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 Geraldton

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

  • 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 Geraldton
Data Science A data scientist is a person who uses a variety of procedures, methods, systems, and algorithms to analyze data to provide actionable insights. Experts provide immersive online instructor-led seminars. This finest Data Science course was built with the needs of businesses in mind when it comes to the field of 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 Geraldton. 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. Effectively analyze both organized and unstructured data Create strategies to address company issues. 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.

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 Geraldton

 courses in Geraldton
com) Fiona Shallcross, Project Manager (Tourism), Midwest Development Commission Bruce Sherwood, Principal, Eastman Poletti Sherwood Architects Rob Smallwood, Digital Economy, Mid West Development Commission Susan Smith, Community and Cultural Development, Manager City of Greater Geraldton Mayor Shane Van Styn, City of Greater Geraldton Pieter Vorster, Economic Development, City of Greater Geraldton Sara Walker, Acting Coordinator Art Gallery and Cultural Development, Geraldton Regional Art Gallery Savanah Wilkinson, Artist We also acknowledge the assistance of Nicki Hall and Vivien Clifford in undertaking this research. The local arts ecosystem is rich and varied and includes a number of arts and cultural assets and groups, for example: Maritime, museums, and tourism  Museum of Geraldton’s collection about the Batavia shipwreck  maritime heritage  Abrolhos Islands  HMAS Sydney (II) Wreck Memorial  Hamlet Greenough Museum and Gardens  eco-touring Arts and media  Yamaji Art  Irra Wangga Language Centre. au/creativehotspots/ 5 FINAL v2 19 May 2020 Strategic theme 1: What are the interrelationships across the sub-sectors of the creative industries? There are three key elements in the vibrant arts and culture ecosystem of Geraldton:  a long-term community-led arts and culture scene  prominent local arts institutions with publicly funded staff  a spirit of arts entrepreneurship. qut. The report draws on socio-economic data1 compiled for the ‘creative hotspots’ included in this study, and all Australian local government areas – as well as desk research, and a small but diverse sample of key informant interviews in Geraldton. Pursuant to this aim we provide key statistics, discuss notable ‘hotspot’ enablers, and their context, and provide mini-cases studies which will be of benefit to the arts and culture sector in Australia. It's clear that artists are struggling a bit to understand some of the basics of how to build an audience and collaborate on for more platform opportunities. The opportunity to connect with other artists was welcome but the lack of Aboriginal representatives was noted. , artists said they were attracted to Geraldton's lifestyle and area because of the supportive culture and , more affordable housing and rent compared to Perth. In addition to creators, Euphorium also emphasizes the development of Artistic Products and encourages participation in skill-building activities (eg through Sweet Orange Productions).

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