Leading Institute in Mysore for Data Science Training & Placement

  • Enroll in our 100% Job Placement focused Data Science course in Mysore with Industry-Ready Skills & a Proven Placement Framework, designed to help you get hired faster. Master in-demand skills using latest tools and technologies through hands-on projects and expert mentorship—all at reasonable fees. Book Your FREE DEMO today and take the first step toward a successful tech career. Inquire Now.

  • About Our AI-Powered Data Science & Analytics Course -

    Learn from basics of Python to advanced A.I. and Analytics modules with projects to become a Data Science Professional in short duration.

  • 100% Placement Support -

    End-to-end placement support with early placement initiation during training (eligibility-based), continuous expert guidance and a simple placement process powered by our dedicated placement team and strong hiring network of 1000+ companies to help you get the best job opportunities and placement faster. Check company list below.

  • Job Opportunities -

    Data Science Engineer, Machine Learning Engineer, Data Analyst etc.

  • Pre-requisites -

    None. Any one from IT & non IT can learn.

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  • Related Course - Adv. Data Science Training Program (JG)

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Syllabus

Data Science & Anaytics Course
  • Oracle Installation
  • Basics
    • Oracle Basics
    • Database models
    • ER Model Overview
    • Data types
    • Understanding Test Database
    • Basics Queries
    • Arithmetic and String functions
    • DML Operations - Insert, Update and Delete
    • FETCH command - Top N, Top % Rows
  • DDL
    • Tables - Create, Alter, Drop
    • Indexes - Types, Create, Drop
    • Constraints - Not Null, Primary Key, Unique Key, Foreign Key
    • Sequence
    • Synonyms
  • Foreign Keys
  • DML operations - Insert, Update & Delete
    • Insert, Update, Delete & Truncate
    • Common Operations
    • Creating Tables using queries
    • Bulk Data Inserts using Queries
  • Writing Queries
  • Filters
    • DISTINCT
    • BETWEEN
    • IN
    • LIKE
    • NOT
    • NULL
    • AND, OR, NOT etc
    • Using Complex Data Filtering Techniques
  • Sorting
    • Ascending Order
    • Descending Order
    • Complex Sorting
  • Functions
    • String Functions - lower case, uppercase, substring, instring, trim etc.
    • Number Functions
    • Date Manipulation
    • Null Value Functions - NVL, NVL2, NULLIF, COALESCE
    • Conditional Expressions - Case and Decode
  • Groups
    • Basics
    • Grouping functions - AVG, MIN, MAX, COUNT, DISTINCT COUNT etc
    • Grouping Filters - HAVING
    • Rollups & Cubes
  • Joins
    • Cartesian Product
    • Equi and Non-Equi Joins
    • Left Outer Join
    • Right Outer Join
    • Full Outer Join
    • Self Join
    • Complex Queries - Joins & Groups Integration
  • SET Operators
    • Union
    • Union All
    • Intersect
    • Minus
  • Subquery
    • Single Valued Row Sub Query
    • Multi Valued Sub Query
  • Views
  • Inline Views
  • ROWID & ROWNUM
  • TOP N Analysis using Inline Views
  • Analytic (Window) Functions
    • Top N Analysis
    • Over() with summary functions
    • Partition By Queries
    • ROW_NUMBER()
    • RANK()
    • DENSE_RANK()
  • Transactions ( T SQL)
    • Commit
    • Rollback
    • Savepoint
  • DCL - Grant & Revoke
  • Project
  • Introduction to Python
    • What is Python? Features & uses
    • Python installation & IDE setup (VS Code, PyCharm, Jupyter, Anaconda)
    • Running Python programs (script mode vs interactive mode)
    • First program: print("Hello, World!")
  • Python Basics
    • Variables & Data Types (int, float, str, bool)
    • Input/Output functions (print(), input())
    • Comments & indentation
    • Type conversion & type() function
  • Operators
    • Arithmetic operators (+, -, *, /, //, %, **)
    • Comparison operators (==, !=, <, >, <=, >=)
    • Logical operators (and, or, not)
    • Assignment operators (+=, -=, etc.)
    • Identity operators (is, is not)
    • Membership operators (in, not in)
  • Control Flow
    • if, elif, else statements
    • Nested conditions
    • for and while loops
    • break, continue, pass
  • Data Structures
    • Strings → indexing, slicing, methods
    • Lists → create, access, update, iterate, list methods
    • Tuples → immutable collections
    • Sets → unique values, set operations (union, intersection)
    • Dictionaries → key-value pairs, methods
  • Functions
    • Defining & calling functions
    • Function arguments (positional, keyword, default, variable-length *args, **kwargs)
    • Return values
    • Lambda (anonymous functions)
    • Scope (local vs global variables)
  • Modules & Packages
    • Importing built-in modules (math, random, datetime, os)
    • Creating your own modules
    • Installing external libraries with pip
  • File Handling
    • Opening & closing files
    • Reading & writing (read(), write(), with open)
    • Working with CSV and JSON files
  • Exception Handling
    • try, except blocks
    • finally, else in exceptions
    • Raising exceptions (raise)
    • Custom exceptions
  • Object-Oriented Programming (OOP)
    • Classes & objects
    • Constructors (__init__)
    • Instance & class variables
    • Methods (instance, class, static)
    • Inheritance (single, multiple, multilevel)
    • Method overriding
    • Encapsulation & Abstraction
    • Polymorphism
    • Special methods (__str__, __len__, etc.)
  • Advanced Python Concepts
    • Iterators & Generators (iter(), next(), yield)
    • Decorators (function decorators, @staticmethod, @classmethod)
    • Regular Expressions (re module)
  • Working with APIs
    • Sending HTTP requests with requests
    • JSON handling
  • Database Connectivity
    • Installation
    • CRUD Operations (create, read, insert, update, delete)
    • MySQL with connectors
  • Python for Automation
    • Automating tasks (file renaming, web scraping with BeautifulSoup)
    • Working with Excel (openpyxl, pandas)
  • Python for Data Science
    • NumPy → arrays, vectorized operations
    • Pandas → DataFrames, Series, CSV/Excel handling
  • Python for Data Visualization
    • Installing Matplotlib (pip install matplotlib)
    • Basic Plots: Line chart, Bar chart, Pie chart
    • Statistical Plots: Histogram, Scatter plot, Box plot
  • Project
  • Mean, Median, Mode, Variance, Standard Deviation
  • Skewness, Kurtosis, Correlation, Covariance
  • Probability Distributions: Normal, Binomial, Poisson
  • Sampling, Central Limit Theorem
  • Hypothesis Testing: t-test, z-test, ANOVA, chi-square
  • Confidence Intervals, p-values, Type I & II errors
  • Note – Statistics part will be taken along with ML & AI as and when required
  • Data Preprocessing & EDA Overview
  • Handling missing values, outliers, duplicates
  • Encoding (One-hot, Label), Scaling (Standard, MinMax)
  • Feature engineering, selection techniques
  • EDA: Univariate, Bivariate, Multivariate analysis
  • Visuals: Heatmaps, Pairplots, Boxplots, Violin plots
  • Supervised Learning
    • Linear & Logistic Regression
    • Decision Tree, Random Forest, KNN
    • SVM with kernel functions
    • Naive Bayes, AdaBoost, XGBoost
    • Project 1: House Price Prediction
    • Project 2: Email Spam Detection
  • Unsupervised Learning
    • K-Means, DBSCAN, Hierarchical Clustering
    • Dimensionality Reduction: PCA
    • Project 3: Employee Role Segmentation
  • Model Evaluation & Optimization
    • Metrics: Accuracy, Precision, Recall, F1, AUC, R2, RMSE
    • Cross Validation, Stratified K-Fold
    • Hyperparameter tuning: GridSearchCV, RandomizedSearchCV
    • Imbalanced Data Handling: SMOTE, NearMiss
  • Time Series Analysis Basics
  • Trend, Seasonality, Noise, Stationarity
  • ADF Test, ARIMA, SARIMA
  • Facebook Prophet, Exponential Smoothing
  • Project 4: Sales Forecasting
  • NLP Basics
  • Text preprocessing: Tokenization, Lemmatization, Stopwords
  • BoW, TF-IDF, Word2Vec
  • Text Classification, Sentiment Analysis
  • Project 5: Sentiment Analysis of Product Reviews
  • Artificial Neural Networks (ANN)
    • Perceptron, Multilayer Perceptron
    • Forward & Backward Propagation
    • Activation, Loss functions, Optimizers
    • Overfitting, Regularization, Batch Normalization
    • Project 6: Iris Flower Classification using ANN
  • Computer Vision (CNNs for Image Processing)
    • Convolution, Pooling, Padding
    • Transfer Learning (VGG, ResNet)
    • R-CNN, Faster R-CNN
    • Project 7: Handwritten Digit Recognition
  • RNNs & LSTMs
    • RNN, LSTM, GRU, Bidirectional RNN
    • Sequence Modeling, Time Series, Text Generation
    • Project 8: Next Word Prediction using RNN & LSTM
  • Attention Mechanism, Transformer Architecture
  • Using Pre-trained Models (BERT, RoBERTa)
  • Embedding generation & fine-tuning
  • Project 9: Sentiment Analysis using BERT (Transformers)
  • GenAI
    • What is GenAI? Prompt Engineering
    • Use cases: Q&A, Summarization, Image Generation
    • Ethical concerns & safety
  • LLM Apps with OpenAI & LangChain
    • Using ollama
    • LangChain Pipelines, FAISS, ChromaDB
    • Chat with PDF, CSV, Excel
    • Project 10: Flow Stack Machine Learning Project
  • Web Deployment: Streamlit or Flask
  • Create AWS Instance
  • Model Deployment on AWS
  • Note - this module will be mostly online as trainers aare rare.
  • Introduction to Power BI
    • What is Power BI and Why Power BI
    • Installing Power BI Desktop
    • Exploring the Power BI Workflow
    • Adjusting the settings of the Power BI Desktop
    • Comparison of Power BI vs Other Reporting Tools
  • Getting and Transforming Data with Power BI Desktop
    • Connecting to different sources
    • Connecting to multiple sources
    • Different connecting Options(DirectQuery vs Import Data Vs Live Connection)
    • Shaping and transforming data with Power Query
    • Editing,Merging,Appending queries etc
  • Advance Importing techniques without coding
  • Data cleaning & Advance data cleaning
  • Modeling with Power BI
    • Introduction to Modeling
    • Building Relational Models(Setup and Manage Relationships)
    • Creating table relationships
    • Understanding the filter flow
    • Cardinality and Cross filtering
  • DAX
    • Understanding Dax Syntax
    • Calculated Columns vs Measures
    • Common Dax functions and formulae
    • Understanding the evaluation context in DAX
  • Visualising Data with Reports
    • Creating Visualisations
    • Color & Conditional Formatting
    • Setting Sort Order
    • Scatter & Bubble Charts & Play Axis
    • Tooltips
    • Slicers, Timeline Slicers & Sync Slicers
    • Cross Filtering and Highlighting
    • Visual, Page and Report Level Filters
    • Drill Down/Up
    • Hierarchies
    • Constant Lines
    • Tables, Matrices & Table Conditional Formatting
    • KPI's, Cards & Gauges
    • Map Visualisations
    • Custom Visuals
    • Managing and Arranging
    • Drillthrough
    • Custom Report Themes
    • Grouping and Binning
    • Bookmarks & Buttons
  • Introduction to Power BI Service
    • Introduction to the Power BI Service
    • Quick Tour of Power BI Service
    • Connecting to Data from Power BI service
    • Building Blocks of Power BI Service
  • Sharing and Collaboration Tools in Power BI Service
    • Sharing and Collaboration Options Overview
    • Publish from Power BI Desktop
    • Publish Reports to Web
    • Printing and Exporting from Power BI Service
    • Sharing Reports & Dashboards
  • Power BI Refreshing Datasets
    • What is refreshing?
    • Implementation
  • A.I. in Power BI
  • Advance Report techniques
  • Power Automate (Latest)
    • Introduction
    • installation
    • Types of flows
    • Actions
    • Copy multiple excel, pdf files from one folder
    • Merge PDF files together, Rename etc.
    • Extract phones name and price from amazon
  • Project
  • Best Practices
  • Git & GitHub Basics
    • What is Git?
    • What is GitHub?
    • Difference between Git and GitHub
    • Local Repository vs Remote Repository
    • Initializing a Git project (git init)
    • Cloning a repository (git clone)
    • Staging changes (git add)
    • Commit changes (git commit -m)
    • Push code to GitHub (git push)
    • Pull latest code (git pull)
  • Popular AI Tools & AI Assistants
    • ChatGPT
    • Gemini
    • Claude
    • Perplexity
    • Free vs Paid AI Tools
    • Choosing the Right Tool
  • Prompt Engineering Fundamentals
    • What is Prompt Engineering?
    • Components of a Good Prompt
    • Basic Prompting
    • Role-Based Prompting
    • Common Prompt Mistakes
    • Improving AI Output Quality
  • AI for Technical Tasks
  • 7 to 7.5 Months (without Addon)
  • 8 Months (with Addon)
  • For Faster Job-Readiness - Early completion option in 5 months is available. Please connect with counsellors for more info.
  • Customized Training Available
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100% Proven Placement Framework


 
A placement-focused framework designed around current hiring requirements. It includes early placement initiation, end-to-end placement support, easy guidelines and continued expert guidance through our dedicated placement team, backed by our strong network of 1000+ companies to help you get hired faster.

Outcome:
Students are frequently placed during their ongoing training or within just 30 days of completion.

Recent Placements:
2.4 LPA, 3 LPA, 5 LPA, 5.1 LPA, 8.2 LPA & more

Few Placed Students

[check company names in institute]
Recent Top Placement - 8.2 LPA
Successfully Placing Students Since 2014
You can be the Next
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1000+ Companies

(few mentioned below)

  • Company names are hidden, to restrict others compete our students. Full list with company names available in the institute.
  • This is a combined list for all courses because companies work in multiple technologies. Therefore, lots of job opportunities.
    • Tr….. Global Consulting Pvt. Ltd.
    • Pu…... Solutions India Private Limited
    • Ga…... Technologies Pvt. Ltd.
    • Pi... Technology
    • Em... Technologies Pvt. Ltd.
    • Ndim........... Solutions Pvt Ltd 
    • Star Union …......
    • Hum…......... Technologies Pvt. Ltd
    • Neo…... Pvt Ltd
    • Lo…... Solutions Private Limited
    • Co…...... Solution
    • Ve…...... Systems Pvt.Ltd
    • Shriya …............. Solutions, Pvt. Ltd
    • Val….......... Technologies Pvt Ltd
    • Tr…..... Technologies
    • Mae…....... Infotech Ltd.
    • Hu…. Systems Private Limited
    • Ve…. Solutions Pvt Ltd
    • Capgemini
    • Lio…......... Technologies
    • Elec…...... India Pvt Ltd (R & D Center)
    •  Int…...t Bizware Services Pvt .Ltd
    • Ne…..n Software Technologies
    • Car….. Innovations Pvt. Ltd
    • AT…. INDIA
    • Big…. Technologies Pvt. Ltd.
    • Biz….... Solutions
    • D... Consultants
    • eC….. Services Ltd
    • Ema…......... Technologies
    • In…. HR Pvt Ltd.
    • Ne…......t Design - Website Development
    • U….t Technologies
    • R…....d Technologies
    • Bl…............ Systems Infotech Pvt. Ltd.
    • Ne….. Solution Pvt Ltd
    • Con…....... Software & Systems
    • Quo…....... - A Technology Company
    • AX... Technologies Pvt Ltd
    • ANALYTIC…....... SOFTWARES PRIVATE.
    • Hi…...... Infotech Services
    • In…........ Business Solutions Pvt Ltd
    • In…............. Knowledge Solutions Pvt Ltd
    • Ge…..... Healthcare Solution
    • Cre…...... India Pvt Ltd
    • Qu…...... Intelligence Pvt Ltd
    • VE…... ALT…. INDIA PRIVATE LIMITED
    • Max….... Technologies Pvt .Ltd
    • Min…....... Software Technologies Pvt. Ltd
    • Ne…...... Systems Ltd
    • Quality Ki…...
    • Mso….. Solutions
    • Sarla …............ Pvt. Ltd
    • S….n …...... Technologies Pvt. Ltd. 
    • R... Analytics
    • Tark….......a Technologies
    • Sy…......s Solutions
    • Co…. Consultancy Services Pvt Ltd
    • Chem…............... technologies
    • Atos Syntel
    • Le…............ Consulting Pvt Ltd
    • NTT DATA
    • SA… Technologies Private Limited
    • Ora…....... Solutions Pvt ltd
    • T…......nect Media Services
    • SYS….....E INFOTECH
    • MU…................AAR PVT LTD
    • BLO…..........EMS PRIVATE LIMITED
    • Allied…............... Pvt. Ltd.
    • Pres…......... Digital India Pvt. Ltd.
    • Aim…..... Softech Pvt. Ltd.
    • Red…........ Pharmtech Pvt. Ltd.
    • Suthe….......
    • Es…...... Comp…............ Pvt Ltd.
    • He….................. Technologies India Private Limited
    • …. 1000+ Companies ...check full list in institute
    Dedicated Placement Team
    + Early Placement Initiation

    Outcome - Lots of students placed in ongoing training or within 30 days of completion. 2.8 LPA, 3 LPA, 3.4 LPA, 3.7 LPA, 4 LPA, 4.5 LPA, 5 LPA, 6 LPA, 8.2 LPA and more.

    You can be the Next Placed Student


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