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
Course Fees
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Data Science Jobs in Brisbane

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

  • 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 Brisbane
Data Science . To find trends and patterns, use algorithms and modules. To succeed as a data scientist, you must, nevertheless, make a particular effort to apply soft skills. 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. 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. 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. 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 Brisbane. Cleaning and validating data to ensure that it is accurate and consistent. 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. 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 Brisbane.

List of All Courses & Internship by TechnoMaster

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List of Training Institutes / Companies in Brisbane

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  • NetworkAustralia | Location details: 241 Adelaide St, Brisbane City QLD 4000, Australia | Classification: Training centre, Training centre | Visit Online: networkaustralia.com.au | Contact Number (Helpline): +61 1300 766 330
  • RetailExpress | Location details: 320 Adelaide St, Brisbane City QLD 4000, Australia | Classification: Software company, Software company | Visit Online: retailexpress.com.au | Contact Number (Helpline): +61 1300 732 618
  • SarinaRussoInstitute | Location details: 100 Eagle St, Brisbane City QLD 4000, Australia | Classification: Educational institution, Educational institution | Visit Online: sri.edu.au | Contact Number (Helpline): +61 1800 078 776
  • TAFEQueenslandSouthBankCampus | Location details: 60 Tribune St, South Brisbane QLD 4101, Australia | Classification: Vocational college, Vocational college | Visit Online: tafeqld.edu.au | Contact Number (Helpline): +61 1300 308 233
  • NexacuHeadOffice | Location details: 10/333 Adelaide St, Brisbane City QLD 4000, Australia | Classification: Software training institute, Software training institute | Visit Online: nexacu.com.au | Contact Number (Helpline): +61 1300 886 190
 courses in Brisbane
Export opportunities for expertise and services in these and other sectors are expected to be strong, but only if their potential is. We would only need to improve Australia's productivity growth to around 1. Economists and policymakers generally expect the current elevated terms of trade to return to more normal conditions, while investment will continue to support GDP for several years. However, many of these assumptions are based on Australia's ongoing trade relations with our Asian neighbours, particularly in goods, and the belief that this trade will continue at current levels. Although Australia has a "first mover" advantage in natural resource exploitation, in the medium term research and investment elsewhere will have a major impact on both the terms of trade and the willingness of trade to continue at a high level. I have allocated $15. The economy of our multi-speed is still much talked about and serious concerns have been expressed by dealers. and industry to ensure their continued financial viability. Recent statistics on Australia's GDP growth and employment paint a picture of an economy on steroids, but there are some lingering questions about the current and future contributions of various sectors to the country's long-term economic sustainability. its international competitive advantage and this focus needs to be reaffirmed.

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