthusiasts

            8. Big Data and Analytics for Retail Summit, Sydney

            • About the conference: This conference allowed its attendees to explore the latest tools and platforms used in data analytics, discuss the latest and upcoming trends, and connect with experts in Big Data and Analytics.
            • Event Date: 21-22 September, 2017
            • Venue: 161 Elizabeth Street, Sydney, NSW 2000
            • Days of Program: 2
            • Speakers & Profile:
              • Dr. Amin Beheshti - Head, Data Analytics Research Group, Macquarie University
              • Jaime Noda - Former senior data strategy architect at CBA, Commonwealth Bank
              • Damian Lum - Enterprise Architect, The Hong Kong Jockey Club
              • Dr. Con Menictas - Principal, Strategic Precision
              • Ban Pradhan - Business Intelligence Specialist, Macquarie University
              • Mike Congdon - Head of Enterprise Information Management, New Zealand Post
              • Nicholas Wade - Senior Enterprise Architect - Business, Data & Application Architect, Orica
              • Eric Ling - Technology Partner, Bank of the West
              • Ahmed Saeed - Head of Targeted Data Platforms & Decision Science, FOX Sports Digital
            • Who were the major sponsors:
              • Servian
              • Yellowfin
              • Minitab
              • CrowdReviews

            Data Scientist Jobs in Sydney, Australia

            Here is the logical sequence of steps you need to follow to get a job as a Data Science professional:

            1. Getting started: Firstly, you need to choose a programming language that you are proficient and comfortable working in. Python and R are recommended. Also, you need to understand the roles and responsibilities of a data scientist.
            2. Mathematics: You need to have a good command over mathematics and statistics to analyze the data to decipher patterns and relationships and represent them in a way that is understandable. Here are a few topics that you must have a good grasp on:
              • Inferential statistics
              • Probability
              • Descriptive statistics
              • Linear algebra
            3. Libraries: Libraries are an important part of data science. They aid in several processes include data preprocessing, plotting of processed data, applying machine learning algorithms and creating graphs and charts to visualize the data. Some of the famous libraries are mentioned below:
              • Pandas
              • Scikit-learn
              • Ggplot2
              • SciPy
              • NumPy
              • Matplotlib
            4. Data visualization: It is the job of a data scientist to find patterns in the data and make it simple for the non-technical members of the team. For this, data visualization is used. Graphs and charts are created. The following libraries are used for this:
              • Matplotlib - Python
              • Ggplot2 - R
            5. Data preprocessing: Most of the data that is generated today is in an unstructured form. So, to make this data ready for analysis, data scientists need to preprocess this data. It is performed using feature engineering and variable selection. Once this is done, ML tools are used for analyzing the data.
            6. ML and Deep learning: For analyzing data, machine learning and deep learning skills are a must. Deep learning is preferred while dealing with a huge set of data. You should have a thorough knowledge of topics like RNN, CNN, Neural Networks, etc.
            7. Natural Language processing: Expertise in Natural Language Processing is essential for every data scientist. This includes processing and classification of text form of data.
            8. Polishing skills: You can participate in competitions like Kaggle etc. to polish and exhibit your data science skills. Another way to do this is to take on real-world projects. You can try exploring new solutions to already solved problems as well.

            The 5 important steps to prepare for Data Scientist jobs are:

            • Study: You need to study and cover all the important topics including the following to prepare for an interview -
              • Statistics
              • Statistical models
              • Probability
              • Understanding neural networks
              • Machine Learning
            • Meetups and conferences: You will need referrals for getting the interview. For this, you need to start building your professional network and expand your connections. The best way to do this is through tech conferences and meetups.
            • Competitions: You can try online competitions like Kaggle for implementing, testing, and improving your data science skills.
            • Referral: You need to keep your LinkedIn profile updated to help you with the referrals, which is the primary source of interviews in tech companies.

            • Interview: Once you think you are ready for the interview, go for it. Don’t worry if a couple of interviews don’t go your way. Learn from the questions that you couldn’t answer and study them for the next interview.

            A data scientist is required for analyzing the huge amount of data, discover patterns and relationships and inference information that is required to meet the goals and needs of the business.

            We generate tons of data every day. This data is available in the structured as well as unstructured form. This has made the job of a data scientist all the more important. The data is a goldmine of ideas that can advance the business. It is the job of a data scientist to extract the information in order to benefit the business:

            Data Scientist Roles & Responsibilities:

            • Fetching the relevant data from the huge pile of structured and unstructured data provided by the organization.
            • Next, they organize and analyze the extracted data.
            • Next, machine learning tools, programs, and techniques are created for making sense of the data.
            • Lastly, to predict future outcomes, statistical analysis is performed on the relevant data.

            The base salary of a data scientist is about 36% higher than any other predictive analytics professional. Sydney is home to several leading companies such as Metigy, Opus RS, BCG Digital Ventures, ASIC, Morgan McKinley etc. which offer high salary. The average pay for a Data Scientist in Sydney, New South Wales is AU$100,149 per year.

            A data scientist is one who has a good grasp over mathematics, computer science, and trend spotting. These abilities are required for deciphering large datasets, mining relevant data, and analyzing this data to make future predictions for similar data. 

            Here is how the career path in the field of data science can be explained:

            Business Intelligence Analyst: A Business Intelligence Analyst is responsible for figuring out the business and market trends. They perform the analysis of the data to understand exactly where the business stands in the market.

            Data Mining Engineer: The role of a Data Mining Engineer is to examine the data according to the needs of the business. More often than not, they are hired as a third party by an organization. They are also responsible for creating sophisticated algorithms that are required for data analysis.

            Data Architect: The role of Data Architect is to work with system developers, designers, and users for the creation of blueprints used by data management systems for the integration, centralization, maintenance, and protection of the data sources.

            Data Scientist: A Data Scientist pursues a business case by analyzing, creating hypotheses, developing an understanding of the data, and exploring patterns in this data. They are also responsible for creating systems and algorithms that help in using this data in a productive manner and further the interests of the organization.

            Senior Data Scientist: It is the responsibility of a Senior Data Scientist to anticipate the needs of the business in the future and shape the current projects according to it. They make sure that the analysis and the systems are suited to meet the needs of the business.

            Meeting and networking with other data scientist is very important because it can help you with referrals, which are an effective way of finding a job. Here are some of the ways to network with other data scientists:

            • An online platform like LinkedIn
            • Data science conference
            • Social gatherings like Meetup 

            Sydney is home to several leading companies such as Metigy, Opus RS, BCG Digital Ventures, ASIC, Morgan McKinley etc. which offer high salaries and demand efficiency. 

            Employers prefer data scientists to have mastery of the following:

            • Education: To be a data scientist, you need to have a Master's degree or a Ph.D. You can also try getting some certifications that will improve your CV significantly.
            • Programming: You need to be an expert in programming. Python is the most commonly used programming language in data science projects. So, you need to cover your python basics before moving forward to any data science library.
            • Machine Learning: Once you have prepared the data, you will need deep learning and machine learning skills for data analysis and finding a relationship.

            • Projects: You need to try on real-world projects to learn data science and build your portfolio.

            Data Science with Python Sydney, Australia

            • Python is one of the most used languages in the field of data science. It is an object-oriented language with several packages and libraries that can be used in Data Science. It is a multi-paradigm programming language which means that it has multiple facets that are useful for Data Science purposes.
            • It is preferred by Data Scientists because as compared to other programming languages, Python is inherently simple and readable. It comes along with tailor-made libraries and packages that make it more useful in the field of data science than other programming languages. 
            • With python comes along the diverse range of resources that are easily available to a data scientist. If a data scientist gets stuck at a python program or a data science model, these resources can help them get out of it.
            • Another advantage of using python in data science is the vast python community. There are millions of developers using python that might be facing the same problem as you. So, if you are facing an issue, chances are someone has already faced those issues and found a solution for it. Even if your problem is new, the Python community will help you find a solution for it.

            Choosing a programming language is one of the most important steps in building a data science model because you would need multiple libraries to work together smoothly. Here are the 5 most popular programming languages used in the field of data science:

            • R: This language is a bit difficult to learn but it comes with some advantages making it one of the most commonly used languages in the data science model.
              • The high-quality open source packages provided by the big, open-source community of R.
              • With ggplot2, R can act as a great visualization tool.
              • The language can handle complex matrix operations smoothly and has a lot of statistical function that makes analysis easy.
            • Python: It is one of the most sought after languages used in the field of data science even though it offers fewer packages than R. The reason behind this can be:
              • Python is easy to learn, understand and implement.
              • Most of the libraries that can be required for the purpose of data science can be provided by Pandas, scikit-learn, and tensorflow.
              • Like R, Python has a big, open source community too.
            • SQL: SQL or Structured Query Language is a language that works on relational databases. It offers the following advantages:
              • The syntax of SQL is easy to read and understand.
              • When it comes to relational databases, SQL is efficient in manipulating, querying, and updating.
            • Java: Java is not the most preferred language by data scientists. It verbosity limits its potential and it has fewer libraries for data science. However, it certainly has some advantages:
              • It is a very compatible language since there are so many systems with backend coded in Java, it makes the integration of data science projects coded in Java easier.
              • It is a general purpose, compiled, and high-performance language.
            • Scala: Scala as a complex syntax. Still, due to the following advantages, it is a preferred language in data science domain:
              • SVM runs on JYM, meaning it can run on JAVA as well.
              • You can go for high-performance cluster computing by using Scala with Apache Spark.

            Here are the steps you need to follow to download and install Python 3 on Windows:

            • Download and setup: Go to the download page and use the GUI installer to set up your python on your windows. Make sure that when you are installing python, you select the checkbox that asks you to add Python 3.x to PATH. This path is the classpath that allows the usage of python's functionalities from the terminal.

            You can try using Anaconda to install python as well.

            To check if python is installed on the system, you can try the following command that will show you the version of the language installed:

            python --version

            • Update and install setuptools and pip: For installing and updating 2 of most crucial libraries (3rd party), use the following command:

            python -m pip install -U pip

            Note: For creating isolated python environments and pipenv, python dependency manager, you can install virtaulenv.

            You can either install Python 3 using a .dmg package from their official website or use Homebrew for installing the language and its dependencies. The Homebrew method is recommended. Here is how you do it:

            • Install Xcode: Before you install brew, you need to install the Xcode package of Apple. You need to start with the following command:

            $ xcode-select --install

            • Install brew: Once the Xcode package is installed, you can install Homebrew, a package manager for Apple. To do so, use 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: For installing the latest version of the python, type in the following command:

            brew install python

            • For confirming its version, use: python --version

            If you want to create isolated places where you can run different projects and use different python versions in each one of them, you can try installing virtualenv.

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            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 Sydney

            Among the most versatile and multicultural cities in the world, Sydney is characterized by its clear beaches and warm sunny skies. It?s a city like no other and a constant buzz of excitement surrounds it. Being the most populous city in Australia, it also has a strong economy with a significant concentration of international and national banks and other multinationals. Nearly half of the top 500 Australian companies are headquartered in Sydney, including Woolworths, Westpac, Westfield, and Qantas. Multinationals with a firm base include Cathay Pacific, Boeing, IBM, and Philips. To compensate for the high cost of living, workers are offered higher wages than global averages. Sydney is a great place to start your career and if you have well recognized credentials like PMP, PMI-ACP, CSM, CEH, PRINCE2 and knowledge of hot technologies such as Android and Hadoop, then this is the place to be. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.