Data Science Online Live Classes 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 Data Science Syllabus
Course Fees
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Data Science Jobs in Hyderabad

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

  • 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 jobs in Hyderabad

Data Science Internship/Course Details

Data Science 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. Cleaning and validating data to ensure that it is accurate and consistent. To find trends and patterns, use algorithms and modules. 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 Hyderabad. To succeed as a data scientist, you must, nevertheless, make a particular effort to apply soft skills. Identify and collect data from data sources. Exercises, tasks, and projects that are completed in real-time 24 hours a day, 7 days a week, A large network of like-minded newbies, an industry-recognized intellipaat credential, and individualized employment support Several data scientist responsibilities are listed below. Effectively analyze both organized and unstructured data Create strategies to address company issues. . This finest Data Science course was built with the needs of businesses in mind when it comes to the field of Data Science.

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 Hyderabad
1%) of faculties at the side of standalone institutions9 (75. The effect of kingdom coverage, funding, regulatory framework, instructional requirements and governance on attaining those 5 dimensions of schooling gadget might be the point of interest of studies research. The kingdom coverage of social and inclusive improvement in realising the developmental desires in popular and expectancies its humans particularly is some thing that the kingdom has to attempt for. In this recognize the universalising primary schooling become one of the 8 desires of Millennium Development Goals (MDGs) and carried forwarded withinside the post-2015 improvement schedule. The technical schooling in India at some stage in the British regime had began at some stage in the primary 1/2 of nineteenth Century with business schools4 which had been typically connected to ordinance factories. Human capital encompassing instructional stages and skills, determined to be an crucial supply of financial boom at the side of the conventional factors, labour and capital (see Shultz, 1961; 1964; Dennison, 1967; Baro, 1997; Baro and Sala-i-Martin, 2004). Research research are to be thematically prepared round 5 dimensions or foundational pillars of schooling gadget: Access, Quality, Equity, Affordability and Accountability. The institutional framework includes vital and kingdom universities, deemed and/or personal universities, establishments of country wide significance, and self sustaining and non-self sustaining or affiliated faculties. Over a period, a number of the colleges started out supplying engineering schooling or courses. Its instrumental position is remodeling the destiny staff into professional one and growing the labour productiveness and growing the incomes capacities of individuals.

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