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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
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Data Science Jobs in Porirua

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

  • 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 Porirua
Data Science Effectively analyze both organized and unstructured data Create strategies to address company issues. This curriculum prepares you to work in a variety of Data Science professions and earn top-dollar wages. 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. Identify and collect data from data sources. There are numerous reasons why you should take this course. You'll have a personal mentor who will keep track of your development. 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. This finest Data Science course was built with the needs of businesses in mind when it comes to the field of Data Science. 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 Porirua. Create data strategies with the help of team members and leaders.

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 Porirua

  • PresbyterianSupportCentral | Location details: 1 Prosser Street, Elsdon, Porirua 5022, New Zealand | Classification: Social services organization, Social services organization | Visit Online: psc.org.nz | Contact Number (Helpline): +64 4 439 4900
  • AtareiraMentalHealthSupport | Location details: 14 Hartham Place North, Te Aro, Porirua 5022, New Zealand | Classification: Mental health service, Mental health service | Visit Online: atareira.org.nz | Contact Number (Helpline): +64 4 499 1049
  • ManaCoachServices | Location details: 7 Commerce Crescent, Waitangirua, Porirua 5024, New Zealand | Classification: Corporate office, Corporate office | Visit Online: manacoach.co.nz | Contact Number (Helpline): +64 4 235 8819
 courses in Porirua
There were 27,585 males and 28,974 females, giving a sex ratio of 0. 6% other ethnicities. As of June 2022 Porirua had a population of 60,200. 80 km2 (67. 5, compared with 27. In the 19th century, the name designated a land-registration district that stretched from Kaiwharawhara (or Kaiwara) on the north-west shore of Wellington Harbour northwards to and around Porirua Harbour. The percentage of people born overseas was 24. The name "Porirua" has a Māori origin: it may represent a variant of pari-rua, a reference to the two arms of the Porirua Harbour. In 1948, the Tawa Flat and Linden Progressive Associations and Porirua interests, led by Arthur Carman and Percy Clark, made a representation to the Local Government Commission asking for a single borough covering the whole of the Porirua Basin. The provinces were reformed again when the New Zealand Constitution Act of 1852 established six provinces with Porirua included in the Wellington Province.

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