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 Ras Al Khaimah

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

  • 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 Ras Al Khaimah
Data Science Cleaning and validating data to ensure that it is accurate and consistent. Identify and collect data from data sources. 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. This finest Data Science course was built with the needs of businesses in mind when it comes to the field of Data Science. Effectively analyze both organized and unstructured data Create strategies to address company issues. A data scientist is a person who uses a variety of procedures, methods, systems, and algorithms to analyze data to provide actionable insights. 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. 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. 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 Ras Al Khaimah

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  • AMTech Software company in Ras Al-Khaimah, United Arab Emirates Address: Madar Tower Main Road - North Ras Al Khaimah - United Arab Emirates Phone: +971 55 444 5586 , website:https://www.facebook.com/amteclinks/
  • Pearl Information System Software company in Ras Al-Khaimah, United Arab Emirates Address: CMM mall -M floor Room M15 Opposite Manama Hypermarket Nakeel Ras al Khaimah - Buskerud - Norway Phone: +971 50 188 2429 , website:www.pearlisys.com/
  • Anexas FZE Business management consultant in Ras Al-Khaimah, United Arab Emirates Address: RAK FTZ - Ras Al-Khaimah - United Arab Emirates Phone: +971 50 836 8495 , website:https://www.anexas.net/
  • Talent Technologies Software company in Ras Al-Khaimah, United Arab Emirates Address: North Ras Al Khaimah - United Arab Emirates Phone: +971 7 226 3936 ,website:https://m.khaleejtimes.com/.../software-sector-witnesses-highest-talent...
 courses in Ras Al Khaimah
Typically, the nations that financially sponsored the colleges additionally staffed them and used their own texts and curricula. , 2015; Davidson, 2008). At the quit of the 19th century, there has been a essential shift in training because the vicinity skilled more wealth following a increase withinside the nearby pearling enterprise. Despite the status quo of numerous colleges throughout this time period, training changed into nevertheless normally to be had to youngsters from privileged backgrounds (Ridge, 2009). These instructors delivered with them teacher-focused strategies to coaching and learning (Davidson, 2008). With the brand new wealth from pearling, call for for training increased. 1 BOX Historical Development of Education withinside the United Arab Emirates1 The earliest types of training in today`s United Arab Emirates (UAE) have been the katateeb, extensions of training in mosques, which targeted on Islamic teachings and the fundamentals of analyzing and writing withinside the former sheikdoms of the Arabian Gulf2 (AlNaqbi, 2009). Historically, many Egyptian instructors got here to the Gulf vicinity as settlement people employed via the Egyptian Ministry of Education, however that has in view that changed. In 1912, the primary personal colleges withinside the Trucial States opened in Dubai and Sharjah (Davidson, 2008). , 2015; Davidson, 2008).

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