Data Science Training

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

What student say about us?

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Data Science Training