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
Course Fees
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Data Science Jobs in Manitoba

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

  • 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 Manitoba
Data Science This finest Data Science course was built with the needs of businesses in mind when it comes to the field of Data Science. 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. 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. Experts provide immersive online instructor-led seminars. A data scientist is a person who uses a variety of procedures, methods, systems, and algorithms to analyze data to provide actionable insights. 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 Manitoba. 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. 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. To find trends and patterns, use algorithms and modules. There are numerous reasons why you should take this course.

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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 Manitoba

  • ManitobaEmergencyServicesCollege | Location details: 1601 Van Horne Ave E, Brandon, MB R7A 7K2, Canada | Classification: Higher education, Higher education | Visit Online: firecomm.gov.mb.ca | Contact Number (Helpline): +1 204-726-6855
  • ManitobaInstituteOfTradesAndTechnologyEnglishLanguageInstitute | Location details: 67 Scurfield Blvd, Winnipeg, MB R3Y 1G4, Canada | Classification: English language school, English language school | Visit Online: mitt.ca | Contact Number (Helpline): +1 204-989-7740
  • FacultyOfScience,UniversityOfManitoba | Location details: 186 Dysart Rd, Winnipeg, MB R3T 2N2, Canada | Classification: University department, University department | Visit Online: sci.umanitoba.ca | Contact Number (Helpline): +1 204-474-8256
  • InternationalCollegeOfManitoba(ICM) | Location details: University of Manitoba 190 Extended Education Complex 406, University Crescent, Winnipeg, MB R3T 2N2, Canada | Classification: College, College | Visit Online: icmanitoba.ca | Contact Number (Helpline): +1 204-474-8479
  • ManitobaInstituteOfTradesAndTechnology | Location details: 609 Erin St, Winnipeg, MB R3G 2W1, Canada | Classification: High school, High school | Visit Online: mitt.ca | Contact Number (Helpline): +1 204-989-6434
  • ManitobaInstituteOfTradesAndTechnology | Location details: 130 Henlow Bay, Winnipeg, MB R3Y 1G4, Canada | Classification: Educational institution, Educational institution | Visit Online: mitt.ca | Contact Number (Helpline): +1 204-989-6500
  • ComputersForSchoolsManitoba | Location details: 18 Terracon Pl, Winnipeg, MB R2J 4G7, Canada | Classification: Non-profit organization, Non-profit organization | Visit Online: c4smb.ca | Contact Number (Helpline): +1 204-988-1790
  • ManitobaInstituteOfTradesAndTechnology | Location details: 1551 Pembina Hwy, Winnipeg, MB R3T 2E5, Canada | Classification: Educational institution, Educational institution | Visit Online: mitt.ca | Contact Number (Helpline): +1 204-989-6500
  • OperatingEngineersOfManitobaLocal987 | Location details: 200 Regent Ave W, Winnipeg, MB R2C 1R2, Canada | Classification: Labor union, Labor union | Visit Online: oe987.mb.ca | Contact Number (Helpline): +1 204-786-8658
  • OperatingEngineersTrainingInstituteOfManitoba | Location details: 225 McPhillips St, Winnipeg, MB R3E 2K3, Canada | Classification: Vocational school, Vocational school | Visit Online: oetim.com | Contact Number (Helpline): +1 204-775-7059
  • UniversityOfManitoba,ExtendedEducation | Location details: 406 University Crescent, Winnipeg, MB R3T 2N2, Canada | Classification: Education center, Education center | Visit Online: umanitoba.ca | Contact Number (Helpline): +1 204-474-8800
  • UniversityOfManitoba | Location details: 66 Chancellors Cir, Winnipeg, MB R3T 2N2, Canada | Classification: University, University | Visit Online: umanitoba.ca | Contact Number (Helpline): +1 800-432-1960
  • ManitobaSoftwareServices | Location details: 701 Regent Ave W Unit #136, Winnipeg, MB R2C 1S3, Canada | Classification: Software company, Software company | Visit Online: manitobasoftwareservices.com | Contact Number (Helpline):
 courses in Manitoba
Oromocto: (New Brunswick) This phrase is derived from the Maliseet phrase “welawelamooktook”, meaning “exact river”. Quebec: Aboriginal peoples first used the call "kebek" for the vicinity round Québec City. Mississauga: This town is known as after the Mississauga individuals who stay withinside the area, and describes the mouth of a river. This phrase is usually interpreted to mean "going lower back up. " The call refers to the sockeye salmon not unusualplace to the area. He diagnosed the voice of his bride, who become nevertheless many days tour away. It has additionally been counseled that the call comes from the Assiniboine phrases mini and tobow, meaning "Lake of the Prairie. Rimouski: (Quebec) This is a phrase of Mi`kmaq or Maliseet beginning, which has been translated as “land of moose” or “retreat of dogs”, possibly regarding its looking grounds. Ontario: This Huron call, first implemented to the lake, can be a corruption of onitariio, meaning "beautiful lake," or kanadario, which interprets as "sparkling" or "beautiful" water. Michi or missi means “many” and “saki” means “outlet”, “a river having many outlets”.

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