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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 Derry City

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

  • 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 Derry City
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. Identify and collect data from data sources. You'll have a personal mentor who will keep track of your development. Experts provide immersive online instructor-led seminars. 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 Derry City. This curriculum prepares you to work in a variety of Data Science professions and earn top-dollar wages. 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. To succeed as a data scientist, you must, nevertheless, make a particular effort to apply soft skills. A data scientist is a person who uses a variety of procedures, methods, systems, and algorithms to analyze data to provide actionable insights.

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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 Derry City

 courses in Derry City
This is particularly true in Ireland where further and further jobs are getting available in the burgeoning Irish language sector, and also in the European Union, where Irish has been upgraded to a full working language of the European institutions, bringing with it numerous and varied openings. English language achievement exploration constantly finds that the absorption experience actually enhances English language development( pall, Genesee & Hamayan 2000). This position was challenged in relation to the provision of academy transport for IME in the Judicial Review case of McKee V Department of Education. ( Bialystok, Craik, Freedman, 2006) Area Planning Recommendations for Irish-medium Primary Provision in Derry City 5| P a g e profitable Benefits A study carried out in the United States on life- chances of monolingual and bilingual youthful people concluded that there were significant goods of bilingualism on three socio- profitable issues dwindling the odds of dropping out of high academy, adding occupational status and earnings. This involves the natural accession of the language by children where Irish is the primary language of instruction and communication in Irish-medium seminaries. The Drumragh decision specifically linked the limitations of the requirements model in Area Planning when it comes to the statutory duty to encourage and grease. A natural development was the setting up of an education system which would give Irish-medium Education for their children, in the absence of similar provision by the state. Irish-medium Education in the North started when a group of youthful Belfast families in the 1960s decided to develop Irish as a family and community language by setting up a original Irish- speaking nexus. This case is particularly applicable to the current process of area planning and the fact the Area Planning process doesn't assume growth in the integrated or IM sectors. I consider that this is a valid argument and as it isn't duly addressed in the decision timber process.

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