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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
Course Fees
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25+

Data Science Jobs in Ontario

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

  • 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 Ontario
Data Science 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. 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. Effectively analyze both organized and unstructured data Create strategies to address company issues. Create data strategies with the help of team members and leaders. 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. 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 Ontario. A data scientist is a person who uses a variety of procedures, methods, systems, and algorithms to analyze data to provide actionable insights. Creative thinking, problem-solving skills, curiosity, and a drive to learn about and investigate industry trends and development, as well as teamwork, are among the soft skills required by data scientists. This finest Data Science course was built with the needs of businesses in mind when it comes to the field of Data Science.

List of All Courses & Internship by TechnoMaster

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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 Ontario

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  • OntarioTechUniversity | Location details: 2000 Simcoe St N, Oshawa, ON L1G 0C5 | Classification: University, University | Visit Online: ontariotechu.ca | Contact Number (Helpline): (905) 721-8668
  • CIMTCollege-BramptonCampus | Location details: 7900 Hurontario St Suite 1, Brampton, ON L6Y 0P6 | Classification: Educational institution, Educational institution | Visit Online: cimtcollege.com | Contact Number (Helpline): (905) 671-9999
  • StarTekComputerServices | Location details: 758 Ontario St, Cobourg, ON K9A 3C5 | Classification: Computer service, Computer service | Visit Online: startekservices.com | Contact Number (Helpline): (905) 373-0613
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  • CIMTCollege-BramptonCampus | Location details: 7900 Hurontario St Suite 1, Brampton, ON L6Y 0P6 | Classification: Educational institution, Educational institution | Visit Online: cimtcollege.com | Contact Number (Helpline): (905) 671-9999
  • OntarioTechUniversity | Location details: 2000 Simcoe St N, Oshawa, ON L1G 0C5 | Classification: University, University | Visit Online: ontariotechu.ca | Contact Number (Helpline): (905) 721-8668
  • OperatingEngineersTrainingInstituteOfOntario | Location details: 12580 Stormont, Dundas and Glengarry County Road 2, Morrisburg, ON K0C 1X0 | Classification: Training centre, Training centre | Visit Online: oetio.com | Contact Number (Helpline): (613) 543-2911
  • P2L | Location details: 64 Hurontario St, Collingwood, ON L9Y 2L6 | Classification: Distance learning center, Distance learning center | Visit Online: p2linc.com | Contact Number (Helpline): (289) 400-2297
  • MalayaliWebDesignerToronto,ontario,MississaugaONCanada | Location details: Manimangalam House, PO, Veloor, Kottayam, Kerala 686003, India | Classification: Website designer, Website designer | Visit Online: webkitsolution.com | Contact Number (Helpline):
 courses in Ontario
Canada has performed fulfillment inside a exceedingly federated gadget, which functions widespread variety, specifically with recognize to problems of language and usa of origin. The provincial authorities is answerable for placing the curriculum, figuring out many main policies for colleges and offering the majority, if now no longer all, of the investment for colleges (investment styles range barely throughout provinces). Responsibility in the provinces is split among the critical provincial authorities and extra locally-elected college forums. These effects had been showed in subsequent PISA tests, that have discovered that Canada has each sturdy common effects in addition to much less dispersion amongst its high and coffee socio-financial status (SES) college students than many different nations (OECD, 2010). Education is the obligation of its 10 provinces and three territories. In 1987, British Columbia become the primary to make its instructors self-governing, granting to the British Columbia College of Teachers exclusive obligation for governing entry, discipline, and expert improvement of instructors. This shape manner that those colleges and college forums in Canada are in the public gadget and below partial manipulate of the Ministry of Education, now no longer withinside the personal sector. Section 23 of the Canadian Charter of Rights and Freedoms protects dad and mom who communicate a minority language (English or French), offers their kids the proper to acquire number one and secondary guidance in their local language, and lets in for the established order of “minority language instructional facilities,” if enough numbers warrant it. In 1996, Ontario followed suit, developing an Ontario College of Teachers which governs comparable functions; on its 31 member governing council take a seat down 17 instructors elected via way of means of the College, and 14 participants appointed via way of means of the Ontario Minster of Education. In each cases, extra conventional problems, along with wages, maintain to fall below collective bargaining and are become independent from the paintings of those self-regulating bodies.

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