Full Stack Data Science Program

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 Available

Note: Enrollments are closed for June batch,
July batch enrollments will begin soon

Data Science in Telugu
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Course Details

3 Months

Course Duration


Teaching Language


Mode Of Teaching

July 04, 2022

Start Date

Life Time

Content Access

Fee Stucture


Including GST

Note: Fee will increase after this batch.

Batch Timings

Monday to Friday

07:00 PM – 09:00 PM

Course Syllabus

In this Introduction, you will view the course syllabus to learn what will be taught in this course. You will hear from data science professionals to discover what data science is, what data scientists do, and what tools and algorithms data scientists use on a daily basis. Finally, you will find out why data science is considered the sexiest job in the 21st century.

  • What can Python do? 
  • Why Python? 
  • Python Syntax compared to other programming languages
  • Python Install
  • VS Code Installation
  • VS Code Setup
  • The print statement
  • Comments
  • Python Data Structures & Data Types
  • String Operations in Python
  • Simple Input & Output
  • Simple Output Formatting 
  • Operators in python
  • Indentation 
  • The If statement and its’ related statement 
  • An example with if and it’s related statement
  • The while loop
  • The for loop
  • The range statement
  • Break & Continue
  • Pass
  • Examples for looping
  • 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
  • NumPy – Ndarray Object
  • NumPy – Data Types
  • NumPy – Array Attributes
  • NumPy – Array Creation Routines
  • 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 – Copies & Views
  • NumPy – Matrix Library
  • NumPy – Linear Algebra
  • 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
  • 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
    • 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
  • Real-time projects
  • 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
  • Flask
  • Flask introduction
  • Flask application
  • Open link flask
  • App routing flask
  • URL building flask
  • Http methods flask
  • Templates flask
  • Flask project: portfolio
  • Account creation
  • Pushing Projects
  • Pulling Projects
  • ReadME File
  • 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
  • Creating DB
  • Creating tables
  • Select 
  • Where Clause
  • Having Clause  
  • Operators
  • Aggregate functions
  • Joints
    • Inner
    • Outer
    • Left 
    • Right


Data Statistical Analysis

Here, you will handle data from the client where their customers are searching for the best house for a low cost Here, you need to do all the analytics on the dataset and help your client suggest a good area for their customers.

Vehicle Insurance

Our client is an Insurance company that has provided Health Insurance to its customers now they need your help in building a model to predict whether the policyholders (customers) from past year will also be interested in Vehicle Insurance provided by the company.

CarDekho Dataset

Car dekho is a Indian second hand car selling company, now it was looking for data scientist who can build a regression model and help them to find the car price accurately.

Fetal Health Classification

Classify fetal health in order to prevent child and maternal mortality. Reduction of child mortality is reflected in several of the United Nations’ Sustainable Development Goals and is a key indicator of human progress.


The lymphatic system is a network of vessels that transports a clear fluid called lymph around your body. The lymphatic system also includes glands (called lymph nodes) and organs. Lymphangiography is an imaging technique used to provide precise information on the extent and location of lymph vessels and lymph nodes.

Corona Lungs Dataset

Here is the good project, there was a patient that he needs to know he got effected with corona or not by seeing their lungs images. You are as data scientist need to solve the problem for that patient by taking their images and need test them and show them the result.

Sentimental analysis on Financial dataset

This dataset contains the sentiments for financial news headlines from the perspective of a retail investor, we are as a data scientist we need to help this company to create sentimental machine learning model. It will take text as an input and return output as “Negative or positive or Neutral”

Model Deployment for Wine Quality data

The main reason for this project out client who is working with wine we need to deploy his project on web because everyone need to use his website and should help them to know the wine quality

S3 BUCKET to Sage Maker

Hey data scientist you’re going to work in a company that which uses a AWS cloud platform to train the model, here you’re going to work on AWS cloud perform your company stores data in S3 bucket and use sage maker to train the models

Tools Covered


What We Provide

Online Community - Codingrad

Online Community

Although our instructors are available 24/7 to clear doubts, we also provide a community where students can post their doubts and also help each other.

LMS - Codingrad

Learning Platform

Along with daily live lectures we provide a separate learning platform where our students can learn topics, attempt quizzes, assignments and many more.

Certificate - Codingrad


Right after completion of the course by submitting the required projects and maintaining attendance, students will receive the course completion certificate. 

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Frequently Asked Questions

The training which CodinGrad provides is Industrial training. We are well known for our course curriculum. And whatever we teach is starts from scratch to advance level. Our well experienced instructors will be available for 24/7 to clear your doubts.

Yes, all the concepts are taught from the basics to the advanced level and our instructors will make sure that the students are understanding or not before going to any futher topics.

Of course, We at CodinGrad train the students according to the industry needs and specification, We also provide in-house projects and mock interviews.

We don’t have any eligibility criteria for our courses as we teach from start to end, thus anybody interested in the course can join.

Yes, you will be receiving a course completion certification from CodinGrad after submitting the projects at the end of the course.

You can enroll by doing the payment from our website and right after payment you will receive the confirmation from our end and we’ll guide for further process.

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