Data Science Training/Course 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+
50+
25+

Data Science Jobs in Bangalore

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

  • 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 Bangalore
Data Science To find trends and patterns, use algorithms and modules. Cleaning and validating data to ensure that it is accurate and consistent. Identify and collect data from data sources. 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. This curriculum prepares you to work in a variety of Data Science professions and earn top-dollar wages. Today's Data Scientists must possess a wide range of abilities, including the ability to work with large amounts of data, parse that data, and translate it into an easily comprehensible format from which business insights may be drawn. Experts provide immersive online instructor-led seminars. . The Data Science Process, Communicating with Stakeholders, Software Engineering Practices, Object-Oriented Programming, Web Development, ETL Pipelines, Natural Language Processing, Machine Learning Pipelines, Experiment Design, Statistical Concerns of Experimentation, A/B Testing, and Introduction to Recommendation Engines are some of the topics covered in. To succeed as a data scientist, you must, nevertheless, make a particular effort to apply soft skills.

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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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The Bangalore City Corporation, with current extensions of city limits, has approximately 224 rectangular kilometres below its manipulate. The examine unearths that first of all the principle cause for the economic increase in Bangalore changed into to be located in the sturdy business way of life of the region, courting returned to the sooner a part of the 20 th century and later, through authorities owned electronics and telecommunication industries that have been based withinside the town without delay after the country's independence (in 1947). The empirical evaluation changed into carried out at tm, levels. Philip Amis, Valli and Viswanath. com While responsibilties for all mistakes continue to be with the author, he would love to thank the following folks who're a part of this studies in Bangalore or who gave valuable comments: Bhuvaneswari, Manohar, Rajan, Dr. Electronics industries persisted to base themselves throughout the 1 960s and thru the I 980s withinside the town. Block 6th. Three studies hypotheses were examined on this studies examine. This paper indicates that an knowledge of poverty in towns together with Bangalore (frequently called India`s Silicon Valley) calls for greater atten- tion to the governance procedures wherein specific organizations compete for public invest- ments and guide. However, while the Indian financial coverage changed into hberalised from the mid-Eighties and greater perceptibly after 1991, it changed into the lnfomiation Technology industries that commenced to set up themselves in Bangalore basically to faucet the available expert skills, and to utilize the town's current base as a distinguished centre for excessive era industries.

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