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Data Science with Python Training in Kochi

76,533 Ratings

Enroll in this Data Science with Python course in Kochi that focuses on covering the techniques of deployment of Python for data science using libraries SciPy, Matplotlib, and NumPy. The course content is curated by IITM experts and a dedicated support team is available 24/7 to resolve your doubts and assist you in becoming a certified data scientist with Python skills.

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Data Science with Python Course Key Highlights

50+ Live sessions across 7 months
218 Hrs Self-paced Videos
200 Hrs Project & Exercises
Learn from IIT Madras Faculty & Industry Practitioners
1:1 with Industry Mentors
Resume Preparation and LinkedIn Profile Review
24*7 Support
No Cost EMI Option
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Data Science with Python Course in Kochi Overview

What modules will you be taught in this Python for Data Science training in Kochi?

In this online data science with Python course in Kochi, you will learn about

  • Python for data science
  • OOP: Expressions and functions
  • Creating Pig and Hive UDFs
  • SQLite: Operations and classes
  • NumPy for mathematical computing
  • MapReduce programming by deploying Python
  • Dimensionality reduction
  • Time series forecasting

The following facts prove why joining this Data Science with Python course in Kochiis the right decision for your career:

  • 90+ Data Science job openings are currently available in Kochi alone – Indeed
  • The average income of a Data Scientist in Kochi is around₹5 lakhs per year – Glassdoor
  • Python’s design and libraries are 10 times more productive than C, C++, or Java – Journyx

According to AmbitionBox, the average salary for a Data Scientist in Kochi, India is ₹12.6 lakhs per year. This is based on data from over 80 Data Scientists in Kochi, across various industries.

Here is a more detailed breakdown of the salary ranges for Data Scientists in Kochi based on experience:

Experience Salary (per year)
Entry-level ₹8.7 lakhs – ₹9.2 lakhs
Mid-level ₹9.2 lakhs – ₹10.4 lakhs
Senior-level ₹10.4 lakhs – ₹23 lakhs

The data science with Python training in Kochi from IITM is suitable for:

  • Software Developers
  • Analytics Experts
  • BI Managers
  • Big Data Experts
  • Project Managers
  • ETL Professionals
  • Data Science and Python Aspirants

Register today and start your career as a Data Scientist with Python skills!

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Data Scientist: The Sexiest Job of the 21st Century - Harvard Business Review
Data really powers everything that we do. - By Jeff Weiner, CEO of LinkedIn

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55% Average Salary Hike

$1,20,000 Highest Salary

12000+ Career Transitions

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*Past record is no guarantee of future job prospects

Meet the Python Data Science Mentors

Skills Covered

Python

Data Science

Data Analysis

AI

GIT

MLOps

Data Wrangling

SQL

Story Telling

Machine Learning

Prediction algorithms

NLP

PySpark

Model

Data visualization

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Tools Covered

pyspark python jupyter Scipy numpy pandas matplotlib tensorflow SQL tableau excel git SparkSQL
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Data Science with Python Course Fees in Kochi

Self Paced Training

  • 218 Hrs Self-paced Videos e-learning videos
  • Resume Preparation and LinkedIn Profile Review
  • 24*7 Support

$176

Online Classroom Preferred

  • Everything in Self-Paced Learning, plus
  • 50+ Live sessions across 7 months of Instructor-led Training
  • One to one doubt resolution sessions
  • Attend as many batches as you want for Lifetime
  • Job Assistance
02 Mar

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

05 Mar

TUE - FRI

07:00 AM TO 09:00 AM IST (GMT +5:30)

09 Mar

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

17 Mar

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

$1,229 10% OFF Expires in

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  • Customized Learning
  • Enterprise grade learning management system (LMS)
  • 24x7 Support
  • Enterprise grade reporting

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Data Science with Python Course Syllabus

Live Course Industry Expert Academic Faculty

Module 1 – Preparatory Session - Linux and Python

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Python 

  • Introduction to Python and IDEs – The basics of the Python programming language, how you can use various IDEs for Python development like Jupyter, Pycharm, etc. 
  • Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, file handling, exception handling ,etc.
  • Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.
  • Hands-on Sessions And Assignments for Practice – The culmination of all the above concepts with real-world problem statements for better understanding. 

Linux

  • Introduction to Linux  – Establishing the fundamental knowledge of how Linux works and how you can begin with Linux OS. 
  • Linux Basics – File Handling, data extraction, etc.
  • Hands-on Sessions And Assignments for Practice – Strategically curated problem statements for you to start with Linux. 
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Module 2 – Data Wrangling with SQL

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SQL Basics – 

  • Fundamentals of Structured Query Language
  • SQL Tables, Joins, Variables 

Advanced SQL –  

  • SQL Functions, Subqueries, Rules, Views
  • Nested Queries, string functions, pattern matching
  • Mathematical functions, Date-time functions, etc. 

Deep Dive into User-Defined Functions

  • Types of UDFs, Inline table value, multi-statement table. 
  • Stored procedures, rank function, SQL ROLLUP, etc.

SQL Optimization and Performance

  • Record grouping, searching, sorting, etc. 
  • Clustered indexes, common table expressions.

Hands-on exercise: 

Writing comparison data between the past year and the present year with respect to top products, ignoring the redundant/junk data, identifying the meaningful data,  and identifying the demand in the future(using complex subqueries, functions, pattern matching concepts).

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Extract Transform Load

  • Web Scraping, Interacting with APIs

Data Handling with NumPy

  • NumPy Arrays, CRUD Operations, etc.
  • Linear Algebra – Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, Matrices.

Data Manipulation Using Pandas

  • Loading the data, data frames, series, CRUD operations, splitting the data, etc.

Data Preprocessing

  • Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, standardization, etc.
  • Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross-validation techniques, train-test split, etc.

Data Visualization

  • Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc. with Python matplotlib.
  • Regression plots, categorical plots, area plots, etc, with Python seaborn.
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Descriptive Statistics – 

  • Measure of central tendency, the measure of spread, five points summary, etc. 

Probability 

  • Probability Distributions, Bayes’ theorem, central limit theorem. 

Inferential Statistics –  

  • Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.
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Introduction to Machine Learning 

  • Supervised, Unsupervised Learning.
  • Introduction to scikit-learn, Keras, etc.

Regression 

  • Introduction classification problems, Identification of a regression problem, dependent and independent variables.
  • How to train the model in a regression problem.
  • How to evaluate the model for a regression problem.
  • How to optimize the efficiency of the regression model.

Classification 

  • Introduction to classification problems, Identification of a classification problem, and dependent and independent variables.
  • How to train the model in a classification problem.
  • How to evaluate the model for a classification problem.
  • How to optimize the efficiency of the classification model.

Clustering 

  • Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.
  • How to train the model in a clustering problem.
  • How to evaluate the model for a clustering problem.
  • How to optimize the efficiency of the clustering model.
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Supervised Learning 

  • Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.
  • Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.
  • Decision Tree – Creating decision tree models on classification problems in a tree-like format with optimal solutions.
  • Random Forest – Creating random forest models for classification problems in a supervised learning approach.
  • Support Vector Machine – SVM or support vector machines for regression and classification problems.
  • Gradient DescentThe gradient descent algorithm is an iterative optimization approach to finding the local minimum and maximum of a given function.
  • K-Nearest Neighbors – A simple algorithm that can be used for classification problems.
  • Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting.

Unsupervised Learning 

  • K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach.
  • Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation.
  • Linear Discriminant Analysis –  LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data.
  • Principal Component Analysis – PCA follows the same approach in handling multidimensional data.
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  • Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc.
  • Confusion matrix – To evaluate the true positive/negative, and false positive/negative outcomes in the model.
  • r2, adjusted r2, mean squared error, etc.
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Artificial Intelligence Basics 

  • Introduction to keras API and TensorFlow

Neural Networks

  • Neural networks
  • Multi-layered Neural Networks
  • Artificial Neural Networks 

Deep Learning

  • Introduction to Deep Learning (by Academic Faculty)
  • Deep neural networks
  • Convolutional Neural Networks 
  • Recurrent Neural Networks
  • GPU in deep learning
  • Autoencoders, restricted boltzmann machine 
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The Data Science capstone project focuses on establishing a strong hold of analyzing a problem and coming up with solutions based on insights from the data analysis perspective. The capstone project will help you master the following verticals: 

  • Extracting, loading and transforming data into usable format to gather insights. 
  • Data manipulation and handling to pre-process the data.
  • Feature engineering and scaling the data for various problem statements. 
  • Model selection and model building on various classification, regression problems using supervised/unsupervised machine learning algorithms.
  • Assessment and monitoring of the model created using the machine learning models.
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Electives:

Power BI Basics

  • Introduction to PowerBI, Use cases and BI Tools , Data Warehousing, Power BI components, Power BI Desktop, workflows and reports , Data Extraction with Power BI.
  • SaaS Connectors, Working with Azure SQL database, Python and R with Power BI
  • Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data ,M Query and Hierarchies in Power BI.

DAX 

  • Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features

Data Visualization with Analytics  

  • Slicers, filters, Drill Down Reports
  • Power BI Query, Q & A and Data Insights
  • Power BI Settings, Administration and Direct Connectivity
  • Embedded Power BI API and Power BI Mobile
  • Power BI Advance and Power BI Premium

Hands-on Exercise:

Creating a dashboard to depict actionable insights in sales data.

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  • Job Search Strategy
  • Resume Building
  • Linkedin Profile Creation
  • Interview Preparation Sessions by Industry Experts
  • Mock Interviews
  • Placement opportunities with 400+ hiring partners upon clearing the Placement Readiness Test.
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Data Science with Python Training Projects

Peer Learning

Via Intellipaat PeerChat, you can interact with your peers across all classes and batches and even our alumni. Collaborate on projects, share job referrals & interview experiences, compete with the best, make new friends — the possibilities are endless and our community has something for everyone!

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Data Science with Python Certification in Kochi

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Note: All certificate images are for illustrative purposes only and may be subject to change at the discretion of the IITM Pravartak.

How can I get Data Science with Python Certification in Kochi?

To get the Data Science and AI certification from IITM Pravartak, you are required to complete all the assignments successfully, and project work provided as part of this program. You will be mentored by IIT Madras faculty and learn from them in live classes.

  • Once the course is completed, receive certification in Data Science with Python from IITM Pravartak

Yes. Intellipaat’s Python Data Science certification is totally worth it. Data science is one of the most sought-after career opportunities today. There are various reasons for this:

  • According to Indeed, job postings for Data Scientists with Python skills increased by 56% between 2018 and 2021. This trend has continued in 2024, with job postings for Data Scientists with Python skills increasing by an additional 25% in the past year.
  • According to a recent survey by Kaggle, Python is the most popular programming language among Data Scientists, with 83% of respondents reporting that they use Python regularly.
  • According to PayScale, Data Scientists with Python skills earn an average of 10% more than those without Python skills.

Once you complete the Data Science Python course and clear the examination, you will receive the certificate through our LMS which has lifetime validity and is well-recognized by leading organizations across the world.

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Data Science with Python Training FAQs

What is Python for data science?

It is an open-source, high-level, interpreted programming language that offers an excellent approach to object-oriented programming. It is one of the most popular languages used by data scientists for a variety of projects and applications. This programming language has a lot of features for dealing with arithmetic, statistics, and scientific functions which will be helpful for data science-related tasks.

This language has always been a highly preferred choice of data scientists due to its well-known features such as simplicity, scalability, numerous libraries, and large community, among other things. It offers hundreds of libraries and frameworks most of which are focused on data analytics, data visualizations, machine learning, and many more, making it ideal for data science projects.

Yes, Intellipaat publishes various blogs on data science and Python. Among them, the major ones are Python for Data Science Tutorials, Data Science Interview Questions, and Python Interview Questions.

Intellipaat is offering 24/7 query resolution, and you can raise a ticket with the dedicated support team at any time. You can avail of email support for all your queries. If your query does not get resolved through email, we can also arrange one-on-one sessions with our support team. However, 1:1 session support is provided for a period of 6 months from the start date of your course.

Intellipaat provides placement assistance to all learners who have successfully completed the training and moved to the placement pool after clearing the PRT( Placement Readiness Test) More than 500+ top MNC’s and startups hire Intellipaat learners. Our Alumni works with Google, Microsoft, Amazon, Sony, Ericsson, TCS, Mu Sigma, etc.

Apparently, no. Our job assistance is aimed at helping you land in your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.

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