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Data Science

5.0 Created by potrace 1.15, written by Peter Selinger 2001-2017

5.0 Created by potrace 1.15, written by Peter Selinger 2001-2017

4.6 Created by potrace 1.15, written by Peter Selinger 2001-2017

Best Data Science Training Institute in Hyderabad, Kukatpally & KPHB

SSSIT Computer Education is rated as one of the Best Data Science Training Institutes in KPHB, Kukatpally and Hyderabad by trained students. Here Trainers are highly qualified & experienced in delivering Training and Development delivers the content as per industry expectation from a Data Scientist. The Data Science Training class consists of more project oriented scenarios with the Industry Aligned Curriculum

What is Data Science?

Data Science is a dynamic field that blends statistics, computer science, and domain knowledge to derive insights from structured and unstructured data. It empowers organizations to make data-driven decisions by uncovering patterns, trends, and relationships using techniques like machine learning, data mining, and big data analytics. Data from sources such as customer transactions, social media, and sensors is collected, processed, analyzed, and interpreted to drive innovation across industries.

Applications of Data Science

Data Science finds applications across diverse fields, such as healthcare for predictive analytics, finance for risk management, marketing for customer segmentation, and technology for creating recommendation systems. Its adaptability and influence make it a cornerstone of success for data-driven organizations.

Beyond processing numbers, Data Science focuses on transforming raw data into actionable insights that fuel strategic decisions and spark innovation. Whether you're a business leader aiming to optimize operations or a tech enthusiast eager to explore the data-driven world, understanding Data Science is vital for harnessing the potential of the digital era.

Key Areas in Data Science

1. Data Collection and Preparation

  • Gathering raw data from diverse sources like databases, APIs, social media, sensors, or web scraping.
  • Cleaning, preprocessing, and organizing data to ensure quality and consistency for analysis.

2. Data Exploration and Visualization

  • Employing statistical methods and visual tools (e.g., matplotlib, Tableau, Power BI) to understand data distribution, identify trends, and uncover patterns.
  • Creating visual representations to communicate findings effectively.

3. Statistical Analysis

  • Applying statistical techniques to test hypotheses, measure variability, and draw meaningful inferences from data.
  • Understanding probability, regression, and statistical modeling as foundational skills.

4. Machine Learning and Artificial Intelligence

  • Designing algorithms that enable systems to learn from data and make predictions or decisions without explicit programming.
  • Core methods include supervised, unsupervised, and reinforcement learning.

5. Big Data and Cloud Computing

  • Leveraging frameworks like Hadoop and Spark to process and analyze large datasets efficiently.
  • Using cloud platforms (AWS, Azure, Google Cloud) for scalable data storage and computation.

6. Data Engineering

  • Building and maintaining data pipelines to ensure seamless data flow and accessibility.
  • Managing databases, ETL (Extract, Transform, Load) processes, and integrating tools for effective data storage and processing.

7. Natural Language Processing (NLP)

  • Analyzing and interpreting human language using techniques like text classification, sentiment analysis, and language translation.
  • Applications include chatbots, voice assistants, and text mining.

8. Deep Learning

  • Using neural networks to solve complex problems like image recognition, speech processing, and autonomous systems.
  • Popular frameworks include TensorFlow and PyTorch.

9. Data Ethics and Privacy

  • Ensuring the ethical use of data while adhering to privacy regulations like GDPR and CCPA.
  • Addressing concerns about bias, transparency, and accountability in data-driven decision-making.

10. Domain Expertise

  • Combining technical skills with knowledge of specific industries (e.g., healthcare, finance, marketing) to tailor solutions to real-world challenges.

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)
  • : Linear Regression
    • 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
  • Logistic Regression
    • Classification vs Regression
    • Sigmoid function and decision boundary
    • Cost function and gradient descent for classification
    • Accuracy, precision, recall, F1 score
    • Sklearn implementation + ROC curve
  • Trees and Ensembles
    • 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)
  • Support Vector Machines
    • SVM concept and margin explanation
    • Kernel trick – RBF, polynomial
    • Hyperparameter tuning (C, gamma)
    • Implementing SVM in sklearn
    • Visualizing support vectors
  • Clustering
    • K-means clustering
    • Elbow method and silhouette score
    • Hierarchical clustering
    • DBSCAN overview
    • : Applications and visualization
  • Dimensionality Reduction
    • Introduction
    • Curse of dimensionality
    • PCA theory and math
    • Variance explained and eigenfaces
    • PCA in sklearn
    • t-SNE for visualization
  • Model Selection and Tuning
    • Train-test split vs cross-validation
    • k-fold cross-validation
    • GridSearchCV and RandomizedSearchCV
    • Feature scaling techniques
    • Evaluation strategy and best practices
  • Capstone Project
    • 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

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