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 Portland

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

  • 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 Portland
Data Science There are numerous reasons why you should take this course. 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. To find trends and patterns, use algorithms and modules. Create data strategies with the help of team members and leaders. 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. To succeed as a data scientist, you must, nevertheless, make a particular effort to apply soft skills. You'll have a personal mentor who will keep track of your development. 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. Experts provide immersive online instructor-led seminars. 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.

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.

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 courses in Portland
Portland`s contribution to the latter strugglefare attempt become the development of greater than one thousand shipment ships and small naval vessels at 3 large shipyards operated with the aid of using industrialist Henry J. At the identical time, grasp making plans efforts have given identical interest to livability and open area withinside the town, so that Portland these days boasts a complete of 247 parks and leisure web sites consisting of 196 community parks. Still wondering ahead, Portland Parks followed their plan for the future, the Parks 2020 Vision in the 12 months 2000. The first steps that modified a dowdy and unimaginative town right into a national pace-setter got here withinside the past due Nineteen Sixties. The six-county place includes Multnomah County (City of Portland), Washington, Clackamas, Yamhill, Clark, and Columbia counties. In 1905, the town subsidized the Lewis and Clark Centennial Exposition and Oriental Fair, the primary world`s honest on the American west coast. After the Civil War, the town drew the grain change of jap Oregon and southeastern Washington through Columbia River steamers and railroads (it received a transcontinental rail connection in 1883) and exported farm and woodland products. Portland, Oregon is an environmentally modern Northwest town that has taken the idea of open area making plans to coronary heart. Yet on the identical time, Portland's centers are getting older and require interest if they're to fulfill the desires of an increasing population. 1).

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