Data Science Training by Experts

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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 Toowoomba

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

  • 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 Toowoomba
Data Science 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. Cleaning and validating data to ensure that it is accurate and consistent. . Create data strategies with the help of team members and leaders. Identify and collect data from data sources. 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. 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 Toowoomba. Experts provide immersive online instructor-led seminars. 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. A data scientist is a person who uses a variety of procedures, methods, systems, and algorithms to analyze data to provide actionable insights.

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 Toowoomba

  • UniversityOfSouthernQueensland(UniSQ) | Location details: UniSQ Toowoomba, 487-535 West St, Darling Heights QLD 4350, Australia | Classification: University, University | Visit Online: unisq.edu.au | Contact Number (Helpline): +61 1800 269 500
 courses in Toowoomba
Institutions are collective human- designed action, such as government strategies, plans, policies or laws, business or industry norms, social norms, cultural beliefs or the general patterns of consumer behaviour (Mantzavinos, North and Shariq, 200 ). This paper reports on a preliminary investigation into social values and perceptions of tourism and economic development in the case study of Toowoomba, Australia. Review of the literature indicates that there is a lack of knowledge surrounding the dynamic interaction of structures and institutions and the reciprocal relationship they have with tourism, particularly at a local level (Agarwal, 2002; Scott, 2003; Rodriguez, Parra-Lopez and Yanes-Estevez, 2008). A number of other studies have linked community perceptions towards visitors with the Tourism Area Life Cycle (TALC) model (Butler, 1980), giving rise to concepts of carrying capacity and management across the triple bottom line (Belisle and Hoy, 1980; Coccossis, 2002; Diedrich and Garcia-Buades, 2009). Diedrich and Garcia-Buades (2009) show that as tourism grows and has more severe impacts on an area, so does the population's perception of tourism implications. When considering tourism planning, a key concern in the tourism transformation literature is the role and responsibility of government (Haung, 200 ; Briedenhann and Butts, 200 ; Pavlovich, 2003; McLennan, 2005). It is often postulated that local or regional governments should self-direct and play a greater role in tourism development because structural changes and impacts have the greatest effect and can be more readily observed at the local level (Adams, Dixon and Rimmer, 2001; Milne and Ateljevic, 2001; Pavlovich, 2003; Haung, 200 ) and, at this level, institutional modifications and planned intervention are more likely to be effective (Roberts, 200 ; McLennan, 2005; Sebastian and Rajagoplan, 2009). , 2007; Gartner, 200 ). Transformation theory is about structural change that results from modifications of human institutions (Seliger, 2002). One occurs when tourists are attracted to the unspoiled nature of a destination, but their increasing visitation transforms the destination and traditional lifestyle into a more urban or globalised one (Bruner, 1991; Dahms and McComb, 1999; Agarwal, 2002; Zhong, et al.

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