Data Science Course in Chennai

30 LPA Highest Offer

Intellipaat helped me acquire a solid job in my third year of B.Tech. I received seven job offers, with 30 LPA as the highest CTC. Thanks to Intellipaat for making my career successful.

Fresher

Data Engineer

Gaurav Saboo

Senior Technical Associate | India

Job with a salary hike

I got promoted to senior technical associate with a 60% hike after completing this course from Intellipaat. The training taught me new skills and how to implement them in my current role.

Technical Analyst

Senior Technical Associate

Melwin Rodrigues

Data Scientist | India

Job as Data Scientist

Becoming a data scientist after working as a customer service agent was possible only due to expert guidance from Intellipaat trainers. Even after working for 10 years in customer care, I am a data scientist today.

Customer Service Agent

Data Scientist

D Rajesh Sahoo

Machine Learning Engineer | India

Civil Engg. to ML Engg.

Intellipaat made my transition from a civil engineering graduate to an IT industry professional very smooth with its round-the-clock support, excellent curriculum, industry-standard projects, and efficient trainers.

B. Tech — Civil Engineering

Machine Learning Engineer

Ankit Kumar

Data Scientist | India

Career Transition Handbook

Non-tech to Tech

Intellipaat has given me the confidence that anyone who aspires can become a data scientist because of the rich course and expert guidance. I managed to switch from a non-tech education background to becoming a data scientist.

B.com Graduate

Data Scientist

Mahesh Chowdary

Data Scientist | Hyderabad

Non-tech to Tech

Today, I am a certified data scientist, and this happened after I enrolled in this program. It covers all the necessary concepts and techniques, in lucid language. I am thankful to Intellipaat for giving me this chance at career growth. Read More

Aircraft Maintenance Engineer

Data Scientist

Jeanette Masso

Big Data Developer | USA

Good salary hike

The course helped me make a career transition from computer technical specialist to big data developer with a 60% hike. The online interactive sessions hosted by the trainers are the best thing about Intellipaat.

Computer Technical Specialist

Big Data Developer

Pratik Kumar

Product Manager | India

85% Salary Hike

With this course, I switched to a product manager role with an 85% hike. They conducted many interviews, made us complete live capstone projects, and provided many resume-building strategies and all of this gave me confidence.

Marketing Manager

Product Manager

Prosenjeet Saha

Data Scientist | Bengaluru

Manager to Data Scientist

Intellipaat offered me the best learning experience, and today, I have successfully transitioned from a manager role in Edutech to a data scientist. The comprehensive curriculum and the career guidance sessions helped me a lot.

Manager Analytics & Data Science

Data Scientist

Sahas Barangale

Program Manager | Pune

Consultant to Program Manager

Thanks to Intellipaat, I was able to switch to the role of program manager from that of a Microsoft dynamics consultant. Gaining knowledge of the latest technologies as per industry standards helped me the most.

Microsoft Dynamics Consultant

Program Manager

Bhawna Kaliraman

Data Science Intern | Gurugram

Lecturer to Data Scientist

I was able to achieve my goal of becoming a data science professional after having been a lecturer, thanks to Intellipaat. It offered a platform to learn data science from the best trainers. The real-life projects were good.

Lecturer

Data Science Intern

55% Average Salary Hike

$1,20,000 The Highest Salary

12000+ Career Transitions

500+ Hiring Partners

*Past record is no guarantee of future job prospects

Data Scientist Course Content

Live Course Self Paced Industry Expert Academic Faculty

Module 1 – Preparatory Session - Linux and Python

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.

Module 2 – Data Wrangling with SQL

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).

Module 3 – Python With Data Science

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.

Module 4 – Linear Algebra and Advanced Statistics

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.

Module 5 – Machine Learning

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.

Module 6 – Supervised and Unsupervised Learning in ML

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 Descent – Gradient descent algorithm that 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 the multidimensional data.

Module 7 – Performance Metrics

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.

Module 8 – Deep Learning Using TensorFlow

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

Module 9 – Data Science Capstone Project

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.

Module 10 - Business Case Studies

Recommendation Engine – The case study will guide you through various processes and techniques in machine learning to build a recommendation engine that can be used for movie recommendations, restaurant recommendations, book recommendations, etc.
Rating Predictions – This text classification and sentiment analysis case study will guide you towards working with text data and building efficient machine learning models that can predict ratings, sentiments, etc.
Census – Using predictive modeling techniques on the census data, you will be able to create actionable insights for a given population and create machine learning models that will predict or classify various features like total population, user income, etc.
Housing – This real estate case study will guide you towards real world problems, where a culmination of multiple features will guide you towards creating a predictive model to predict housing prices.
Object Detection – A much more advanced yet simple case study that will guide you toward making a machine learning model that can detect objects in real-time.
Stock Market Analysis – Using historical stock market data, you will learn about how feature engineering and feature selection can provide you with some really helpful and actionable insights for specific stocks.
Banking Problem – A classification problem that predicts consumer behavior based on various features using machine learning models.
AI Chatbot – Using the NLTK python library, you will be able to apply machine learning algorithms and create an AI chatbot.

Electives:

Module 11 – Power BI

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.

Module 12 – Deploying Machine Learning Models With Cloud

Introduction to MLOps

MLOps lifecycle
MLOps pipeline
MLOps Components, Processes, etc

Deploying Machine Learning Models

Introduction to Azure Machine Learning
Deploying Machine Learning Models using Azure

Job Readiness

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.

Module 13 – Data Analysis With MS-Excel

Excel Fundamentals

Reading the Data, Referencing in formulae , Name Range, Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering
Working with Charts in Excel, Pivot Table, Dashboards, Data And File Security
VBA Macros, Ranges and Worksheet in VBA
IF conditions, loops, Debugging, etc.

Excel For Data Analytics

Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc.

Data Visualization with Excel

Charts, Pie charts, Scatter and bubble charts
Bar charts, Column charts, Line charts, Maps
Multiples: A set of charts with the same axes, Matrices, Cards, Tiles

Excel Power Tools

Power Pivot, Power Query and Power View

Classification Problems using Excel

Binary Classification Problems, Confusion Matrix, AUC and ROC curve
Multiple Classification Problems

Information Measure in Excel

Probability, Entropy, Dependence
Mutual Information

Regression Problems Using Excel

Standardization, Normalization, Probability Distributions
Inferential Statistics, Hypothesis Testing, ANOVA, Covariance, Correlation
Linear Regression, Logistic Regression, Error in regression, Information Gain using Regression

Hands-on Exercise:

Classification problem using excel on sales data, and statistical tests on various samples from the population.