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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 Prince George

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

  • 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 Prince George
Data Science . Identify and collect data from data sources. Effectively analyze both organized and unstructured data Create strategies to address company issues. 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 Prince George. You'll have a personal mentor who will keep track of your development. 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. 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 Prince George. 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.

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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 Prince George

  • InstituteForMathematicalSciences | Location details: 3 Prince George's Park, Singapore 118402 | Classification: University department, University department | Visit Online: | Contact Number (Helpline): +65 6516 1897
 courses in Prince George
These one hundred new households might additionally generate some $2. As shown in Figure 6, 57% of these with French as their mom tongue are over the age of fifty five as compared to handiest 28% a few of the populace overall. Settlement organizations are an crucial asset withinside the place imparting guide offerings to assist novices withinside the network, however, the network itself performs a function in fostering a welcoming surroundings for novices. The Prince George city location (populace 87,000)1 is the monetary capital of northern British Columbia and domestic to a huge woodland merchandise area, with extra than one hundred forty corporations using extra than four,500 workers2 . a developing pool from which to attract members. Supporting the place`s French-talking network Prince George has been decided on as a Welcoming Francophone Community to assist French-talking novices experience welcomed and included in Francophone minority groups outdoor Quebec. Other superb effects Attracting extra immigrants, such as French-talking or bilingual immigrants, to Prince George might assist guide a colourful monetary and social lifestyles in lots of different ways. More than 70% of Prince George citizens are as a minimum thirdgeneration Canadians, that means they have been born in Canada, as have been their mother and father and grandparents. Helping to preserve excessive first-class public offerings There is an immediate courting among monetary boom and provincial and neighborhood authorities ability to offer excessive first-class public offerings and public infrastructure along with roads, faculties and hospitals. It is crucial that novices are welcomed into our social lifestyles and broaden a robust experience of Canadian identification and social cohesion.

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