Full Stack Data Science

Full Stack Data Science

Full Stack Data Science

Program In Telugu

Transform your career with our comprehensive data science course. Learn the latest techniques in data analysis, machine learning, and statistical modeling. Our practical approach provides hands-on experience with real-world projects. Start your journey today and become a skilled data scientist in demand across all industries.

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Course Details

Program Fee: ₹6,500

₹13,000 (50% OFF)

September 25, 2023

Start Date

3 Months Live

Course Duration

Online

Mode Of Teaching

Telugu

Teaching Language

07:00 PM to 09:00 PM

Batch Timings

Mon - Saturday

Weekday Sessions

Course Syllabus

Master the Skills Employers Demand with Our Comprehensive Data Science Course Syllabus

Introduction to Data Engineering, Data Science and Data Analytics
  • Data
  • Data Engineering
  • Data Science
  • Data Analytics
Introduction to Programming Languages, Tools, Frameworks
  • Python
  • TensorFlow - Keras
  • PyTorch Models
  • Hadoop/Spark – SparkSQL, PySpark, Hive.
Core Python
  • Interpretation vs Compilation
  • Python Installation
  • Virtual Environment
  • JVM and Byte Code
  • Python VM
  • Jupyter Notebook
Basics of Python
  • Variables,
  • Data Types
  • Constants
Strings & Formatted Print
  • String functions
  • Print Function
Lists
  • List Functions
  • Scratch List Operations
Tuples & Sets
  • Tuple Functions
  • Difference between List & Set
  • Set Functions
Dictionaries
  • Dictionary functions
  • JSON vs Dictionary
Loops
  • For Loop
  • For Each Loop
  • While Loop
Control Structures
  • If
  • If - Elif-Else
  • Nested If-Elif-Else
Reading Data
  • input() function
  • Type Casting
Keywords
  • pass
  • del
  • break
  • continue
Functions
  • Function definition
  • Function Parameters
  • Default Arguments
  • Assignment - 1
Advanced Functions
  • Positional Arguments
  • Keyword Arguments
  • Variable Arguments
  • Lambda
  • map
  • filter
  • reduce
Complex Functions
  • Decorators
  • Generators
Python packages
  • Inbuilt packages
  • PIP Package manager
  • Installing new packages
  • User defined packages
File Handling
  • Opening files
  • Reading files
  • Writing files
  • Appending files
  • Closing files
Exception Handling
  • try block
  • exception block
  • final block
  • else block
  • user defined exceptions
Introduction to OOPs
  • General Scenario in OOPs
  • Class & Object
  • Encapsulation & Abstraction
  • Inheritance
  • Polymorphism
  • Assignment - 2
NumPy
  • Importance of NumPy
  • NumPy Arrays
  • 1D, 2D, 3D, ND arrays
  • Vectorization
  • Stacking Arrays
  • Linear Algebra
  • Assignment - 3
OpenCV
  • Importance of OpenCV
  • Image Processing functions
  • Assignment - 4
Pandas
  • Importance of Pandas
  • DataFrame
  • Series
  • Loading Dataset
  • Summarization
  • Data Filtering
  • Assignment - 6
SQL
  • Database Management System
  • Constraints
  • NOT NULL
  • UNIQUE
  • PRIMARY KEY
  • DEFAULT
  • CHECK
  • FOREIGN KEY
  • Exercise Queries
  • Assignment - 7
Github
  • Repository
  • Local Repository
  • Centralized Repository
  • Branches
  • Git Commandline
Statistics
  • Descriptive Statistics
  • Visualization using Python
  • Matplotlib/Seaborn
  • Summarizing Data
  • Distributions of Data
  • Inferential Statistics
  • Regression Analysis
  • Mathematical Expression for Regression Analysis
Probability
  • Hypothesis Space
  • Random Variable
  • Discrete Distribution
  • Continuous Distribution
Introduction to Machine Learning
  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Clustering
  • Semi Supervised Machine Learning
  • Reinforcement Learning
Supervised Machine Learning
  • Linear Regression
  • Logistic Regression
  • K-NN methods
  • Decision Trees
  • Support Vector Machines
Artificial Neural Networks
  • Motivating Example
  • Perceptron/Neuron
  • Logistic Regression as NN
  • Computational Graph and Back Propagation
  • Single Layer NN
  • Multi Layer NN
  • Deep NN
ML Strategies
  • Train/Dev/Test
  • Bias & Variance
  • Hyper Parameters
  • Regularization
  • Dropout & Inverted Dropout
  • Normalization
  • Vanishing & Exploding Gradients
  • Weight Initialization
  • Optimization
  • Batching Dataset
  • Mini Batch Gradient Descent
  • Exponential Weighted Averages
  • Gradient Descent with momentum
  • RMS Prop
  • Adam
  • Learning Rate Decay
  • Finetuning Hyper Parameters
  • Batch Normalization & Covariate Shift
  • Softmax and Loss function
  • Callbacks
  • Model Evaluation
Computer Vision
  • Motivating Example
  • Convolution Operation
  • Convolution Operations Exercise
  • Pooling
  • State Of Art CNN architectures
  • ResNets
  • Inception Nets/ Google Nets
  • Mobile Nets
  • Efficient Nets
  • Transfer Learning
  • Object Detection
  • Motivating Example
  • Sliding Window
  • YOLO
  • RCNN
  • Fast RCNN
  • Faster RCNN
  • Segmentation
  • Transposed Convolution
  • U-NET
Introduction to Sequence Models/RNNs/NLP
  • Motivating Example
  • Applications
  • Base Sequence/ RNN model
  • Types of RNN Models
  • Natural Language Processing

Real Time Projects

Library Management System
Logistic & Linear Regression
Image Classification
Object Detection
Text Vectorization
Performance Metrics

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