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 Canberra

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

  • 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 Canberra
Data Science 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. 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 may learn all of the skills and talents required to become a data scientist by enrolling in the top data science online courses in Canberra. . Experts provide immersive online instructor-led seminars. This curriculum prepares you to work in a variety of Data Science professions and earn top-dollar wages. A data scientist is a person who uses a variety of procedures, methods, systems, and algorithms to analyze data to provide actionable insights. 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. 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.

List of All Courses & Internship by TechnoMaster

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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 Canberra

  • HannahTuition,YishunSembawangCanberra | Location details: 156 Yishun Street 11, Singapore 760156 | Classification: Education center, Education center | Visit Online: hannahtuition.com | Contact Number (Helpline): +65 8288 8792
  • HannahTuition,YishunSembawangCanberra | Location details: 156 Yishun Street 11, Singapore 760156 | Classification: Education center, Education center | Visit Online: hannahtuition.com | Contact Number (Helpline): +65 8288 8792
  • TheAustralianNationalUniversity | Location details: Canberra ACT 2601, Australia | Classification: University, University | Visit Online: anu.edu.au | Contact Number (Helpline): +61 2 6125 5111
  • UniversityOfCanberra | Location details: 11 Kirinari St, Bruce ACT 2617, Australia | Classification: Public university, Public university | Visit Online: canberra.edu.au | Contact Number (Helpline): +61 2 6201 5111
  • AlphaInstituteCanberraCampus | Location details: Unit 7/17 Oatley Ct, Belconnen ACT 2617, Australia | Classification: Vocational college, Vocational college | Visit Online: alphainstitute.edu.au | Contact Number (Helpline): +61 2 5134 2966
 courses in Canberra
As a federation of states, Australia needed capital on a "neutral" basis to ensure that minority views could be heard and that no one interest became dominant. . The suffering of World War II clearly showed how handicapped the leaders of the nations were because of the still incomplete nation of the capital. It was also called "Camberra". The theater centre, Civic Square, commercial and commercial office complexes, hotels and motels now gave Canberra the impression of an international national city. The development of the capital remained slow when house construction almost stopped due to the economic "low" of the 1930s. On 9 May 1927, the Duke of York (later King George VI) opened Parliament House, justifying the existence of Canberra. After the war, development was further hampered by an acute shortage of housing, building materials and labor. None of the people in this almost treeless place could imagine a city that only 50 years later would have, among other things, more than three million trees. --.

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