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
Course Fees
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Data Science Jobs in Mesaieed

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

  • 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 Mesaieed
Data Science Cleaning and validating data to ensure that it is accurate and consistent. There are numerous reasons why you should take this course. Identify and collect data from data sources. Experts provide immersive online instructor-led seminars. This curriculum prepares you to work in a variety of Data Science professions and earn top-dollar wages. To succeed as a data scientist, you must, nevertheless, make a particular effort to apply soft skills. Create data strategies with the help of team members and leaders. 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. Effectively analyze both organized and unstructured data Create strategies to address company issues.

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 Mesaieed

  • GulfDrivingSchool | Location details: XHM2+8X7, Mesaieed, Qatar | Classification: Driving school, Driving school | Visit Online: | Contact Number (Helpline): +974 4477 0719
  • PetrosolutionsTrading&Services | Location details: Souq Mesaieed, Bulding A, (A-S03). Mesaieed Industrial City، Mesaieed, Qatar | Classification: Oil field equipment supplier, Oil field equipment supplier | Visit Online: pstsme.com | Contact Number (Helpline): +974 4413 1090
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  • RabbanGabbroStorageArea(MesaieedPort)Career@pakqatarmanpower | Location details: Mesaieed Port، Mesaieed, Qatar | Classification: Stores and shopping, Stores and shopping | Visit Online: | Contact Number (Helpline):
  • DohaCablesManufacturingFacility | Location details: 25.004577, 51.567780, Mesaieed, Qatar | Classification: Manufacturer, Manufacturer | Visit Online: dohacables.com | Contact Number (Helpline): +974 4490 2191
  • QatarSteel,QPSC | Location details: Mesaieed Industry Rd, Mesaieed, Qatar | Classification: Steel construction company, Steel construction company | Visit Online: qatarsteel.com.qa | Contact Number (Helpline): +974 4477 8778
  • MesaieedHospital | Location details: 2H75+2R3, Mesaieed, Qatar | Classification: Hospital, Hospital | Visit Online: | Contact Number (Helpline): +974 7750 0532
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  • QatarChemicalCompany(QChem) | Location details: VGVR+JGC, Mesaieed, Qatar | Classification: Manufacturer, Manufacturer | Visit Online: qchem.com.qa | Contact Number (Helpline): +974 4484 7111
  • AlReemClub | Location details: XGWV+94X, Mesaieed, Qatar | Classification: Recreation center, Recreation center | Visit Online: alreemclub.com | Contact Number (Helpline): +974 4409 1501
  • EnergyPlusGeneralServicesW.L.L | Location details: XHJ4+CGW, Mesaieed, Qatar | Classification: Corporate office, Corporate office | Visit Online: energyplusss.com | Contact Number (Helpline): +974 4431 1996
  • QatalumAdminBuilding | Location details: Unnamed Road, Mesaieed, Qatar | Classification: Corporate office, Corporate office | Visit Online: qatalum.com | Contact Number (Helpline): +974 4403 1111
  • SealineBeach,AMurwabResort | Location details: Sealine Beach Rd, Mesaieed, Qatar | Classification: , | Visit Online: sealinebeachqatar.com | Contact Number (Helpline): +974 4021 4000
  • MesaieedInternationalPrimarySchool | Location details: Mesaieed, Qatar | Classification: School, School | Visit Online: mis.qp.qa | Contact Number (Helpline): +974 4014 5906
 courses in Mesaieed
The sea is bordered by vast streets, banks, hospitals, and marshes, which are saline grounds that still serve as a hen sanctuary, as well as a few buildings, including lodging for workers of enterprises and organizations such as Qatar Petroleum and Qatar Gas.

. The city is remarkable because of the presence of a hotel called Sealine Resort, which is frequented by Qataris on Thursdays, Fridays, and holidays. The "Mesaieed City Industry Department," a sub-department of Qatar Petroleum that was established in 1996, oversees Mesaieed and its commercial area. The Mesaieed Master Plan is a long term plan in 2006 to guide the city's development over a 25-year period from 2006 until 2030. 4 billion project was launched in 2010 to construct a major port strategically located near Mesaieed Industrial Area's port.

During the twentieth century, Mesaieed developed into a premier business district and oil storage hub, making it one of Qatar's most illustrious cities. It has guidelines about the distribution of land for public and private infrastructure, such as power, petrochemical industries, non-petrochemical industries, residential units, green belts, shipping and waste disposal. As part of the Qatari government's National Vision 2030, a $7.

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