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
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Data Science Jobs in Vancouver

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

  • 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 Vancouver
Data Science 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. You'll have a personal mentor who will keep track of your development. 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 Vancouver. Cleaning and validating data to ensure that it is accurate and consistent. 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. There are numerous reasons why you should take this course. A data scientist is a person who uses a variety of procedures, methods, systems, and algorithms to analyze data to provide actionable insights. 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 Vancouver. 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. 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.

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 Vancouver

  • LangaraCollege | Location details: 100 W 49th Ave, Vancouver, BC V5Y 2Z6 | Classification: College, College | Visit Online: langara.ca | Contact Number (Helpline): (604) 323-5511
  • Thinkific | Location details: 369 Terminal Ave #400, Vancouver, BC V6A 4C4 | Classification: Software company, Software company | Visit Online: thinkific.com | Contact Number (Helpline): +1 888-832-2409
  • LangaraCollege | Location details: 100 W 49th Ave, Vancouver, BC V5Y 2Z6 | Classification: College, College | Visit Online: langara.ca | Contact Number (Helpline): (604) 323-5511
  • AshtonCollege | Location details: 1190 Melville St #300, Vancouver, BC V6E 3W1 | Classification: College, College | Visit Online: ashtoncollege.ca | Contact Number (Helpline): (604) 899-0803
  • LangaraCollegeContinuingStudies:WestBroadwayCampus | Location details: 601 W Broadway, Vancouver, BC V5Z 4C2 | Classification: Community college, Community college | Visit Online: langara.ca | Contact Number (Helpline): (604) 323-5322
  • BayswaterVancouver(FormerlyELSVancouver) | Location details: 549 Howe St 6th Floor, Vancouver, BC V6C 2C2 | Classification: Language school, Language school | Visit Online: bayswater.ac | Contact Number (Helpline): (604) 684-9577
  • ILAC-InternationalLanguageAcademyOfCanada | Location details: 688 W Hastings St, Vancouver, BC V6B 1P1 | Classification: Language school, Language school | Visit Online: ilac.com | Contact Number (Helpline): (604) 484-6660
  • RapidComputerTraining,Inc. | Location details: 777 Hornby St #600, Vancouver, BC V6Z 1S4 | Classification: Computer training school, Computer training school | Visit Online: rapidtraining.ca | Contact Number (Helpline): +1 888-376-2981
  • UBCExtendedLearning | Location details: 5950 University Blvd, Vancouver, BC V6T 1Z3 | Classification: Educational institution, Educational institution | Visit Online: extendedlearning.ubc.ca | Contact Number (Helpline): (604) 822-1444
  • SterlingCollege | Location details: 1111 Melville St Suite 200, Vancouver, BC V6E 3V6 | Classification: Educational institution, Educational institution | Visit Online: sterlingcollege.ca | Contact Number (Helpline): (604) 638-7040
  • Trainerize | Location details: 1250 Homer St, Vancouver, BC V6B 2Y5 | Classification: Software company, Software company | Visit Online: trainerize.com | Contact Number (Helpline):
  • On-TrackCorporateTraining | Location details: 609 Granville St Suite 650, Vancouver, BC V6C 1X6 | Classification: Training centre, Training centre | Visit Online: on-track.com | Contact Number (Helpline): (604) 683-0020
  • VCC-DowntownCampus | Location details: 250 W Pender St, Vancouver, BC V6B 1S9 | Classification: College, College | Visit Online: vcc.ca | Contact Number (Helpline): (604) 871-7000
 courses in Vancouver
The 2016 census found out that “seen minorities” (“humans of colour”) make up the bulk of the populace in 5 Metro Vancouver municipalities consisting of Richmond, Burnaby and Surrey2 (Statistics Canada, 2016). The Vancouver metropolitan location has passed through good sized adjustments over the last forty years, each in phrases of populace boom and diversification in addition to the densification of its constructed environment. The offerings sector common stays dominant withinside the nearby economic system because it has been because the 1960s, however the blend of constituent industries consists of big numbers of low-salary jobs withinside the retail, distribution and intake sectors. Section three will set out the specific governance systems and making plans procedures which can be in location withinside the Metro Vancouver location, almost about the `Vancouver Model`, (or so-called “Vancouverism”) that attracted interest in the. These underpin each the set up neighborhood narrative of livability and (relatedly) the fantastic city imaginary successfully advertised internationally. The following Section 2 will caricature out the records of the metropolis, and its an increasing number of complicated hyperlinks globally, specially with the states, societies and economies of the Asia-Pacific, and consisting of dialogue of each the improvement possibilities and obstacles related to this enjoy. In relation to social exclusion, Vancouver`s Downtown Eastside [DTES] is one in all Canada`s maximum disadvantaged neighbourhoods, and but it sits only a stone`s throw farfar from rich residential downtown districts, wherein excessive-upward thrust slim-line rental condominium blocks are advertised for millions, with their lovely perspectives of Vancouver`s north shore and coastal mountains (Figure 1). But considered via a greater stringent analytical lens Vancouver`s enjoy of boom and change discloses greater tricky aspects, fashioned each via way of means of modern-day elements in addition to a records of underdevelopment related to geographical marginality given that first European contact (1792). Ranked via way of means of The Economist because the maximum livable metropolis withinside the global for 10 years running (2002 to 2011), it's far presently ranked in sixth location globally (Economist Intelligence Unit, 2018). On the only hand, it's far mentioned as one of the maximum suitable locations to stay globally, in terms of first-rate of lifestyles and livability indices.

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