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 Kingston

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

  • 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 Kingston
Data Science 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. 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. This finest Data Science course was built with the needs of businesses in mind when it comes to the field of Data Science. To find trends and patterns, use algorithms and modules. This curriculum prepares you to work in a variety of Data Science professions and earn top-dollar wages. Experts provide immersive online instructor-led seminars. 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 Kingston. Effectively analyze both organized and unstructured data Create strategies to address company issues. .

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 Kingston

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  • LearningWithTechnologies | Location details: 10 Kingston Park Ct, Knoxfield VIC 3180, Australia | Classification: Computer store, Computer store | Visit Online: lwt.com.au | Contact Number (Helpline): +61 1300 550 717
  • St.LawrenceCollege | Location details: 100 Portsmouth Ave, Kingston, ON K7L 5A6 | Classification: College, College | Visit Online: stlawrencecollege.ca | Contact Number (Helpline): (613) 544-5400
  • KingstonInstituteAustralia(KIA) | Location details: Level 7/8 Quay St, Haymarket NSW 2000, Australia | Classification: Technical school, Technical school | Visit Online: kingston.nsw.edu.au | Contact Number (Helpline): +61 2 8065 2990
  • EfficientCleaningLondonLtd. | Location details: 23, Brooklands court, Surbiton Rd, Kingston upon Thames KT1 2HE, United Kingdom | Classification: House cleaning service, House cleaning service | Visit Online: efficient-cleaninglondon.co.uk | Contact Number (Helpline): +44 20 8288 9858
  • TrainingTrack | Location details: 71-73 Kingston St, Glasgow G5 8BJ, United Kingdom | Classification: Education, Education | Visit Online: | Contact Number (Helpline): +44 845 805 2652
This resulted in additions and large-scale apartment renovations, as well as conversion of one apartment into two or more apartments. 8 99. Regulatory Framework is outdated and disconnected from the City's Official Plan. Such developments have caused public concern about their impact on the architectural form and character of the affected districts. seems to be the biggest. 1. The strategy aims to enable a long-term vision for Kingston's central residential areas, preserving the values ​​of Kingston's communities and identifying suitable locations and forms for future growth. In January 2018, the City of Kingston (the "City") launched the Central Kingston Growth Strategy (the "Strategy"), which aims to establish a policy and regulatory framework to guide the implementation and improvement of the City . These , Central Kingston residential areas are governed by Zoning By-law No. Phase One Background Report 2 Lawrence College).

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