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
10000+
20+
50+
25+

Data Science Jobs in Wellington

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

  • 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 Wellington
Data Science To succeed as a data scientist, you must, nevertheless, make a particular effort to apply soft skills. Effectively analyze both organized and unstructured data Create strategies to address company issues. This finest Data Science course was built with the needs of businesses in mind when it comes to the field of Data Science. The top Data Science course online for professionals who wish to expand their knowledge base and start a career in this industry is NESTSOFT in Wellington. 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 Wellington. You'll have a personal mentor who will keep track of your development. Today's Data Scientists must possess a wide range of abilities, including the ability to work with large amounts of data, parse that data, and translate it into an easily comprehensible format from which business insights may be drawn. Experts provide immersive online instructor-led seminars. This curriculum prepares you to work in a variety of Data Science professions and earn top-dollar wages. 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.

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 Wellington

  • TaylorMasonTraining&DevelopmentLtd | Location details: West One, 114 Wellington St, Leeds LS1 1BA, United Kingdom | Classification: Training consultant, Training consultant | Visit Online: taylor-mason.co.uk | Contact Number (Helpline): +44 1494 429342
  • MinistryOfForeignAffairs&Trade | Location details: 195 Lambton Quay, Wellington Central, Wellington 6011, New Zealand | Classification: Government office, Government office | Visit Online: mfat.govt.nz | Contact Number (Helpline): +64 4 439 8000
  • NZ-DEALSWellington | Location details: 142 Victoria Street, Te Aro, Wellington 6011, New Zealand | Classification: Cosmetics store, Cosmetics store | Visit Online: nz-deals.co.nz | Contact Number (Helpline): +64 4 971 7445
  • NewZealandMerchantServiceGuild | Location details: Orbit Systems Tower Level 6/94 Dixon Street, Te Aro, Wellington 6011, New Zealand | Classification: Labor union, Labor union | Visit Online: nzmsg.co.nz | Contact Number (Helpline): +64 4 382 9131
  • IHCIncorporated(NationalOffice) | Location details: 57 Willis Street, Wellington Central, Wellington 6011, New Zealand | Classification: Disability services & support organisation, Disability services & support organisation | Visit Online: ihc.org.nz | Contact Number (Helpline): +64 800 442 442
  • Stantec | Location details: Level 15/10 Brandon Street, Wellington Central, Wellington 6011, New Zealand | Classification: Engineering consultant, Engineering consultant | Visit Online: stantec.com | Contact Number (Helpline): +64 4 381 6700
  • InterpretingNewZealand | Location details: Level 1/72 Abel Smith Street, Te Aro, Wellington 6011, New Zealand | Classification: Non-profit organization, Non-profit organization | Visit Online: interpret.org.nz | Contact Number (Helpline): +64 4 384 2849
  • CapitalWatchServices | Location details: 100 Willis Street, Wellington Central, Wellington 6011, New Zealand | Classification: Watch store, Watch store | Visit Online: capitalwatchservices.co.nz | Contact Number (Helpline): +64 4 472 9171
  • NewZealandPassportOfficeWellington-DepartmentOfInternalAffairs | Location details: Level 2/7 Waterloo Quay, Pipitea, Wellington 5010, New Zealand | Classification: Passport office, Passport office | Visit Online: passports.govt.nz | Contact Number (Helpline): +64 4 463 9360
  • CMOS-CleanMyOfficeSpace | Location details: 16 Kaiwharawhara Road, Kaiwharawhara, Wellington 6035, New Zealand | Classification: Commercial cleaning service, Commercial cleaning service | Visit Online: cmos.co.nz | Contact Number (Helpline): +64 800 002 557
  • AIWT | Location details: Ground Floor, 823 Wellington St, West Perth WA 6005, Australia | Classification: Vocational college, Vocational college | Visit Online: aiwt.edu.au | Contact Number (Helpline): +61 8 9249 9688
  • HighCommissionOfIndia | Location details: 72 Pipitea Street, Thorndon, Wellington 6011, New Zealand | Classification: Foreign consulate, Foreign consulate | Visit Online: hciwellington.gov.in | Contact Number (Helpline): +64 4 473 6390
  • SkiNewZealandHolidayAccommodation-Wellington | Location details: 181 Victoria Street, Te Aro, Wellington 6011, New Zealand | Classification: Travel services, Travel services | Visit Online: | Contact Number (Helpline): +64 3 363 3003
  • BenefitRightsService | Location details: 138 Wakefield Street, Te Aro, Wellington 6011, New Zealand | Classification: Insurance agency, Insurance agency | Visit Online: cab.org.nz | Contact Number (Helpline): +64 4 210 2012
  • DepartmentForContinuingEducation,UniversityOfOxford | Location details: 1 Wellington Square, Oxford OX1 2JA, United Kingdom | Classification: University department, University department | Visit Online: conted.ox.ac.uk | Contact Number (Helpline): +44 1865 270360
  • NewZealandOilServices | Location details: Level 3/139 The Terrace, Wellington Central, Wellington 6011, New Zealand | Classification: Corporate office, Corporate office | Visit Online: nzosl.co.nz | Contact Number (Helpline): +64 4 495 4500
 courses in Wellington
It is the heart of New Zealand's film and special effects industries, and is increasingly becoming a hub for information technology and innovation, with two public research universities. Massey University has a campus in Wellington called the "Creative Campus" and offers courses in Media and Business, Engineering and Technology, Health and Wellness, and the Creative Arts. Wellington is one of the leading financial centers in the Asia-Pacific region, ranked 35th in the world by the Global Financial Centers Index 2021. The capital is also home to New Zealand's highest court, the Supreme Court, and the historic Supreme Court building (opened in 1881) has been expanded and restored to use. It enrolled 21,380 students in 2008; of these, 16,609 are full-time students. From 2017 to 2018, Deutsche Bank ranked it first in the world for livability and zero pollution. 5,751 degrees, diplomas and certificates awarded. Wellington's metropolitan area, consisting only of urban areas City of Wellington, having a population of 212,000 in June 2022. 6 million. As one of the world's most livable cities, the 2021 World Liveability Rankings tied Wellington with Tokyo in fourth place in the world.

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