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 Oklahoma City

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

  • 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 Oklahoma City
Data Science Identify and collect data from data sources. 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 Oklahoma City. 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. Create data strategies with the help of team members and leaders. Cleaning and validating data to ensure that it is accurate and consistent. Experts provide immersive online instructor-led seminars. 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 Oklahoma City. There are numerous reasons why you should take this course. To succeed as a data scientist, you must, nevertheless, make a particular effort to apply soft skills.

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 Oklahoma City

  • FrancisTuttleTechnologyCenter-RockwellCampus | Location details: 12777 N Rockwell Ave, Oklahoma City, OK 73142, United States | Classification: Technical school, Technical school | Visit Online: francistuttle.edu | Contact Number (Helpline): +1 405-717-7799
  • TransportationSafetyInstitute | Location details: Lindy Ritz Lane and, 6500 S MacArthur Blvd, Duke Ave, Oklahoma City, OK 73169, United States | Classification: Training school, Training school | Visit Online: transportation.gov | Contact Number (Helpline): +1 405-954-3153
 courses in Oklahoma City
They desired to enhance the first-class of instructional buildings, scholar interaction, grownup schooling, and county roads (due to the fact college students wanted transportation). Typically, dad and mom made payments (round a greenback in keeping with child every month) to the college`s trainer. Imagine having to be in elegance with your older or more youthful sibling all day! The topics studied commonly consisted of reading, writing, arithmetic, history, and geography. Sometimes the places turned out to be tents, dugouts, and churches. —carried out an extensive, quantitative assessment (“audit”) of the innovation district. We tested severa statistics reassets to apprehend the district and area`s studies expertise, enterprise strengths, and entrepreneurship outcomes; undertook onthe-floor observational studies to apprehend how and whilst district areas had been utilized; carried out a survey of Health Center college students and personnel to apprehend their commuting styles and use of public areas; and took part in numerous stakeholder conferences and workshops. The instructors created an agency that might closing for extra than a century. Communities advanced extra faculties presenting grades one via eight. LOCATION The Oklahoma City Metropolitan Statistical Area (MSA) includes seven counties: Oklahoma, Canadian, Cleveland, Grady, Lincoln, Logan, and McClain. This have become viable due to college consolidation wherein the assets of multiple, small faculties had been combined.

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