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 Colorado Springs

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

  • 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 Colorado Springs
Data Science 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. A data scientist is a person who uses a variety of procedures, methods, systems, and algorithms to analyze data to provide actionable insights. There are numerous reasons why you should take this course. 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 Colorado Springs. 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'll have a personal mentor who will keep track of your development. 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 Colorado Springs. . Experts provide immersive online instructor-led seminars. Identify and collect data from data sources.

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.

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 courses in Colorado Springs
Throughout the route of this Agreement, the District will post for OCR evaluation and approval any modifications to the plan permitted via way of means of OCR previous to their implementation. 2. (c) By August 1, 2016, the District will, in session with the Discipline Supervisor and/or professional(s), begin the method of analyzing the foundation motive(s) of the racial disparity withinside the subject of college students withinside the District via way of means of analyzing pertinent literature to be had at the subject, securing professional enter and attractive college students, personnel and individuals of the network with a purpose to discover and take each on the spot and long-time period suitable corrective movements vital to deal with the foundation motive(s) as part of the District`s techniques for assembly its dreams, as defined in object 1(b). The District has dedicated to making sure equitable subject on the idea of race as validated via way of means of Pillar #2 of the District`s signature techniques, entitled “secure weather and robust relationships with households and network,” that's to be performed via way of means of championing “equity, equity and cultural inclusiveness. As defined in extra element below, the District is dedicated to running with college students who show off conduct troubles to make certain that the scholars stay engaged withinside the District`s academic application and are given each possibility to attain their academic potential. (a) By May 31, 2016, the District will designate an worker to function the District`s Discipline Supervisor, and could post this individual`s call, and/or title, workplace deal with, email deal with and cellphone variety on its website, in all college courses concerning subject, and in any notices that the District sends to dad and mom annually. In addition, the District commenced enforcing education on Positive Behavioral Interventions and Supports (“PBIS”) in numerous colleges. (b) Throughout its implementation of this Agreement, the District will seek advice from with and, as vital, hold an professional or professionals in non-discriminatory subject practices, in addition to records evaluation and research-primarily based totally techniques, to prevent discrimination towards African American and Hispanic college students with admire to college subject. (e) By September 1, 2016, September 1, 2017, and September 1, 2018, the District will offer documentation to OCR concerning its implementation of the notice necessities of object 1(a), and documentation concerning its implementation of 1(b), and (c), such as the identification of the professional(s) it has consulted with and/or retained, the enter acquired via way of means of the professional(s), any similarly deliberate examinations and/or determinations regarding the foundation motive(s) of the racial disparity withinside the subject of college students, and the ensuing modifications made via way of means of the District. OCR and the District count on that those new employees, initiatives, and partnerships will align with and may fulfill a number of the phrases of this Agreement.

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