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
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Data Science Jobs in Kilkenny

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

  • 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 Kilkenny
Data Science Identify and collect data from data sources. 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 Kilkenny. To find trends and patterns, use algorithms and modules. 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 Kilkenny. . 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. 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. Cleaning and validating data to ensure that it is accurate and consistent. This curriculum prepares you to work in a variety of Data Science professions and earn top-dollar wages. 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 Kilkenny

  • IrishFlooringAcademy | Location details: Unit 6, Athy Business Campus, Block C, Kilkenny Rd, Woodstock South, Athy, Co. Kildare, R14 FH31, Ireland | Classification: Training centre, Training centre | Visit Online: irishflooringacademy.com | Contact Number (Helpline): +353 59 863 4832
  • KieranKellyWebDesign&DigitalMarketing | Location details: New Work Junction, Old Dublin Road,, Kilkenny, Ireland | Classification: Website designer, Website designer | Visit Online: kierankelly.me | Contact Number (Helpline): +353 85 777 5489
  • SecurityAndSafetyTrainingCoursesIreland | Location details: Security and Safety Solutions, Unit 5/6, Athy Business Campus, Block C, Kilkenny Rd, Athy, Co. Kildare, R14 YR80, Ireland | Classification: Training centre, Training centre | Visit Online: securitysafety.ie | Contact Number (Helpline): +353 87 644 4497
  • SecurityAndSafetyTrainingCoursesIreland | Location details: Security and Safety Solutions, Unit 5/6, Athy Business Campus, Block C, Kilkenny Rd, Athy, Co. Kildare, R14 YR80, Ireland | Classification: Training centre, Training centre | Visit Online: securitysafety.ie | Contact Number (Helpline): +353 87 644 4497
  • RainRainWebDesign&Consultancy | Location details: Mountnugent Upper, Ossary hill, Johnswell, Co. Kilkenny, Ireland | Classification: Website designer, Website designer | Visit Online: rainrain.ie | Contact Number (Helpline): +353 56 770 8942
  • IrishFlooringAcademy | Location details: Unit 6, Athy Business Campus, Block C, Kilkenny Rd, Woodstock South, Athy, Co. Kildare, R14 FH31, Ireland | Classification: Training centre, Training centre | Visit Online: irishflooringacademy.com | Contact Number (Helpline): +353 59 863 4832
 courses in Kilkenny
It is intended that this publication will support and encourage teachers, students and others to explore the rich historic legacy that surrounds us. This beautifully illustrated publication and CD provides a depth of knowledge about this medieval city with its narrow streets, its distinctive townscape and rich historic fabric. While this publication has been produced to meet the needs of teachers and their students it will, I believe, have a use far beyond any specific audience providing as it does a wonderful guide to the city that will be of interest to resident and visitor alike. We hope you will take the time explore the city and see it through the eyes of those who have contributed to this publication and have shared their knowledge and enthusiasm with u. For this reason, and because it is one of Ireland’s built heritage gems, it is with great delight that we have supported the work of Kilkenny Education Centre in bringing together this publication and supporting documentation on the 400 anniversary of its City charter. The Heritage Council’s education and outreach work supports, where possible, efforts to bring our heritage alive through active learning.

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