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Data Science Training

Data Science Training By Experts

Data Science Training in Kochi – By Nestsoft Technologies

Master the Art of Data-Driven Decision Making with Industry Experts

Are you ready to launch your career in one of the most in-demand fields of the 21st century? Nestsoft Technologies, the trusted name in IT education and career training since 1997, offers comprehensive Data Science training in Kochi tailored for students, working professionals, and aspiring analysts.

Why Choose Data Science?

Data Science is revolutionizing industries — from healthcare to e-commerce, finance to logistics. Companies are increasingly relying on data insights to make strategic decisions, improve operations, and innovate faster.

Career Opportunities After Data Science Course:

  • Data Analyst

  • Data Scientist

  • Business Intelligence Analyst

  • Machine Learning Engineer

  • AI Specialist

  • Data Engineer

What You Will Learn:

Our hands-on Data Science training course includes:

  • Python Programming for Data Science

  • Statistics and Probability

  • Data Wrangling & Data Cleaning

  • Exploratory Data Analysis (EDA)

  • Data Visualization using Matplotlib, Seaborn

  • Machine Learning Algorithms

  • Supervised & Unsupervised Learning

  • Introduction to Deep Learning

  • Working with Real-time Projects and Datasets

  • Resume building, interview training & career support

Course Highlights:

  • 100% practical-oriented sessions

  • Learn from expert data scientists with real-time industry exposure

  • Work on live projects and datasets

  • Certification upon course completion

  • Online and Offline Batches Available

  • Flexible timings for students and working professionals

Who Can Join?

  • Graduates / Postgraduates in Science, Engineering, Mathematics, or Statistics

  • Working professionals looking to switch careers

  • Freshers passionate about data and analytics

  • Entrepreneurs and business owners

Join the Top-Rated Data Science Training Program in Kochi!

Build a future-proof career in data science with the best trainers and a trusted institute. Enroll for a free trial class today and take the first step toward becoming a data expert.

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 learns 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.

Duration: 90 Hrs
Attend a Free Trial Class
Syllabus -> Data Science 


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