Data Science Course In Telugu

Full Stack Data Science

Become a Data Scientist. This master-level course is for you if you are looking to learn Data Science in Telugu within a short time!

Note:Limited Seats Only
Course Details
3 Months
Course Duration
Telugu
Teaching Language
Offline
Mode Of Teaching
01 March
Start Date
Life Time
Content Access
Fee Stucture
₹11,999
Batch Timings
Monday to Friday
4:00 PM – 06:00 PM
Course Syllabus

In this Introduction, you will learn what will be taught in this course and benifits of this course.

  • What is Programming?
  • What is Coding?
  • Python Library
  • Python Modules
  • Python Webframework
  • Flavours of Python
  • What can Python do? 
  • Why Python? 
  • Python Syntax compared to other programming languages
  • Python Installation

  • The print statement
  • Comments
  • Python Data Structures & Data Types
  • String Operations in Python
  • Python keywords
  • Python Variables
  • Python Type Conversions
  • Simple Input & Output

  • Arithmetic operators
  • Assignment operators
  • Comparison operators &Logical operators
  • Identity operators
  • Membership operators & Output
  • Simple Output Formatting 

  • Indentation 
  • The If statement and its’ related statement 
  • An example with if and it’s related statement
  • Else
  • Nested If
  • Short Hand If
  • Short Hand If else & Continue
  • Examples for Conditional Statements

  • Indentation 
  • The for statement and its’ related statement 
  • An example with for and it’s related statement
  • While
  • Nested for
  • Nested While
  • Examples for Looping Statements

  • Indentation 
  • The Break statement and its’ related statement 
  • An example with Break and it’s related statement
  • Continue
  • Pass
  • Examples for Jumping Statements

  • String object basics
  • String methods
  • Splitting and Joining strings
  • String format functions

  • List object basics
  • List methods
  • List as Stack and Queues
  • List comprehensions

  • Introduction to Tuples
  • Tuples with built-in functions
  • Tuple operations

  • Introduction to Sets
  • Sets with built-in functions
  • Set Operations
  • Set with functions

  • Introduction to Dictionary
  • Dictionary with built-in functions
  • Dictionary with functions

  • Defining a function
  • Calling a function
  • return statement
  • Difference between return and print
  • Arguments
  • Parameters
  • Keyword arguments
  • Arbitrary argument
  • User defined functions
  • Nested functions
  • Functions with real time examples

  • Introduction to Classes
    • Creation of Classes
    • Real time examples of Classes
  • Creation of Objects
  • init
  • self keyword
  • super keyword
  • Inheritance
  • Types of Inheritance:
    • Single Inheritance.
    • Multiple Inheritance.
    • Multi-Level Inheritance.
    • Hierarchical Inheritance.
  • Polymorphism:
    • Method overloading
    • Method overriding
  • Encapsulation
    • Private
    • Public
    • Protected
  • Data Abstraction
    • Abc class
    • Abstract method
    • Realtime example of Data Abstraction

  • Introduction to File Handling
  • File modes
  • with keyword
  • Working with files
  • Reading and writing files
  • Buffered read and write
  • Other File methods

  • Using standard module
  • Creating new modules
  • Exceptions Handling with Try-except
  • Creating, inserting and retrieving table
  • Updating and deleting the data

  • Introduction to Numpy
  • Numpy Installation
  • NumPy – Ndarray Object
  • NumPy – Data Types
  • NumPy – Array Attributes
  • NumPy – Array Creation Routines
  • NumPy – Array from Existing Data
  • Array From Numerical Ranges
  • NumPy – Indexing & Slicing
  • NumPy – Advanced Indexing
  • NumPy – Broadcasting
  • NumPy – Iterating Over Array
  • NumPy – Array Manipulation
  • NumPy – Binary Operators
  • NumPy – String Functions
  • NumPy – Mathematical Functions
  • NumPy – Arithmetic Operations
  • NumPy – Statistical Functions
  • Sort, Search & Counting Functions
  • NumPy – Byte Swapping
  • NumPy – Copies & Views
  • NumPy – Matrix Library
  • NumPy – Linear Algebra

  • Introduction to Pandas
  • Pandas Installation
  • Python Pandas – Series
  • Python Pandas – DataFrame
  • Python Pandas – Panel
  • Python Pandas – Basic Functionality
  • Descriptive Statistics
  • Function Application
  • Python Pandas – Reindexing
  • Python Pandas – Iteration
  • Python Pandas – Sorting
  • Working with Text Data
  • Options & Customization
  • Indexing & Selecting Data
  • Statistical Functions
  • Python Pandas – Window Functions
  • Python Pandas – Date Functionality
  • Python Pandas – Timedelta
  • Python Pandas – Categorical Data
  • Python Pandas – Visualization
  • Python Pandas – IO Tools

  • Matplotlib

  • PowerBI Introduction
  • PowerBI Installation
  • PowerBI Query Editor
    • Introduction to PowerBI Query
    • Load
    • Transform
    • Extract
    • Data types and Filters in PowerBI Query
    • Inbuilt Column Transformations
    • In built Row Transformations
  • PowerBI Pivot table
  • Report
  • Table
  • Models
  • Visualization Charts
  • Fields
  • Analysis of Data
  • Creating Dashboards
  • Running Python Scripts
    Projects:
    • Data Visualization using PowerBI with Realtime Data sets

  • What is Multi Threading
  • Multi Threading vs Multi Processing
  • Thread class
  • Thread Life Cycle
  • Methods of Multi Threading in Python
  • Examples of MultiThreading

  • What is Web Scraping?
  • What is Beautifull Soap?
  • Request Module
  • Json Module
  • Saving Scraped Data

  • Split
  • Working with special characters, date, emails
  • Quantifiers
  • Match and find all
  • character sequence and substitute
  • Search method

  • what is Django?
  • PIP
  • Django installations
  • Django Creating Project
  • Django Creating application
  • Django Commands
  • Django settings.py
  • Django Views.py
  • Django urls.py
  • Django Templates
  • Django Models
  • Django Migrations
  • Blog Project using Django

  • Github Introduction
  • Account creation
  • Github Repository
  • Pushing Projects
  • Pulling Projects
  • ReadME File
  • Git Introduction
  • Git Installation
  • Git Clone
  • Git Status
  • Git Add
  • Git Commit
  • Git Push
  • Git Pull
  • Git vs Github

  • Descriptive and inferential Statistics
  • Sampling Methods
  • Types of Variables
  • Independent and dependent variables
  • Variable Measurement Scales
  • Frequency Distribution and Cumulative Frequency Distribution
  • Bar Graphs and Pie Charts
  • Histograms and stem & leaf plots
  • Arithmetic Mean for samples and populations
  • Central Tendency
  • Variance and Standard deviation for Population and sample
  • Percentiles and Quartiles
  • Inter Quartile Ranges and Box Plots
  • Outliers in data
  • Skewness for the data
  • The normal curves
  • Z-scores and z-test for the data

  • Basics of probability
  • Addition Rule
  • Multiplication Rule
  • Permutations
  • Combination
  • Discrete and Continuous Random Variables
  • Discrete probability distribution
  • Probability Histogram
  • Mean and Expected values of discrete random variables
  • Variance and standard deviation of discrete random variables

  • Binomial distribution
  • Normal distribution
  • Quadrants
  • Pearsons correlation
  • Hypothesis testing with Pearson’s r
  • Spearman correlation
  • Central Limit theorem
  • Sample proportions
  • Confidence intervals about the mean, population, standard deviation
  • NULL and alternative Hypotheses
  • Type I and Type II Errors
  • One-Tailed and Two-Tailed Tests

  • What is Data?
  • Difference between CPU and GPU
  • Parallel and sequence processors
  • How data will be arranged in the axis
  • Types of machine learnings
  • What is classification?
  • What is regression?
  • What is clustering?
  • Performance metrics
  • What are errors?
  • What are all the libraries in Machine Learning?
  • Knowing about Tensorflow, Keras, Scikit-Learn, etc.
  • Explorative Data Analysis
  • Bias and variance

  • Linear Regression Maths
  • Linear Regression building from scratch without libraries
  • Linear Regression Building with Libraries (Scikit Learn)
  • Maths for the Mean Squared Error, Squared Error, Absolute Squared Error.
  • Writing Code from scratch for Mean Squared error
  • Writing Code from scratch for Squared error
  • Writing Code from scratch for absolute Squared error
  • Logistic Regression Maths
  • Logistic Regression building from scratch without libraries
  • Logistic Regression Building with Libraries (Scikit Learn)
  • Maths for the Accuracy, Precision, Recall, F1-Score
  • Writing Code from scratch for Accuracy
  • Writing Code from scratch for Precision
  • Writing Code from scratch for Recall
  • Writing Code from scratch for F1-Score
  • Writing code for all the metrics using sklearn (MSE, SE, Accuracy, Precision, etc..)

  • Decision Tree Maths
    • Gini
    • Entropy
  • Building Decision Tree classifier using python
  • Random Forest Maths
  • Building Random Forest Maths
  • KNN classifier
  • KNN using python
  • SVM with maths
  • SVM building by using python
  • Voting classifier Maths
    • Harding Voting
    • Soft Voting
  • Building voting classifier with python
  • Bagging classifier with maths
  • Bagging classifier building with python
  • Ridge Regression with maths
  • Ridge Regression building with python
  • Lasso Regression with maths
  • Lasso Regression building with python
  • SVR with maths
  • SVR building with python
  • Decision Tree Regressor with Maths
  • Decision Tree with python

  • What is Perceptron?
  • Neurons in humans and AI?
  • What is a single layers perceptron?
  • Neural Networks
  • Hidden Layers
  • Weights and bias
  • Neural networks maths behind it
  • Tensorflow and Keras introduction
  • Building neural networks with TensorFlow
  • Activation functions
  • Gradient descent algorithms
  • Feedforward network
  • Backpropagation
  • Error and accuracy

  • CNN introduction
  • Convolutions introduction in humans and AI
  • Padding in CNN
  • Strides in CNN
  • Max pooling in CNN
  • Average pooling in CNN
  • Kernels
  • Features
  • Math behind CNN
  • Building CNN with TensorFlow and Keras
  • Training and Testing it

  • What is Corpus?
  • What are Tokens?
  • What are Engrams?
  • What is Tokenization?
    • What is White-space Tokenization?
    • What is Regular Expression Tokenization?
  • What is Normalization?
    • What is Stemming?
    • What is Lemmatization?
  • Part of Speech tags in NLP
  • Building NLP model with SVM
  • Maths behind RNN and LSTM
  • Working with RNN
  • Working with LSTM

  • MySQL Get Started
  • My SQL Installation
  • My SQL Work Bench
  • Data Defination Language
  • Data Manipulatioon Language
  • Data Control Language
  • Data Transaction Language
  • My SQL Connection With Python
  • MySQL Create Database
  • MySQL Create Table
  • MySQL Insert
  • MySQL Select
  • MySQL Where
  • MySQL Drop Table
  • MySQL Join
Projects
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Railway Tickets Reservation System

The management of booking, timetable, train, station, and fare details is the primary goal of the Python project on railway ticket reservation system. It oversees the management of all customer, fare, and booking information. Only the administrator is assured access because the project is entirely created on the administrative end.

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Bank Management System

A straightforward project created with Python is the Simple Banking System project. The project only includes the administrative side. All of the fundamental operations, such as opening a new account, viewing account holders' records, viewing withdrawal and deposit amounts, requesting balance information, etc., are managed by the admin side. gui can be created if necessary.

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Super Market Bill Generation

This project will introduce you to a programming task: This software project is a functionally enhanced version of a conventional grocery store billing system. This technology is designed to quickly process data and generate bills for grocery customers. For successful output, this project incorporates all python functions and OOPS ideas. The user may check the shopping products, pricing, and quantities with excessive ease.

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Medical Image Analysis

This project will introduce you to a programming task:Detect and classify diseases in medical images (e.g., X-rays, MRIs) using CNNs.

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Stock Market Forecasting

This project will introduce you to a programming task: Use time series analysis or recurrent neural networks (RNNs) to predict stock prices or market trends.

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News Article Classification

You will handle data from the client their customers are searching for the best Categorize news articles into predefined classes (e.g., sports, politics, entertainment) using natural language processing (NLP) techniques.

Tools Covered
What We Provide
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Frequently Asked Questions

The education offered by Python Life is industrial education. We are known for our course programs. And whatever we teach, it starts from scratch to advanced level. An experienced instructor is available to him 24/7 to clear any doubts.

Yes, all concepts are taught from basic to advanced level and the instructor will check if students understand before moving on to more subjects.

Of course, Python Life trains students according to industry requirements and specifications. We also conduct in-house planning and mock interviews.

There are no eligibility criteria for this course, which is taught from start to finish, so anyone interested in the course can participate.

Yes, you will receive a course completion certificate from Python Life when you submit your project at the end of the course.

Sorry, No refunds.

You can join by paying from our site. Immediately after payment, you will receive a confirmation from us to guide you through the further process.

Yes, all sessions will be recorded and will be provided for the students.