Data Science Training by Experts

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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 Saint John

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

  • 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 Saint John
Data Science To succeed as a data scientist, you must, nevertheless, make a particular effort to apply soft skills. Experts provide immersive online instructor-led seminars. 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. 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. 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. A data scientist is a person who uses a variety of procedures, methods, systems, and algorithms to analyze data to provide actionable insights. Identify and collect data from data sources. . 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. 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 Saint John

  • SolidSolutions-SolidWorksInCambridge | Location details: Saint John's Innovation Centre, Cowley Rd, Milton, Cambridge CB4 0WS, United Kingdom | Classification: Software company, Software company | Visit Online: solidsolutions.co.uk | Contact Number (Helpline): +44 1844 295235
  • UniversityOfNewBrunswickSaintJohn(UNBSJ) | Location details: 100 Tucker Park Rd, Saint John, NB E2K 5E2 | Classification: University, University | Visit Online: unb.ca | Contact Number (Helpline): (506) 648-5500
  • BlueScientific | Location details: Saint John's Innovation Centre, Cowley Rd, Milton, Cambridge CB4 0WS, United Kingdom | Classification: Scientific equipment supplier, Scientific equipment supplier | Visit Online: blue-scientific.com | Contact Number (Helpline): +44 1223 422269
  • NBCCSaintJohnGrandviewCampus | Location details: 950 Grandview Ave, Saint John, NB E2J 4C5 | Classification: Community college, Community college | Visit Online: nbcc.ca | Contact Number (Helpline): (506) 658-6600
  • CarpenterMillwrightCollege | Location details: 300 Grandview Ave, Saint John, NB E2J 4N1 | Classification: School, School | Visit Online: carpentermillwrightcollege.ca | Contact Number (Helpline): (506) 632-8840
  • CUNITECH | Location details: 60 Charlotte St, Saint John, NB E2L 2H9 | Classification: Educational institution, Educational institution | Visit Online: cunitech.ca | Contact Number (Helpline): (506) 405-0133
  • Long&McQuadeMusicalInstruments | Location details: 569 Rothesay Ave, Saint John, NB E2H 2G9 | Classification: Musical instrument store, Musical instrument store | Visit Online: long-mcquade.com | Contact Number (Helpline): (506) 672-2937
  • NbccSaintJohnUnbCampus | Location details: 100 Tucker Park Rd, Saint John, NB E2K 5E2 | Classification: University, University | Visit Online: nbcc.ca | Contact Number (Helpline): (506) 658-6600
 courses in Saint John
By the mid-1870s, Saint John had rail connections with both Central Canada and New England, but steamships made its sailing ships obsolete, and the railroads, instead of increasing the market, destroyed local manufacturers by undermining competition. . Despite this lack of official encouragement, to say nothing of official obstacles, in the middle of the 19th century Saint John achieved "a prominent role in the Atlantic communication system, reaching in one direction to Liverpool and London, in the other to Boston and New York ". Until the 18 0s, provincial and the needs of San Juan complimented each other so that government in the hands of loyal rulers caused few problems. The financial power of Saint John reached its peak in the 1850s and 1860s when its builders, bankers, insurance men, merchants, often the same people, 2 mingled with other people from London, Boston and New York. The lack of provincial support for led to an interest in politics and ultimately control of the politicians who represented it in the implementation of . Wilmot, J. John was not chosen as the capital of the province, and they were not left with , civil, military, and educational institutions, , of which went to little Fredericton. Even with these credentials, , St. Promoters such as Robert Jardine, John Robertson, and Richard Wright envisioned their city as a profitable clearinghouse for both east-west and north-south traffic on the Euro-North American and intercolonial railroads.

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