Data Science Training by Experts

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Our Training Process

Data Science - Syllabus, Fees & Duration

MODULE 1

  • The Data Science Process
  • Apply the CRISP-DM process to business applications
  • Wrangle, explore, and analyze a dataset
  • Apply machine learning for prediction
  • Apply statistics for descriptive and inferential understanding
  • Draw conclusions that motivate others to act on your results

MODULE 2

  • Communicating with Stakeholders
  • Implement best practices in sharing your code and written summaries
  • Learn what makes a great data science blog
  • Learn how to create your ideas with the data science community

MODULE 3

  • Software Engineering Practices
  • Write clean, modular, and well-documented code
  • Refactor code for efficiency
  • Create unit tests to test programs
  • Write useful programs in multiple scripts
  • Track actions and results of processes with logging
  • Conduct and receive code reviews

MODULE 4

  • Object Oriented Programming
  • Understand when to use object oriented programming
  • Build and use classes
  • Understand magic methods
  • Write programs that include multiple classes, and follow good code structure
  • Learn how large, modular Python packages, such as pandas and scikit-learn, use object oriented programming
  • Portfolio Exercise: Build your own Python package

MODULE 5

  • Web Development
  • Learn about the components of a web app
  • Build a web application that uses Flask, Plotly, and the Bootstrap framework
  • Portfolio Exercise: Build a data dashboard using a dataset of your choice and deploy it to a web application

MODULE 6

  • ETL Pipelines
  • Understand what ETL pipelines are
  • Access and combine data from CSV, JSON, logs, APIs, and databases
  • Standardize encodings and columns
  • Normalize data and create dummy variables
  • Handle outliers, missing values, and duplicated data
  • Engineer new features by running calculations • Build a SQLite database to store cleaned data

MODULE 7

  • Natural Language Processing
  • Prepare text data for analysis with tokenization, lemmatization, and removing stop words
  • Use scikit-learn to transform and vectorize text data
  • Build features with bag of words and tf-idf
  • Extract features with tools such as named entity recognition and part of speech tagging
  • Build an NLP model to perform sentiment analysis

MODULE 8

  • Machine Learning Pipelines
  • Understand the advantages of using machine learning pipelines to streamline the data preparation and modeling process
  • Chain data transformations and an estimator with scikit- learn’s Pipeline
  • Use feature unions to perform steps in parallel and create more complex workflows
  • Grid search over pipeline to optimize parameters for entire workflow
  • Complete a case study to build a full machine learning pipeline that prepares data and creates a model for a dataset

MODULE 9

  • Experiment Design
  • Understand how to set up an experiment, and the ideas associated with experiments vs. observational studies
  • Defining control and test conditions
  • Choosing control and testing groups

MODULE 10

  • Statistical Concerns of Experimentation
  • Applications of statistics in the real world
  • Establishing key metrics
  • SMART experiments: Specific, Measurable, Actionable, Realistic, Timely

MODULE 11

  • A/B Testing
  • How it works and its limitations
  • Sources of Bias: Novelty and Recency Effects
  • Multiple Comparison Techniques (FDR, Bonferroni, Tukey)
  • Portfolio Exercise: Using a technical screener from Starbucks to analyze the results of an experiment and write up your findings

MODULE 12

  • Introduction to Recommendation Engines
  • Distinguish between common techniques for creating recommendation engines including knowledge based, content based, and collaborative filtering based methods.
  • Implement each of these techniques in python.
  • List business goals associated with recommendation engines, and be able to recognize which of these goals are most easily met with existing recommendation techniques.

MODULE 13

  • Matrix Factorization for Recommendations
  • Understand the pitfalls of traditional methods and pitfalls of measuring the influence of recommendation engines under traditional regression and classification techniques.
  • Create recommendation engines using matrix factorization and FunkSVD
  • Interpret the results of matrix factorization to better understand latent features of customer data
  • Determine common pitfalls of recommendation engines like the cold start problem and difficulties associated with usual tactics for assessing the effectiveness of recommendation engines using usual techniques, and potential solutions.

Download Syllabus - Data Science
Course Fees
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Data Science Jobs in Melbourne

Enjoy the demand

Find jobs related to Data Science in search engines (Google, Bing, Yahoo) and recruitment websites (monsterindia, placementindia, naukri, jobsNEAR.in, indeed.co.in, shine.com etc.) based in Melbourne, chennai and europe countries. You can find many jobs for freshers related to the job positions in Melbourne.

  • Data Scientist
  • Data Analyst
  • Data Engineer
  • Data Storyteller
  • Machine Learning Scientist
  • Machine Learning Engineer
  • Business Intelligence Developer
  • Database Administrator
  • ML Engineer
  • Computer Vision Engineer

Data Science Internship/Course Details

Data Science internship jobs in Melbourne
Data Science To find trends and patterns, use algorithms and modules. This finest Data Science course was built with the needs of businesses in mind when it comes to the field of Data Science. This curriculum prepares you to work in a variety of Data Science professions and earn top-dollar wages. To succeed as a data scientist, you must, nevertheless, make a particular effort to apply soft skills. Exercises, tasks, and projects that are completed in real-time 24 hours a day, 7 days a week, A large network of like-minded newbies, an industry-recognized intellipaat credential, and individualized employment support Several data scientist responsibilities are listed below. A Data Scientist is a highly skilled someone with advanced mathematical, statistical, scientific, analytical, and technical abilities who can prepare, clean, and validate organized and unstructured data for industries to utilize in making better decisions. Create data strategies with the help of team members and leaders. Experts provide immersive online instructor-led seminars. Effectively analyze both organized and unstructured data Create strategies to address company issues. You may learn all of the skills and talents required to become a data scientist by enrolling in the top data science online courses in Melbourne.

List of All Courses & Internship by TechnoMaster

Success Stories

The enviable salary packages and track record of our previous students are the proof of our excellence. Please go through our students' reviews about our training methods and faculty and compare it to the recorded video classes that most of the other institutes offer. See for yourself how TechnoMaster is truly unique.

List of Training Institutes / Companies in Melbourne

  • TheCareerAcademyAustralia | Location details: Level 21/459 Collins St, Melbourne VIC 3000, Australia | Classification: Education center, Education center | Visit Online: thecareeracademy.com.au | Contact Number (Helpline): +61 1800 837 550
  • SamvitSoftwareSolutions | Location details: 530 Little Collins St, Melbourne VIC 3000, Australia | Classification: Software company, Software company | Visit Online: samvit.com.au | Contact Number (Helpline): +61 478 898 712
  • DVSConsulting&Services | Location details: 50 Queen St, Melbourne VIC 3000, Australia | Classification: Website designer, Website designer | Visit Online: | Contact Number (Helpline): +61 490 802 370
  • AuscanusInstituteOfAustralia | Location details: Level 6/55 Swanston St, Melbourne VIC 3000, Australia | Classification: College, College | Visit Online: auscanus.edu.au | Contact Number (Helpline): +61 3 9945 8247
  • LeWagonMelbourne | Location details: 41 Stewart St, Melbourne c/o Inspire9, Level1, Richmond VIC 3121, Australia | Classification: Educational institution, Educational institution | Visit Online: lewagon.com | Contact Number (Helpline): +61 423 967 255
  • AustralianInstituteOfEntrepreneurship(AIE) | Location details: level 11/474 Flinders St, Melbourne VIC 3000, Australia | Classification: Educational institution, Educational institution | Visit Online: aiemel.edu.au | Contact Number (Helpline): +61 3 9428 9570
  • UniversityOfMelbourne | Location details: Parkville VIC 3010, Australia | Classification: University, University | Visit Online: unimelb.edu.au | Contact Number (Helpline): +61 3 9035 5511
  • PMPCertificationTrainingMelbourne-Sprintzeal | Location details: 727 Collins Street Collins Square Tower Five, Level 23, Melbourne VIC 3008, Australia | Classification: Education center, Education center | Visit Online: sprintzeal.com | Contact Number (Helpline): +61 2 4039 8333
  • WeAreWaypoint | Location details: Level 4/152 Elizabeth St, Melbourne VIC 3000, Australia | Classification: Software company, Software company | Visit Online: wearewaypoint.com | Contact Number (Helpline): +61 3 9008 5950
  • SAEInstituteMelbourne | Location details: 235 Normanby Rd, South Melbourne VIC 3205, Australia | Classification: College, College | Visit Online: sae.edu.au | Contact Number (Helpline): +61 3 8632 3400
  • DynamicWebTraining | Location details: level 12/379 Collins St, Melbourne VIC 3000, Australia | Classification: Software training institute, Software training institute | Visit Online: dynamicwebtraining.com.au | Contact Number (Helpline): +61 1300 888 724
  • GlenInstitute | Location details: Level 5/310 King St, Melbourne VIC 3000, Australia | Classification: Educational institution, Educational institution | Visit Online: glen.edu.au | Contact Number (Helpline): +61 1300 003 990
  • OceanSoftware | Location details: 120 Collins St, Melbourne VIC 3004, Australia | Classification: , | Visit Online: ocean.software | Contact Number (Helpline):
  • IndustryConnect | Location details: 385 Bourke St, Melbourne VIC 3000, Australia | Classification: Software training institute, Software training institute | Visit Online: industryconnect.org | Contact Number (Helpline): +61 1300 508 817
  • Altora | Location details: Level 19/15 William St, Melbourne VIC 3000, Australia | Classification: Software company, Software company | Visit Online: altora.com.au | Contact Number (Helpline): +61 3 9329 7130
  • Prince2Training | Location details: 8/350 Collins St, Melbourne VIC 3000, Australia | Classification: Training centre, Training centre | Visit Online: prince2training.com.au | Contact Number (Helpline): +61 1300 793 334
 courses in Melbourne
There are courses that are can first take when entering university. . au/ courses-and-studying/how-to-find-a-course. au/international-students Deakin University deakin. edu. Federation University federation. A true university city, Melbourne is home to around , of the world's best universities, combining , academic excellence, state-of-the-art facilities and a vibrant , student life. identifies with more than , ,270 ancestors. Carbon Nexus The Carbon Nexus is a purpose-built research facility, designed to meet the diverse needs of international manufacturing organizations, requiring cost-effective carbon tracking. Some institutions offer both vocational education (VET) and higher education courses, allowing students to transfer credits to the second course.

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