Data Science Training/Course 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
10000+
20+
50+
25+

Data Science Jobs in Auckland

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 Auckland, chennai and europe countries. You can find many jobs for freshers related to the job positions in Auckland.

  • 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 Auckland
Data Science To find trends and patterns, use algorithms and modules. 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 person who uses a variety of procedures, methods, systems, and algorithms to analyze data to provide actionable insights. There are numerous reasons why you should take this course. . This finest Data Science course was built with the needs of businesses in mind when it comes to the field of Data Science. Effectively analyze both organized and unstructured data Create strategies to address company issues. The Data Science Process, Communicating with Stakeholders, Software Engineering Practices, Object-Oriented Programming, Web Development, ETL Pipelines, Natural Language Processing, Machine Learning Pipelines, Experiment Design, Statistical Concerns of Experimentation, A/B Testing, and Introduction to Recommendation Engines are some of the topics covered in. You'll have a personal mentor who will keep track of your development. Data Science provides a diverse set of tools for analyzing data from a range of sources, including financial records, multimedia files, marketing forms, sensors, and text files.

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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 Auckland

  • Unitec,MtAlbertCampus | Location details: 139 Carrington Road, Mount Albert, Auckland 1025, New Zealand | Classification: Educational institution, Educational institution | Visit Online: unitec.ac.nz | Contact Number (Helpline): +64 9 815 4321
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  • TasmanInternationalAcademies | Location details: level 7/290 Queen Street, Auckland CBD, Auckland 1010, New Zealand | Classification: School, School | Visit Online: tasman.ac.nz | Contact Number (Helpline): +64 9 379 3468
  • PerformanceLab | Location details: 19 Byron Avenue, Takapuna, Auckland 0622, New Zealand | Classification: Software company, Software company | Visit Online: | Contact Number (Helpline): +64 9 480 1422
  • LearnPlus | Location details: 19 Como Street, Takapuna, Auckland 0622, New Zealand | Classification: Training centre, Training centre | Visit Online: | Contact Number (Helpline): +64 800 322 7333
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  • CADPROSystemsNewZealandLtd | Location details: 527B Rosebank Road, Avondale, Auckland 1026, New Zealand | Classification: Software company, Software company | Visit Online: cadpro.co.nz | Contact Number (Helpline): +64 9 302 4028
  • TheInductionCompany | Location details: 5 Dockside Lane, Auckland CBD, Auckland 1010, New Zealand | Classification: Software company, Software company | Visit Online: inductionapp.co | Contact Number (Helpline): +64 9 520 5810
  • ComputerRepairsNZ | Location details: 43A Hyde Road, Rothesay Bay, Auckland 0630, New Zealand | Classification: Computer service, Computer service | Visit Online: computerrepairsnz.co.nz | Contact Number (Helpline): +64 21 263 3405
  • ComputerCoaching | Location details: Kinetics Group Ltd, Ground Floor, Building 3, Central Park 666 Great South Road, Penrose, Auckland 1061, New Zealand | Classification: Computer training school, Computer training school | Visit Online: computercoaching.co.nz | Contact Number (Helpline): +64 9 379 8200
  • AGIEducationLimited. | Location details: Level 6 & 7/3 City Road, Grafton, Auckland 1010, New Zealand | Classification: Educational institution, Educational institution | Visit Online: agi.ac.nz | Contact Number (Helpline): +64 9 379 6628
  • SAPNewZealandLtd | Location details: 151 Queen Street, Auckland CBD, Auckland 1010, New Zealand | Classification: Software company, Software company | Visit Online: sap.com | Contact Number (Helpline): +64 9 355 5800
  • SimPRO®SoftwareLimited | Location details: L2, B3, 61 Constellation Drive, Rosedale, Auckland 0632, New Zealand | Classification: Software company, Software company | Visit Online: simprogroup.com | Contact Number (Helpline): +64 800 100 854
  • Tradify | Location details: 81 Union Street, Auckland CBD, Auckland 1010, New Zealand | Classification: Software company, Software company | Visit Online: tradifyhq.com | Contact Number (Helpline): +61 1800 325 674
  • SEBDATA | Location details: 149 Parnell Road, Parnell, Auckland 1052, New Zealand | Classification: Software company, Software company | Visit Online: sebdata.com | Contact Number (Helpline): +64 9 366 0789
  • ThinkConceptsAuckland | Location details: Level 1/29 East Street, Newton, Auckland 1010, New Zealand | Classification: Computer support and services, Computer support and services | Visit Online: thinking.co.nz | Contact Number (Helpline): +64 9 379 5557
 courses in Auckland
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