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Job Oriented Data Science Data Analyst Data Scientist AI Engineer  Course in Bangalore

Job Oriented

Data Science Data Analyst Data Scientist AI Engineer 

Course in Bangalore

Enroll at Indras Academy for Data Science course in Bangalore. Acquire skills that open doors to high-paying jobs in top companies.
Elevate your career with us!
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When you choose us, you embark on a transformative learning journey that opens the doors to a world of opportunities.

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One of our standout features is our unwavering support in helping you achieve the prestigious NASSCOM certification.

Recorded Session

we offer recorded sessions, allowing you to catch up on missed classes and review material at your convenience.

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Your success is our success. We provide dedicated placement support to help you secure the job you've been dreaming of.

Mock interviews

We conduct mock interviews to help you prepare and gain valuable feedback, making sure you're well-prepared.

Guaranteed Interview Calls

We partner with companies and provide you with opportunities to interview directly with them.

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Our resume building sessions help you create a professional and compelling resume that gets noticed by recruiters.

Our Courses

Our Programmes

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Find Out Why You Should Learn

Data science Syllabus

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Topic 1 : Programming Basics & Environment Setup

  • Installing Anaconda Anaconda Basics and Introduction
  • Get familiar with version control, Git and GitHub.
  • Basic Github Commands.
  • Introduction to the Jupyter Notebook environment.
  • Basics Jupyter notebook Commands.
  • Programming language basics.


Topic 2 : Strings, Decisions & Loop Control

  • Working With Numbers, Booleans and Strings, String types and formatting, String operations
  • Simple if Statement, if-else Statement
  • if-elif Statement.
  • Introduction to while Loops, for
  • Loops, Using continue and break


Topic 3 : Python Data Types

  • List, Tuples, Dictionaries
  • Python Lists, Tuples, Dictionaries
  • Accessing Values, Basic Operations
  • Indexing, Slicing, and Matrixes
  • Built-in Functions & Methods
  • Exercises on List, Tuples And Dictionary


Topic 4 : Functions And Modules

  • Introduction To Functions
  • Defining & Calling Functions
  • Functions With Multiple Arguments
  • Anonymous Functions - Lambda
  • Using Built-In Modules, User-Defined
  • Modules, Module Namespaces,
  • Iterators And Generators


Topic 5 : File I/O An d Exceptional Handling and Regular Expression

  • Opening and Closing Files, open Function, file Object Attributes, close() Method, Read, write, seek.
  • Exception Handling, try-finally Clause
  • Raising an Exceptions, User-Defined Exceptions
  • Regular Expression- Search and Replace
  • Regular Expression Modifiers
  • Regular Expression Patterns


Topic 6 : Data Analysis Using Numpy

  • Introduction to Numpy. Array Creation, Printing Arrays, Basic Operation - Indexing, Slicing and Iterating, Shape Manipulation - Changing shape, stacking and splitting of array
  • Vector stacking, Broadcasting with Numpy, Numpy for Statistical Operation


Topic 7 : Data Analysis Using Pandas

  • Pandas : Introduction to Pandas
  • Importing data into Python
  • Pandas Data Frames, Indexing Data Frames ,Basic Operations With Data frame, Renaming Columns, Subsetting and filtering a data frame.


Topic 8 : Data Visualization using Seaborn

  • Seaborn: Intro to Seaborn And Visualizing statistical relationships, Import and Prepare data. Plotting with categorical data and Visualizing linear relationships.
  • Seaborn Exercise


Exercises

  • 3 Case Study on Numpy, Pandas
  • 1 Case Study on Pandas And Seaborn
Topic 1 : Introduction to Statistics

  • Variable and its types
  • Quantitative, Categorical, Discrete, Continuous
  • Outliers, Causes of Outliers, How to treat Outliers, I-QR Method and ZScore Method


Topic 2 : Fundamentals of Math and Probability

  • Probability distributed function & cumulative distribution function.
  • Conditional Probability, Baye's Theorem
  • Problem solving for probability assignments
  • Random Experiments, Mutually Exclusive Events, Joint Events, Dependent & Independent Events


Topic 3 : Inferential Statistics

  • Central Limit Theorem
  • Point estimate and Interval estimate
  • Creating confidence interval for population parameter


Topic 4 : Descriptive Statistics

  • Measures of Central Tendency – Mean, Median and Mode
  • Measures of Dispersion – Standard Deviation, Variance, Range, IQR (Inter-Quartile Range)
  • Measure of Symmetricity/ Shape – Skewness and Kurtosis


Topic 5 : Inferential Statistics

  • Characteristics of Z-distribution and T-Distribution.
  • Type of test and rejection region
  • Type of errors in Hypothesis Testing


Topic 6 : Hypothesis Testing

  • Type of test and Rejection Region
  • Type o errors-Type 1 Errors, Type 2 Errors. P value method, Z score Method. The Chi-Square Test of Independence.
  • Regression. Factorial Analysis of Variance. Pearson Correlation Coefficients in Depth. Statistical Significance
  • Null and Alternative Hypothesis Onetailed and Two-tailed Tests, Critical Value, Rejection region, Inference based on Critical Value
  • Binomial Distribution: Assumptions of Binomial Distribution, Normal Distribution, Properties of Normal Distribution, Z table, Empirical Rule of Normal Distribution & Central Limit Theorem and its Applications
  • Definition, Examples, Importance of Machine Learning
  • Definition of ML Elements: Algorithm, Model, Predictor Variable, Response
  • Variable, Training - Test Split, Steps in Machine Learning,
  • ML Models Type: Supervised Learning, Unsupervised Learning and Reinforcement Learning


Topic 1 : Data Preprocessing

  • Types of Missing values (MCAR, MAR, MNAR), Methods to handle missing values
  • Outliers, Methods to handle outliers: IQR Method, Z Method
  • Feature Scaling: Definition , Methods: Absolute Maximum Scaling, Min-MaxScaler, Normalization, Standardization, Robust Scaling


Topic 2 : Logistic Regression Model

  • Definition. Why is it called the “Regression model”?
  • Sigmoid Function, Transformation & Graph of Sigmoid Function


Topic 3 : Evaluation Metrics for Classification

  • Misclassification, TPR, FPR, TNR, Precision, Recall, F1 Score, ROC Curve,and AUC. Using Python library Sklearn to create the Logistic Regression Model and evaluate the model created model


Topic 4 : Decision Tree Model

  • Definition, Basic Terminologies, Tree Splitting Constraints, Splitting
  • Splitting Methods:
    - GINI, Entropy, Chi-Square, and Reduction in Variance
  • Using Python library Sklearn to create the Decision Tree Model and evaluate the model created


Topic 5 : Random Forest Model

  • Ensemble Techniques:
    Bagging/bootstrapping & Boosting.
  • Definition of Random Forest, OOB Score
  • K-Fold Cross-Validation


Topic 6 : Naive Baye’s Model

  • Definition, Advantages, Baye's Theorem Applicability, Disadvantages of Naive Baye's Model, Laplace's Correction, Types of Classifiers: Gaussian, Multinomial and Bernoulli
  • Using Python library Sklearn to create the Naive Baye's Model and evaluatethe model created


Topic 7 : K Means and Hierarchical Clustering

  • Definition of Clustering, Use cases of Clustering
  • K Means Clustering Algorithm,Assumptions of K Means Clustering
  • Sum of Squares Curve or Elbow Curve


Topic 8 : Machine Learning Exercises

  • Business Case Study for Kart Model
  • Business Case Study for Random Forest
  • Business Case Study for SVM
  • Business Case Study for Linear Regression
  • Business Case Study for Logistic Regression
  • Business Case Study for KMean Cluster
Topic 1 : Introduction to Time Series Forecasting

  • Basics of Time Series Analysis and Forecasting
  • Method Selection in Forecasting
  • Moving Average (MA) Forecast Example
  • Different Components of Time Series Data
  • Log Based Differencing, Linear Regression for Detrending


Topic 2 : Introduction to ARIMA Models

  • ARIMA Model Calculations, Manual ARIMA Parameter Selection
  • ARIMA with Explanatory Variables
  • Understanding Multivariate Time Series and their Structure
  • Checking for Stationarity and Differencing the MTS
Topic 1 : Natural Language Processing

  • Text Analytics
  • Introduction to NLP
  • Use cases of NLP algorithms
  • NLP Libraries
  • Need for Textual Analytics
  • Applications of NLP
  • Word Frequency Algorithms for NLP Sentiment Analysis


Topic 2 : Text Analysis

  • Distance Algorithms used in Text Analytics
  • String Similarity
  • Cosine Similarity Mechanism
  • The similarity between two text documents
  • Levenshtein distance - measuring the difference between two sequences


Topic 3 : Understanding Keras API for implementing Neural Networks

  • Information Retrieval Systems
  • Information Retrieval - Precision,
  • Recall,F- score TF-IDF
  • KNN for document retrieval
  • K-Means for document retrieval
  • Clustering for document retrieval


Topic 4 : Text Pre Processing Techniques

  • Need for Pre-Processing
  • Various methods to Process the Textdata
  • Tokenization, Challenges inTokenization
  • Stopping, Stop Word Removal


Topic 5 : Stemming

  • Stemming - Errors in Stemming
  • Types of Stemming Algorithms - Table
  • Lookup Approach
  • N-Gram Stemmers


Topic 6 : Use cases on NLP

  • Sentiment Analysis
  • Content summarization
Topic 1 : RDBMS And SQL Operations

  • Introduction To RDBMS
  • Single Table Queries - SELECT, WHERE,ORDER BY, Distinct, And, OR
  • Multiple Table Queries: INNER, SELF,CROSS, and OUTER, Join, Left Join, Right Join, Full Join, Union
Topic 1 : Introduction to Tableau

  • Connecting to data source
  • Creating dashboard pages
  • How to create calculated columns
  • Different charts
  • Hands-on :
    -Hands on on connecting data source and data cleansing
    -Hands on various charts


Topic 2 : Visual Analytics

  • Getting Started With Visual Analytics
  • Sorting and grouping
  • Working with sets, set action
  • Filters: Ways to filter, Interactive Filters
  • Forecasting and Clustering
  • Hands-on :
    - Hands on deployment of Predictive
    - model in visualization


Topic 3 : Dashboard and Stories

  • Working in Views with Dashboards and Stories
  • Working with Sheets
  • Fitting Sheets
  • Legends and Quick Filters
  • Tiled and Floating Layout
  • Floating Objects
Download Detailed Syllabus
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Students

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Students

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Students
Find Out Why You Should Learn

Data science Syllabus

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tempor incididunt ut labore et dolore magna aliqua
Module 1

Python

  1. Programming Basics & Environment Setup
  2. Strings, Decisions & Loop Control
  3. Python Data Types
  4. Functions And Modules
  5. File I/O An d Exceptional Handling and Regular Expression
  6. Data Analysis Using Numpy
  7. Data Analysis Using Pandas
  8. Data Visualization using Seaborn
Module 2

Statistics

  1. Introduction to Statistics
  2. Fundamentals of Math and Probability
  3. Inferential Statistics
  4. Descriptive Statistics
  5. Inferential Statistics
  6. Hypothesis Testing
Module 3

Machine Learning

  1. Data Preprocessing
  2. Logistic Regression Model
  3. Evaluation Metrics for Classification
  4. Decision Tree Model
  5. Random Forest Model
  6. Naive Baye’s Model
  7. K Means and Hierarchical Clustering
  8. Machine Learning Exercises
Module 4

Time Series

  1. Introduction to Time Series Forecasting
  2. Introduction to ARIMA Models
Module 5

Natural Language Processing

  1. Natural Language Processing
  2. Text Analysis
  3. Understanding Keras API for implementing Neural Networks
  4. Text Pre Processing Techniques
  5. Stemming
  6. Use cases on NLP
Module 6

SQL

  1. RDBMS And SQL Operations
Module 7

TABLEAU

  1. Introduction to Tableau
  2. Visual Analytics
  3. Dashboard and Stories
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