×
Course Categories
×
Product Categories
Best Data Science Training in Hyderabad
100% Job Oriented Training & Placement Assistance
5.0
5.0
4.6
Data Science
Data Science is a multidisciplinary field that combines statistics, computer science, and domain expertise to extract meaningful insights from structured and unstructured data. It has revolutionized the way organizations understand and leverage data, driving innovation and informed decision-making across various industries.
What is Data Science?
At its core, Data Science involves collecting, processing, analyzing, and interpreting vast amounts of data. This data can come from multiple sources, such as customer transactions, social media interactions, sensors, and more. Data Scientists use various tools and techniques, including machine learning, data mining, and big data analytics, to uncover patterns, trends, and relationships within the data.
Applications of Data Science
Data Science is applied in numerous fields including healthcare for predictive analytics, finance for risk management, marketing for customer segmentation, and technology for developing recommendation systems. Its versatility and power make it an essential asset for any data-driven organization.
Data Science is more than just crunching numbers; it’s about transforming data into actionable insights that drive strategic decisions and foster innovation. Whether you're a business leader looking to optimize operations or a tech enthusiast eager to dive into the world of data, understanding Data Science is key to unlocking the potential of the information age.
Key Areas in Data Science
- Machine Learning
- Data Engineering
- Data Analysis
- Business Intelligence
- Natural Language Processing (NLP)
- Big Data
Job Opportunities to Look For
- Data Scientist
- Machine Learning Engineer
- Data Analyst
- Business Intelligence Analyst
- Data Engineer
- Statistical Analyst
- AI Specialist
Companies Hiring Data Scientists
- Tech Giants: Google, Amazon, Facebook, Microsoft
- Consulting Firms: McKinsey, Deloitte, PwC, Accenture
- Financial Services: Goldman Sachs, JPMorgan Chase, Citibank
- Healthcare: Pfizer, Johnson & Johnson, Medtronic
- Startups: Many startups are looking for data science talent to drive innovation.
Required Skills
- Programming Languages: Python, R
- Data Manipulation: SQL, Pandas
- Machine Learning Libraries: Scikit-Learn, TensorFlow, PyTorch
- Data Visualization: Matplotlib, Seaborn, Tableau, Power BI
- Big Data Tools: Hadoop, Spark
- Statistics and Mathematics: Fundamental knowledge is crucial
Data Science Course Includes
120 Hours of Training
240 Hours of Practice
Real Time Scenarios
Flexible Class Timings
Individual Doubts Clarification
Career Guidance
Add-ons
Most of the IT Jobs in the industry expect the following add-on skills. Hence, we offer these skills-set as FREE
Courses (Basics) to ease your learning process and help you stay ahead of the competition.
- Mathematics
- Statistics
- Resume Prep..
- Mock Inter..
Project Oriented Course Curriculum
You will be exposed to the following content in Data Scrience with Gen. AI
- Introduction to Data Science
- Introduction to Data Science
- Discussion on Course Curriculum
- Introduction to Programming
- Python - Basics
- Introduction to Python, syntax, data types
- Operators and expressions
- Control flow (if-else, nested ifs)
- Loops – for, while, break, continue
- Functions and scope
- Error handling – try, except
- Strings and string methods
- Lists and list operations
- Dictionaries and sets
- Python for Data
- Introduction to Numpy – arrays and operations
- Indexing and slicing arrays
- Array math and broadcasting
- Pandas – Series and DataFrames
- Import/export data (CSV, Excel)
- Filtering and subsetting
- GroupBy and aggregation
- Data cleaning basics (handling NaNs)
- Merging and joining datasets
- Matplotlib
- Introduction
- Pyplot
- Figure Class
- Axes Class
- Setting Limits and Tick Labels
- Multiple Plots
- Legend
- Different Types of Plots
- Line Graph
- Bar Chart
- Histograms
- Scatter Plot
- Pie Chart
- 3D Plots
- Working with Images
- Customizing Plots
- Seaborn
- catplot() function
- stripplot() function
- boxplot() function
- violinplot() function
- pointplot() function
- barplot() function
- Visualizing statistical relationship with Seaborn relplot() function
- scatterplot() function
- regplot() function
- lmplot() function
- Seaborn Facetgrid() function
- Multi-plot grids
- Statistical Plots
- Color Palettes
- Faceting
- Regression Plots
- Distribution Plots
- Categorical Plots
- Pair Plots
- Scipy
- Signal and Image Processing (scipy.signal, scipy.ndimage):
- Linear Algebra (scipy.linalg):
- Integration (scipy.integrate)
- Statistics (scipy.stats):
- Spatial Distance and Clustering (scipy.spatial):
- Statsmodels
- Linear Regression (statsmodels.regression):
- Time Series Analysis (statsmodels.tsa):
- Statistical Tests (statsmodels.stats)
- Anova (statsmodels.stats.anova):
- Datasets (statsmodels.datasets):
- Scalars, vectors, matrices – definitions
- Matrix operations and types
- Dot product and cross product
- Matrix multiplication and transposition
- Determinants and inverse
- Eigenvalues and eigenvectors
- Functions and limits
- : Derivatives and partial derivatives
- Gradient and optimization overview
- Measures of central tendency and spread
- Probability theory and axioms
- Conditional probability, independence
- Baye's theorem
- Probability distributions (Normal, Binomial)
- Central Limit Theorem
- : Z-test and t-test
- Hypothesis testing and confidence intervals
- : Skewness, kurtosis, outliers
- Introduction
- DBMS vs RDBMS
- Intro to SQL
- SQL vs NoSQL
- MySQL Installation
- Keys
- Primary Key
- Foreign Key
- Constraints
- Unique
- Not NULL
- Check
- Default
- Auto Increment
- CRUD Operations
- Create
- Retrieve
- Update
- Delete
- SQL Languages
- Data Definition Language (DDL)
- Data Query Language
- Data Manipulation Language (DML)
- Data Control Language
- Transaction Control Language
- SQL Commands
- Create
- Insert
- Alter, Modify, Rename, Update
- Delete, Truncate, Drop
- Grant, Revoke
- Commit, Rollback
- Select
- SQL Clauses
- Where
- Distinct
- OrderBy
- GroupBy
- Having
- Limit
- Operators
- Comparison Operators
- Logical Operators
- Membership Operators
- Identity Operators
- Wild Cards
- Aggregate Functions
- SQL Joins
- Inner Join & Outer Join
- Left Join & Right Join
- Self & Cross Join
- Natural Join
- What is ML? Types of ML
- ML pipeline: preprocessing to evaluation
- Supervised vs Unsupervised
- Real-world applications and datasets
- Tools and environment setup (sklearn, Colab)
- Regression intuition and use-cases
- Least squares method and cost function
- Gradient descent math
- R2, MAE, MSE, RMSE
- Implementing Linear Regression in sklearn
- Visualizing regression results
- Polynomial regression
- Handling multicollinearity
- Feature selection basics
- Classification vs Regression
- Sigmoid function and decision boundary
- Cost function and gradient descent for classification
- Accuracy, precision, recall, F1 score
- Sklearn implementation + ROC curve
- Decision trees – concept and splitting
- Gini index vs entropy
- Overfitting and pruning
- Random forests – bagging and voting
- Feature importance and interpretation
- Introduction to Gradient Boosting
- : Hyperparameter tuning of trees
- Hands-on with real dataset (Titanic/Loan)
- SVM concept and margin explanation
- Kernel trick – RBF, polynomial
- Hyperparameter tuning (C, gamma)
- Implementing SVM in sklearn
- Visualizing support vectors
- K-means clustering
- Elbow method and silhouette score
- Hierarchical clustering
- DBSCAN overview
- : Applications and visualization
- Introduction
- Curse of dimensionality
- PCA theory and math
- Variance explained and eigenfaces
- PCA in sklearn
- t-SNE for visualization
- Train-test split vs cross-validation
- k-fold cross-validation
- GridSearchCV and RandomizedSearchCV
- Feature scaling techniques
- Evaluation strategy and best practices
- Project planning and dataset selection
- Data cleaning and exploration
- Model development and validation
- Evaluation and tuning
- Final report and presentation prep
- Introduction
- Power BI for Data scientist
- Types of reports
- Data source types
- Installation
- Basic Report Design
- Data sources and Visual types
- Canvas and fields
- Table and Tree map
- Format button and Data Labels
- Legend,Category and Grid
- CSV and PDF Exports
- Visual Sync, Grouping
- Slicer visual
- Orientation,selection process
- Slicer:Number,Text,slicer list
- Bin count,Binning
- Hierarchies, Filters
- Creating Hierarchies
- Drill Down options
- Expand and show
- Visual filter,Page filter,Report filter
- Drill Thru Reports
- Power Query
- Power Query transformation
- Table and Column Transformations
- Text and time transformations
- Power query functions
- Merge and append transformations
- DAX Functions
- DAX Data types,Syntax Rules
- DAX measures and calculations
- Creating measures
- Creating Columns
- Deep learning
- Neural networks basics
- Perceptrons and activation functions
- Loss functions and gradient descent
- Training, validation, test split
- Introduction to PyTorch/TensorFlow (choose one)
- Building a simple feedforward network
- Overfitting, underfitting, regularization
- Optimizers (SGD, Adam)
- ReLU, Sigmoid, Softmax comparison
- Project: Train a digit recognizer on MNIST
- Natural Language Processing (NLP)
- Text preprocessing (tokenization, stemming, stop words)
- Bag of Words and TF-IDF
- Word embeddings: Word2Vec, GloVe
- Sentiment analysis with scikit-learn
- POS tagging, NER using spaCy
- Introduction to Hugging Face library
- Using pre-trained transformers
- Text classification with BERT
- Hands-on: Fine-tune BERT
- What are transformers?
- Encoder-decoder architecture
- Attention mechanism in detail
- Self-attention and positional encoding
- Overview of GPT, BERT, T5, LLaMA
- Inference from pre-trained LLMs
- Text generation using GPT-2
- Hugging Face pipeline for text tasks
- Zero-shot and few-shot learning
- What is prompt engineering?
- Prompt structure – few-shot, zero-shot, chain-of-thought
- Designing good prompts with GPT-3.5/GPT-4
- Prompt templates and formatting
- Hands-on with OpenAI API
- Using temperature, top_p, max_tokens effectively
- Prompt tuning vs fine-tuning
- Testing prompts using LangChain PromptTemplate
- Case study: customer support prompt design
- Mid-term project: Multi-turn prompt-based chatbot
- Introduction to LangChain framework
- LLMChain, PromptTemplate, chains
- Agents and tools in LangChain
- Vector stores and embeddings with FAISS
- Introduction to OpenAI Python SDK
- Role of environment variables and API keys
- Hugging Face Transformers deeper usage
- Uploading your own model to Hugging Face Hub
- : LLM evaluation: perplexity, BLEU, ROUGE
- : What is RAG?
- Difference between RAG and closed-book QA
- Document loading and splitting
- Embedding creation and storage
- Vector search with FAISS/ChromaDB
- Connecting RAG with LangChain
- RAG with OpenAI embeddings
- Case study: document assistant with PDFs
- Hands-on project: PDF-based chatbot
