Data Science Course

Data Science Course in Mumbai

Built for Mumbai's dynamic job market, our program equips you with every specific skill the industry demands. Join our data science course in Mumbai and get ready to succeed in the city's booming tech and business sectors!

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15,213
Students Enrolled

4.8
Google Reviews

6 Months
Duration

220+ Hours
of Learining

Job
Readiness

Course Overview

Mumbai:
An Excellent Option for Data Science Training

Mumbai, India’s financial capital, is home to tech-driven industries and corporate headquarters. Choose data science training in Mumbai to unlock networking opportunities and shape your tech future.

Our Course Highlights

220+ Hours of Learning

In-depth instructor-led training covering essential concepts

Jumbo Pass

Access multiple trainers and attend unlimited live online batches for 365 days.

25+ Assignments

Solidify your understanding with real-world scenarios and case studies

18 Guided Projects

Get hands-on with industry-relevant projects with expert guidance

Post-Training Support

Ongoing assistance, even for your assignments and projects

6 Value-Added Courses

Tableau, MySQL, AI, Big Data Tools, Basics of R, ChatGPT

Placement Prep

Placement Assistance with 5000+ Hiring Partners

Learning Path

Tools and Technologies

Python

Pandas

Numpy

Scikit Learn

Tableau

Apache Spark

MySQL

Azure

ChatGPT

Skills covered

Statistics

Data Analytics

Data Visualization

Machine Learning Algorithms

Ensemble techniques

Text Mining/NLP

Forecasting analytics

LLM & ChatGPT

Course Curriculum

Data Science

  • Data Types
  • Measure Of central tendency
  • Measures of Dispersion
  • Graphical Techniques
  • Skewness & Kurtosis
  • Box Plot.
  • Random Variable
  • Probability
  • Probability Distribution
  • Normal Distribution
  • SND
  • Expected Value
  • Sampling Funnel
  • Sampling Variation
  • Central Limit Theorem
  • Confidence interval
  • Introduction to Hypothesis Testing
  • Hypothesis Testing ( 2 proportion test, 2 t sample t test)
  • Anova and Chi Square
  • Principles of Regression
  • Intro to Simple Linear Regression
  • Multiple Linear Regression
  • Logistic Regression
  • Data Cleaning
  • Imputation Techniques
  • Data analysis and Visualization
  • Scatter Diagram
  • Correlation Analysis
  • Transformations
  • Encoding Methods - OHE, Label Encoders,Outlier detection-Isolation Forest and Calculating the Predictive Power Score (PPS)
  • Clustering introduction
  • Hierarchical clustering
  • K Means
  • DBSCAN
  • PCA
  • Association Rules
  • Recommender System
  • Python Model Deployment
  • Regression Tasks / Classification Tasks
  • Decision Tree
  • KNN
  • Support Vector Machines
  • Feature Engineering (Tree based methods, RFE,PCA)
  • Model Validation Methods (train-test,CV,Shuffle CV, and Accuracy methods)
  • Lasso and Ridge Regressions
  • ANN
  • Optimization Algorithm(Gradient descent)
  • Stochastic gradient descent(intro)
  • Back Propagation method
  • Introduction to CNN
  • Bagging and Random Forest
  • Boosting
  • XGBM
  • LGBM
  • Introduction to Text Mining
  • VSM
  • Intro to word embeddings
  • Word clouds and Document Similarity using cosine similarity
  • Named Entity Recognition
  • Text classification using Naive Bayes
  • Emotion Mining
  • Introduction to Time Series
  • Level
  • Trend and Seasonality
  • Strategy
  • Scatter plot
  • Lag plot
  • ACF
  • Principles of Visualization
  • Naive forecasts
  • Forecasting Error and it metrics
  • Model Based Approaches
  • AR Model for errors
  • Data driven approaches
  • MA
  • Exponential Smoothing
  • ARIMA

Core Python

  • Python Introduction- Programing Cycle of Python,PythonIDE and Jupyter Notebook
  • Variables
  • DataType
  • Github
  • HackerRank
  • CodeWars and Sanfoundry Account Creation Number
  • String
  • List
  • Tuple
  • Dictionary
  • Operator-Arithmetic
  • Comparison
  • Decision Making-Loops
  • While Loop
  • For Loop and Nested Loop
  • Number Type Conversion-int(), long().Float()
  • Strings-EscapeChar
  • String Special Operator
  • String Formatting Operator
  • Python List
    • Accessing values in list
    • Delete list elements
    • Indexing, Slicing & Matrices
  • Tuples
    • Accessing values in Tuples
    • Delete Tuples elements
    • Indexing
    • Slicing & Matrices
  • Dictionary
    • Accessing Values from Dictionary
    • Deleting and Updating Elements in Dict
    • Properties of Dist
    • Built-In Dist Functions & Methods
    • Dict Comprehension
  • Function
    • Define Function
    • Calling Function
    • Pass by Reference as Value
    • Function Arguments
    • Anonymous Functions
    • Return Statements
  • Scope of Variables
    • Local & Global
    • Decorators and Recursion
    • Import Statements
  • Locating Modules
    • Current Directory
    • Python path
    • Dir() Function
    • Global and Location Functions & Reload() Functions
    • Sys Module and Subprocess Module
    • Packages in Python
  • Files in Python
    • Reading Keyboard Input
    • Input Function
    • Opening and Closing Files
    • Syntax and List of Modes
    • Files Object Attribute Open,Close.
    • Reading and Writing Files
    • File Position Directories Mkdir Method
    • Chdir() Method
    • Getcwd Method
    • Rmdir
  • Exception Handling
    • List of Exceptions
    • TryandException
  • OOP Concepts, Class, Objects, Inheritance, Overriding Methods like __init__, Overloading Operators, Data Hiding
  • Match Function
  • Search Function
  • Matching Vs Searching
  • Regular Exp Modifiers and Patterns
  • Database Connectivity
  • Methods
    • MySQL
    • Oracle
    • How to Install MySQL
    • DB Connection

Tableau

  • What is Tableau ?
  • What is Data Visualization ?
  • Tableau Products
  • Tableau Desktop Variations
  • Tableau File Extensions
  • Data Types
  • Dimensions
  • Measures
  • Aggregation concept
  • Tableau Desktop Installation
  • Data Source Overview
  • Live Vs Extract
  • Bar Chart
  • Pi-Chart
  • Heat Maps
  • Histogram
  • Maps
  • Scatterplot
  • Donut Chart
  • Waterfall Chart etc..
  • Dual axis
  • Blended axis
  • Dimension Filter
  • Measure Filter
  • Data Source Filter
  • Extract Filter
  • Context Filter
  • Quick Filter
  • Basic Calculations
  • Table Calculations
  • Quick Table Calculations
  • LOD's
  • KPI's
  • Joins
  • Relationship
  • Data Blending
  • Union
  • Hierarchy
  • Group
  • Sets
  • Parameters
  • Reference Lines
  • Trend Line
  • Forecasting
  • Clustering
  • Dashboard Objects
  • Dashboard Actions
  • Tableau Public website

MySQL

  • Introduction to Databases
  • Introduction to RDBMS
  • Different types of RDBMS
  • Software Installation(MySQL Workbench)
  • Data Definition language
  • Data Manipulation Language
  • Data Query Language
  • Transactional Control Language
  • Data Control Language
  • SELECT
  • LIMIT
  • DISTINCT
  • WHERE
  • AND
  • OR
  • IN
  • NOT IN
  • BETWEEN
  • EXIST
  • ISNULL
  • IS NOT NULL
  • WILD CARDS
  • ORDER BY
  • GROUP BY
  • HAVING
  • COUNT
  • SUM
  • AVG
  • MIN
  • MAX
  • COUNT
  • String Functions
  • Date & Time Function
  • NOT NULL
  • UNIQUE
  • CHECK
  • DEFAULT
  • ENUM
  • Primary key
  • Foreign Key (Both at column level and table level)
  • Inner
  • Left
  • Right
  • Cross
  • Self Joins
  • Full outer join
  • Index
  • View
  • Sub-query
  • Window Functions
  • Stored Procedures
  • Exception Handling
  • Loops
  • Cursor
  • Triggers

Artificial Intelligence

  • Introduction
  • DeepLearningImportance[Strength & Limitation]
  • SP | MLP Neural Network Overview
  • Neural Network Representation Activation Function
  • Loss Function Importance of Non-LinearActivation Function
  • Gradient Descent for NeuralNetwork
  • Train
  • Test & Validation Set
  • Vanishing &ExplodingGradient
  • Dropout Regularization
  • OptimizationAlgo
  • LearningRate
  • Tuning
  • Softmax
  • CNN
  • Deep Convolution Model
  • Detection Algorithm
  • CNN FaceRecognition
  • RNN
  • LSTM
  • BiDirectionalLSTM

Big Data Tools

  • Introduction to BigData, Challenges in Big Data and Workarounds| Introduction to Hadoop and Its Components|HadoopComponents andHands-On|Understand the Map Reduce and ItsDrawbacks
  • Introduction to Spark and DataBricks|Spark Components, Spark MLlib Spark &DataBricks andHands-On One ML Model in Spark
  • Cloud Computing
  • Azure Cloud Platform
  • Cloud Applications
  • Cloud Services
  • Open AI Studio

Basics of R

  • Data Structures & Operators in R|Conditional Statement|Decision Making|Loops|Strings|Functions|How to Import Data set in R| Programming Statistical Graphics

ChatGPT

  • Introduction to ChatGPT and AI
  • Types of AI and ChatGPT architecture
  • ChatGPT Functionalities and Applications
  • ChatGPT Prompt Engineering

Career Progression and Salary Trends:

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Frequently Asked Questions

Our data science course in Mumbai spans over 6 months with 220+ hours of immersive learning. We’ve designed it to balance in-depth knowledge with hands-on experience, so you’ll be industry-ready by the end of it!
You can choose from two flexible options-classroom training or live online sessions. Both provide interactive learning experiences, so you can pick what fits best with your schedule and preferences!
No worries! We’ve got you covered with recorded sessions available for all classes. You can catch up anytime and continue learning at your own pace.
Absolutely! Our data scientist course in Mumbai is designed for all levels, including freshers. Whether you're just starting out or transitioning into data science, we guide you through every step.
Definitely! This data science training in Mumbai is perfect for professionals looking to shift into data-related roles. We’ll help you gain the skills needed to make that smooth transition into the data science field.
Yes, you can! We start with the basics of coding, so even if you have no prior experience, we’ll guide you through learning essential programming skills as part of the course.
Yes, we offer placement assistance with 5000+ hiring partners! Our dedicated team will help you with job preparation, interview coaching, and connecting you to top companies.

Data Science

Bangalore, Hyderabad, Mumbai, Pune

Data Analyst

Bangalore, Hyderabad, Mumbai, Pune

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