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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20+
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25+

Data Science Jobs in Nizwa

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

  • 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 Nizwa
Data Science Identify and collect data from data sources. This curriculum prepares you to work in a variety of Data Science professions and earn top-dollar wages. 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 Nizwa. 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. Effectively analyze both organized and unstructured data Create strategies to address company issues. You'll have a personal mentor who will keep track of your development. There are numerous reasons why you should take this course. 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. 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 Nizwa. .

List of All Courses & Internship by TechnoMaster

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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 Nizwa

  • LuLuHypermarket-Nizwa | Location details: Nizwa, Oman | Classification: Hypermarket, Hypermarket | Visit Online: luluhypermarket.com | Contact Number (Helpline): +968 25 415900
  • OmanCloudExpress-Karsha,Nizwa|عمانكلاوداكسبرس-نزوى،كرشا | Location details: Karsha industrial Nizwa OM, 611, Oman | Classification: Mobile phone repair shop, Mobile phone repair shop | Visit Online: | Contact Number (Helpline): +968 9383 8995
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  • IndianSchoolNizwa | Location details: ISN, Indian School CBSE Affiliation No: 6130008, P.O. Box: 598, P.C. 611, Thymsa, Nizwa, Oman | Classification: School, School | Visit Online: isnizwa.org | Contact Number (Helpline): +968 25 449286
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  • Aramex | Location details: Nizwa, Main Road , Farq area , Opp Oman-tell Near Hungry Punny Restaurant , Beside Jeep Showroom Block no. 31, Bld # 3, Nizwa, Oman | Classification: Logistics service, Logistics service | Visit Online: aramex.com | Contact Number (Helpline): +968 9251 6272
  • LuLuHypermarket-Nizwa | Location details: Nizwa, Oman | Classification: Hypermarket, Hypermarket | Visit Online: luluhypermarket.com | Contact Number (Helpline): +968 25 415900
  • OmanOilServiceStation-AlMaamurah | Location details: Saad District, Nizwa-Ibri Rd, Al, Bahla, Oman | Classification: Gas station, Gas station | Visit Online: oomco.com | Contact Number (Helpline): +968 800 76626
  • Omantel | Location details: Grand Mall, Nizwa, Oman | Classification: Telephone company, Telephone company | Visit Online: omantel.com | Contact Number (Helpline): +968 25 447770
  • DHLNizwa | Location details: Nr. Suq Signal Soal area Nizwa OM، 611،, Nizwa, Oman | Classification: Shipping company, Shipping company | Visit Online: dhl.com | Contact Number (Helpline): +968 9136 1407
  • WeatherfordNizwa | Location details: RFMQ+VJM، طيمسا، Oman | Classification: Engineer, Engineer | Visit Online: weatherford.com | Contact Number (Helpline):
  • ShumokhNizwaComputerSalesAndRepairs | Location details: Nizwa Souq_2, ولاية نزوى،،, Nizwa, Oman | Classification: Computer store, Computer store | Visit Online: | Contact Number (Helpline):
 courses in Nizwa
In reaction to those demands, the eye of the Ministry of Education in recent years has shifted farfar from worries approximately access (considering that ninety seven percentage of fundamental college-age Omanis are enrolled in schooling) closer to tries to qualitatively enhance and growth the relevance of the schooling device with the intention to put together our college students to fulfill the demanding situations of a knowledge-primarily based totally economic system. I am assured that the record show to be of massive advantage to the Sultanate in assisting it to enhance its instructional device in phrases of access, equity, high-satisfactory and efficiency. The united states of america is pursuing a improvement plan that specializes in monetary diversification as a way of decreasing its dependency at the oil quarter. To prosper withinside the worldwide marketplace, the Sultanate calls for an schooling device which could produce destiny personnel who can have interaction in analytical wondering and hassle fixing and who're creative, adaptable and aggressive. We are thankful for the persisted fruitful collaboration and help supplied through the World Bank and, as always, we're deeply appreciative of its contribution. The reforms emphasize converting teaching, gaining knowledge of and evaluation methodologies, updating the curriculum, including new resources, enhancing facilities, decreasing elegance sizes and upgrading the qualifications and skills of teachers. There have been over 43,000 teachers, of which 89 percentage have been Omani. To all intents and purposes, therefore, the united states of america became beginning with what became really a smooth slate in phrases of complete instructional provision. The Ministry of Education is currently engaged in some of predominant reform tasks across the complete device. In addition, globalization of the world economic system has introduced its very own strains, demanding situations and possibilities.

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