Data Science Course - Data Science Training

Our Course

DATA SCIENCE

ABOUT THIS COURSE

Course Description: Data Science

Data Science is an interdisciplinary field that leverages statistical methods, algorithms, and technology to extract insights and knowledge from structured and unstructured data. In today’s data-driven world, the significance of Data Science cannot be overstated. It empowers organizations across various industries—such as healthcare, finance, marketing, and technology—to make informed decisions, optimize processes, and innovate solutions.

THE COURSE INCLUDE

CURRICULUM

Beginner Python

Lists
Tuples
Dictionraies
Sets
IF-else statements
Loops (for, while)
Break
Continue
Control flow
Funtcions
Modules
Packages
Exceptional Handling
Try-except blocks
oop, class, Object
Inheritance
polymorphism
Encapsulation
Jupyter Notebooks

Neural Networks

Artificial Neural Network
Deep Learning
Perceptron
Activation Function
Weight
Bias
Layer (Input, Hidden, Output)
Feedforward Neural Network
Backpropagation
Loss Function
Optimizer
Single-Layer Perceptron
Multi-Layer Perceptron
Autoencoder
Sigmoid
Hyperbolic Tangent (tanh)
Rectified Linear Unit (ReLU)
Leaky ReLU
Softmax
Gradient Descent
Stochastic Gradient Descent (SGD)
Mini-Batch Gradient Descent
Learning Rate
Batch Normalization
Regularization (L1, L2)
Dropout
Fine-tuning
Cross-Entropy Loss
TensorFlow
Keras
Explainable AI (XAI)
Interpretability in Neural Networks
Gradient Check
Vanishing Gradient
Exploding Gradient
GPU
TPU (Tensor Processing Unit)
Vanishing Gradient Problem
Exploding Gradient Problem

Introduction to ML & NN

Supervised Learning
Unsupervised Learning
Semi-supervised Learning
Regression
Classification
Ridge and Lasso Regression
Regularisation
Performance Metrics
Confusion Matrix
F1-score
Receiver operating characteristic (ROC AUC)
Accuracy
Mean Squared Error (MSE)
Root Mean Squared Error (RMSE)
Mean Absolute Percentage Error (MAPE)
Mean Absolute Error (MAE)
Gradient Descent
Stochastic Gradient Descent (SGD)
Mini-Batch Gradient Descent
Learning Rate

ML Supervised Algorithms

Linear Regression
Polynomial Regression
Logistic Regression
Support Vector Machines (SVM)
Decision Trees
Bagging/Boosting
Random Forest
Gradient Boosting
XGBoost Naive Bayes

ML Unsupervised Algorithms

Clustering Analysis
K-Means Clustering
Elbow Method
K-Means++
Hierarchical Clustering
Agglomerative clustering
Silhouette score
Within-Cluster Sum of Squares (WCSS)
DBSCAN
Anomaly Detection
Outlier Detection
Isolation Forest
Gaussian Mixture Model (GMMs)
Elliptic Envelope
Local Outlier Factor (LOF)
One-class-SVM
Dimensionality Reduction
Feature Extraction
Eigenvalues/Eigenvectors
Eigenvalues decomposition
Principal Component Analysis (PCA)
t-Distributed Stochastic Neighbor Embedding (t-SNE)

Natural Language Processing

Word Embeddings
Word2Vec
GloVe (Global Vectors for Word Representation)
Sentiment Analysis
Topic Modeling
Sequence Modeling
Language Modeling
Temporal Dependency
Machine Translation
Text Generation
Transfer Learning in NLP
Sentiment Analysis
Text Classification
Question Answering
Text Summarization
Speech Recognition
NLTK (Natural Language Toolkit)
SpaCy
Gensim
Transformers (Hugging Face)
TensorFlow
PyTorch
Named Entity Recognition (NER)
Part-of-Speech Tagging (POS Tagging)
Sequence-to-Sequence Model (Seq2Seq)
Encoder-Decoder Architecture
Backpropagation Through Time (BPTT)
Gradient Clipping
Attention Mechanism
Self-Attention
Transformer Architecture
GPT (Generative Pre-trained Transformer)
Recurrent Neural Network (RNN)
Long Short-Term Memory (LSTM)
Gated Recurrent Unit (GRU)
Bidirectional RNN
BERT (Bidirectional Encoder Representations from Transformers)
BERT

Gen AI

Introduction to GenAI
Types of GenAI Models
Transformers
Diffusion Models
Text Generation Models
Applications of LLMs
Langchain Framework
RAG (Retrieval Augment Generation)
Fine-tuning of LLMs

Python libraries

Pandas

Numpy

SciPy

Matplotlib

Seaborn

Plotly

Data Analysis

Data Visualisation

Data Cleaning

Data Wrangling

Exploratory Data Analysis (EDA)

Missing Data Analysis

Data Discovery

Data Profiling

Data Inspection

Beautiful Soup

Web-API

Data Quality Checks

3 Months

90+ Learning Hours

Tool Box

5+ Assured Interviews

OTHER COURSES

Oracle FCCS

Data Engineering

Python

Oracle EPM

Oracle PBCS

Who Should Take This Course?

Graduates and Postgraduates

Fresher’s

Working Professionals

No Prior Knowledge is required

What Are The Roles Can You Apply

See Yourself In One Of These Roles?

Tools & Technologies

THE TEACHER

SHIVA

I love teaching Data Science because it allows me to share the exciting world of data analysis and machine learning with students. I strive to make each lesson clear and practical, so students can apply what they learn right away. It’s amazing to watch them grow from beginners to skilled data scientists! I refer this training center both Online & Offline. I can handle both platforms for my students convenience.

SUCCESS STORIES

K. Naveen

I have done this course Online. Glass Konnect team is very good people and friendly staff. I didn’t know about Data Science clearly what it is. Glass Konnect team helped me to complete my entire course. I have sucussesfully done it. As a complete beginner, I was a bit intimidated at first, but this Data Science Course was perfect for me. It started with the basics and gradually built up to more advanced topics like deep learning and neural networks. The instructors were fantastic, offering great support throughout. I am excited to apply my new skills in the real world and begin my career in data science!

Y. Anitha

The Data Science Training exceeded my expectations. The content was comprehensive, covering everything from data cleaning and visualization to machine learning and statistical modeling. I loved the practical assignments that gave me a chance to work with real datasets. I now feel well-prepared to tackle data science projects in my job, and the knowledge I gained has opened up new career opportunities. I have completed my course offline. I strongly recommend this institute for Data Science Training for Beginners.

G. KUMAR

I took the Data Science course to enhance my skills for career advancement, and it delivered on every level. The curriculum is well-structured, and the hands-on exercises made complex concepts easier to grasp. I particularly appreciated the focus on Python and machine learning. This course gave me the tools I need to make data-driven decisions and provided me with the confidence to pursue a data science career.

M. SHANTHI

This course was a game-changer for me. I’ve always been interested in data but didn’t know how to make the transition into data science. The instructors were very knowledgeable, and the course covered a wide range of topics, including data wrangling, analysis, and visualization. The practical projects and real-life applications made it all come to life, and I feel ready to take on data science challenges in the professional world.

Pricing

Affordable Pricing Packages

Online Batch

Basic Package

online batch is a virtual training session where you can join and learn in real-time from anywhere

What's included?

*Terms and Conditions apply

Offline Batch

Regular Package

offline batch at Glass Konnect offers in-person software training at our physical location in Hyderabad.