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Data Science Training Australia | Online Data Science Course Australia

Data Science Online Training Australia Live Classes

  (150 Reviews)
      1100 Registered Users

THE SMARTEST WAY TO Learn Data Science Online Training in Australia
The abilities individuals and businesses need to succeed are evolving. Regardless of where you are in your career or what field you work in, you should comprehend the language of information. With Nearlearn, you learn data science today and apply it tomorrow.
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Nearlearn lessons are reduced down so you can learn such that accommodates your schedule, on any gadget. Tracks advantageously order the courses so you can discover what meets your requirements initially.

Data Science Online Training in Australia
$650
$580

(0% Interest on EMI)

To extract information from any type of structured and non-structural data, few algorithms, methods, procedures, and scientific systems are used. These inter-disciplinary fields altogether are known as Data Science. The present world is moving towards the digital platform and requires a fast-paced action to set their every demand. Data science combines three myriad technological developments, i.e. data mining, big data, and machine learning to serve the industry more efficiently. Today’s professional world is offering the highest demand for professionals with Data science Training Certification in Australia with an excellent pay scale. To get skilled with the deep knowledge of data science with an accredited certification, NearLearn is the Data Science Training Institute in Australia with top professionals and excellently crafted syllabus to educate aspirants from the basic level. Our Data Science Online Training in Australia is renowned for maintaining high education standards with the highest numbers of job placements.

• Our Data Science Course in Australia is compiled by the experts of the industry and it includes every single detail of the course starting from the most basic information till the latest feature update.
• Our sessions are delivered by the professionals with an experience of several years to give you deep knowledge about the real work scenarios.
• Real-time projects are the key to the success of our aspirants that they perform during their Data science Training in Australia

The professional world is extending a great career opportunity for the data science sector, but it is a highly competitive world where only skills are hired. Experts of NearLearn groom your skills as a professional of data science with the edge to edge knowledge of the relevant sector. The standard of industry undergoes a huge fluctuation and our data science experts to educate you with the past, existing and the upcoming skills of data science to boost your confidence in achieving your dream job. NearLearn offers an accredited Data science Training Course Certification in Australia to open the door of job opportunities for you all across the globe. Our experts not only provide Best Data science Training in Australia but also educate you on the tactics to face interview with a master hand on complete data science technology.

Data science is emerging as one of the most chosen courses in Australia and other parts of the world because of the extensive number of job opportunities it is offering. Those who all come across the data science course training in Australia must first know who should take this course; here is the answer to your question:

• Professionals with basic mathematical and analytical skills
• Aspirants working on business intelligence
• Skilled with data warehousing and reporting tools
• Knowledge of software programming
• Business analysts
• Fresher with a will to learn all the above skills

To take data science training in Australia, a candidate must possess certain qualities, they are listed below:

• Software development skills
• Database knowledge
• Machine learning knowledge
• Mathematics
• Statistics
• Data visualization

COURSES CURRICULUM

Lecture1.1 Key Elements of Machine Learning & Data Science & differences between them

Lecture1.2 Data Warehousing

Lecture1.3 Business Intelligence

Lecture1.4 Data Visualization

Lecture1.5 Data Mining

Lecture1.6 Machine Learning

Lecture1.7 Artificial Intelligence

Lecture1.8 Cloud Computing

Lecture1.9 Big Data

Lecture2.1 What is Machine Learning (ML)?

Lecture2.2 How machines learn

Lecture2.3 Basics of Classification, Regression and Clustering algorithms

Lecture2.4 Creating your first Prediction Model

Lecture2.5 Training & Model Evaluation

Lecture2.6 Choosing Machine Learning Algorithm

Lecture3.1 Operators, Operands and Expressions

Lecture3.2 Python Data Types

Lecture3.3 Conditional statements in Python

Lecture3.4 Loops in Python

Lecture3.5 Lists and dictionaries and Tuples

Lecture3.6 Programming practice in Python

Lecture3.7 Iterators & Generators

Lecture3.8 File Handling in Python

Lecture3.9 Modules and Libraries

Lecture3.10 Classes and Objects

Lecture3.11 String Formatting in Python

Lecture3.12 Decorators, Context Managers, Regular Expressions

Lecture3.13 List and Dictionary Comprehensions

Lecture3.14 Lambda and Argument Passing

Lecture3.15 Multiple Inheritance

Lecture4.1 Linear Algebra (Vectors, Matrix, Eigen Values)

Lecture4.2 Probability and Statistics

Lecture4.3 Hypothesis testing

Lecture4.4 Optimization

Lecture5.1 Introduction to Numpy

Lecture5.2 Arrays, Matrices,

Lecture5.3 Various operations on arrays and matrices

Lecture5.4 Introduction to Pandas

Lecture5.5 Reading csv and matlab files

Lecture5.6 Data frame object manipulation in python

Lecture5.7 Various operations on data frame

Lecture5.8 Visualization using Matplotlib

Lecture5.9 Scatter plots, line plots etc on a given data

Lecture5.10 Advance visualization using Seaborn

Lecture5.11 Histograms, heatmaps, box plots etc using seaborn

Lecture6.1 Basic Functionalities of a data object

Lecture6.2 Merging of Data objects

Lecture6.3 Concatenation of data objects

Lecture6.4 Types of Joins on data objects

Lecture6.5 Exploring a Dataset

Lecture6.6 Analysing a dataset

Lecture6.7 Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples()

Lecture6.8 GroupBy operations

Lecture6.9 Aggregation

Lecture6.10 Concatenation

Lecture6.11 Merging

Lecture6.12 Joining

Lecture6.13 Data Collection &Preparation

Lecture6.14 Data Mugging

Lecture6.15 Outlier Analysis

Lecture6.16 Missing value treatment

Lecture6.17 Feature Engineering

Lecture6.18 Data Transformation

Lecture6.19 Normalization vs Standardization

Lecture6.20 Creating Dummies

Lecture6.21 Dimensionality Reduction

Lecture6.22 Principal ComponentAnalysis

Lecture7.1 Confidence Interval

Lecture7.2 Student’s t distribution

Lecture7.3 Binomial Distribution

Lecture7.4 A/B Testing

Lecture7.5 Hypothesis Testing

Lecture7.6 t-Tests

Lecture7.7 ANOVA

Lecture7.8 Chi-square test

Lecture7.9 KNN

Lecture7.10 PCA

Lecture7.11 Categorical Variables

Lecture7.12 R Square

Lecture8.1 Linear & Logistic and Regression Techniques

Lecture8.2 Problem of Collinearity

Lecture8.3 WOE and IV

Lecture8.4 Residual Analysis

Lecture8.5 Heteroscedasticity

Lecture8.6 Homoscedasticity

Lecture9.1 Supervised Machine Learning algorithms

Lecture9.2 Linear Regression

Lecture9.3 Multi Feature

Lecture9.4 Logistic Regression

Lecture9.5 2 Class and Multi class

Lecture9.6 Decision/ Classification

Lecture9.7 Trees

Lecture9.8 Ensemble

Lecture9.9 Models

Lecture9.10 Bagging

Lecture9.11 Boosting

Lecture9.12 Random Forest

Lecture9.13 K-Nearest Neighbours (KNN)

Lecture9.14 Naive Bayes

Lecture9.15 Introduction to Neural Network (DeepLearning)

Lecture9.16 Feed Forward Neural Network

Lecture9.17 Forward Propagation

Lecture9.18 Backward Propagation

Lecture9.19 Support Vector Machine

Lecture9.20 Unsupervised Machine Learning algorithms

Lecture9.21 Clustering with K-means Clustering

Lecture9.22 Bias-Variance Tradeof

Lecture9.23 Regularization

Lecture9.24 Parameter tuning & grid search optimization

5000+ Handwritten Digit Recognition Problem

4000+ email spam detection problem

Image compression Problem

Flower species classification problem

Titanic Survivor classification problem from kaggle

Fifa ranking dataset from kaggle

Profit Prediction Problem

Business Case of whether a chip will be accepted or not

Business case of clustering from dataset

Wine classification dataset and problem from Kaggle

Variety of Problems from Kaggle Competition Data Sets

Reviews

studant

Shubham

  (2 Review)

I have enrolled for NearLearn Machine Learning with Python Course Training. The Session was very Nice. The trainer is knowledgeable and his skills of explaining the concepts are awesome.

studant

Sneha Deshpande

  (2 Review)

I am satisfied with the Machine Learning workshop coducted by NearLearn. The instructor explained course objectives clearly. Made class enjoyable as well as educational.

studant

Rudranee

  (2 Review)

Machine Learning Training session was good.I am satisfied about this course.The training experience will be useful in my work. Course explanation was very clear and understandable.

studant

Ketan

  (2 Review)

The session was very enjoyable as well as educational. This training experience will be very useful in my work. I would really recommend this instructor. Thanks

FAQs

Data science is the mining of information by penetrating inside data, market trends, and interpretations using the latest technologies. Data science is a vast sector that includes several scientific technologies and tactics, such as data engineering, hacking mindset, the expertise of various domains, mathematics, statistics, advanced computing, and data visualization. Data scientists are enrolled by various businesses to deeply understand the market trends, predict it, and use it for business development.

Today’s world is completely digitized where businesses are making huge room for data storage and use. When it comes to making decisions based on available data and predictions based on it, businesses seek a data scientist who can make accurate calculations and help them with the prompt decision. Hence, business industries are offering several job opportunities for certified professionals. Those who are willing to build an exponentially developing career with an excellent pay scale must opt for top Data science Training in Australia and become a data scientist.

This course is specially designed for the aspirants who are willing to take remote classes being far from the institute. Hence, NearLearn offers an online training session for most of the curriculum and classroom classes for those sessions which requires an expert’s assistance to perform; this especially includes real-time project assessments.

Most of the students struggle with the study material as they hardly get a described one anywhere. Grabbing every sentence from the lecture is not possible every time and we clearly understand that. Hence, we provide a properly structured study material compiled by the experts of the industry with comprehensive information shared with you during your sessions. Another benefit of taking data science course from NearLearn is that we have our every session recorded, so if you miss any of your online session then you can go through the recordings any time you are available. We have structured our training course for your comfort with intentions to educate you with every single detail of data science and develop your skill as a professional data scientist.

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  • Data Science Training - Instructor LED Online Classes
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