Ria Institute of Technology

Marathahalli, Bangalore
8050601060
ayaz@riainstitute.in
Marathahalli Branch

Data Science Course Marathahalli

Master Python, Machine Learning, and AI. Learn to build predictive models, analyze big data, and visualize insights. Join our intensive 5-month classroom training program.

Duration
5 Months
Mode
Classroom & Online
Fee
₹51,300/-

Enquire Now

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Course Curriculum

1

Python for Data Science

  • Python Basics & Environment Setup
  • Data Types, Loops, and Functions
  • Object-Oriented Programming (OOP)
  • File Handling and Exception Handling
2

NumPy & Pandas

  • Data Manipulation with Pandas
  • Statistical Analysis with NumPy
  • Data Cleaning and Profiling
  • Handling Missing Data and Outliers
3

Data Visualization

  • Matplotlib for Basic Plotting
  • Seaborn for Statistical Visuals
  • Interactive Dashboards with Plotly
  • Exploratory Data Analysis (EDA)
4

Machine Learning Basics

  • Supervised vs Unsupervised Learning
  • Linear and Logistic Regression
  • Decision Trees and Random Forest
  • Model Evaluation Metrics (Accuracy, F1 Score)
5

Deep Learning Intro

  • Neural Networks Fundamentals
  • Introduction to Keras and TensorFlow
  • Building Simple Feedforward Networks
  • Overview of CNN and RNN
6

Real-time Projects

  • End-to-End Industry Capstone Project
  • Exploratory Data Analysis (EDA) Project
  • Machine Learning Model Deployment
  • Building a Complete Data Portfolio

Why Choose RIA Institute?

🔬

Future-Ready Curriculum

Our data science course in Marathahalli covers the latest technologies like Python, TensorFlow, and Big Data to ensure you stay ahead of industry trends.

👨‍🏫

Expert Mentors

Learn from data scientists and engineers working in top tech companies who bring real-world problem-solving experience.

🏢

Marathahalli Center

Our training center in Marathahalli is equipped with high-end labs to run machine learning algorithms smoothly.

💼

Capstone Projects

Work on real-world datasets and build a portfolio that showcases your data science skills to recruiters.

Marathahalli Branch

Data Science Course Marathahalli

Master Python, Machine Learning, and AI. Learn to build predictive models, analyze big data, and visualize insights. Join our intensive 5-month classroom training program.

Duration
5 Months
Mode
Classroom & Online
Fee
₹51,300/-

    Enquire Now






    ✓

    Enquiry Sent!

    We will contact you shortly.

    Detailed Course Syllabus

    A comprehensive, industry-aligned syllabus designed to take you from beginner to job-ready data scientist in 5 months.

    10 Modules
    150+ Hours of Training
    20+ Hands-on Labs
    5+ Capstone Projects
    50+ Tools & Libraries
    1
    ⏱ 2 Weeks

    Python Programming for Data Science

    • Python Basics & Environment Setup
      • Installing Python, Anaconda & Jupyter Notebook
      • Variables, Data Types, Operators
      • Strings, Lists, Tuples, Sets, Dictionaries
    • Control Flow & Functions
      • If-Else, Loops (for, while), List Comprehensions
      • Defining & Calling Functions, Lambda Expressions
      • Args, Kwargs, Recursion
    • Object-Oriented Programming (OOP)
      • Classes, Objects, Inheritance
      • Encapsulation, Polymorphism, Abstraction
    • File Handling & Exception Handling
      • Reading/Writing CSV, JSON, and Text Files
      • Try-Except, Custom Exceptions
    • Python Libraries Overview: os, sys, datetime, re
    2
    ⏱ 2 Weeks

    Data Manipulation with NumPy & Pandas

    • NumPy Fundamentals
      • Arrays, Indexing, Slicing & Reshaping
      • Array Operations: Broadcasting, Vectorization
      • Statistical Functions: mean, std, percentile
      • Linear Algebra with NumPy
    • Pandas for Data Analysis
      • Series and DataFrames: Creating & Importing
      • Indexing with loc, iloc, boolean masks
      • groupby, merge, pivot_table, melt
    • Data Cleaning & Profiling
      • Handling Missing Values (fillna, dropna, interpolate)
      • Outlier Detection & Treatment (IQR, Z-Score)
      • Data Type Conversion & String Operations
    • Practical Lab: Cleaning a Real-World Messy Dataset
    3
    ⏱ 1.5 Weeks

    Data Visualization & Exploratory Data Analysis

    • Matplotlib
      • Line, Bar, Scatter, Histogram, Pie Charts
      • Subplots, Figure Customization, Annotations
    • Seaborn for Statistical Visualization
      • heatmap, pairplot, boxplot, violinplot
      • Distribution Plots: kdeplot, histplot
      • FacetGrid & Categorical Plots
    • Interactive Dashboards with Plotly & Dash
      • Plotly Express & Graph Objects
      • Building Interactive Web Dashboards with Dash
    • Exploratory Data Analysis (EDA)
      • Univariate, Bivariate & Multivariate Analysis
      • Correlation Analysis & Feature Relationships
      • EDA Project: E-Commerce Sales Dataset
    4
    ⏱ 1 Week

    Statistics & Probability for Data Science

    • Descriptive Statistics
      • Mean, Median, Mode, Variance, Standard Deviation
      • Skewness, Kurtosis, Percentiles & Quartiles
    • Probability Theory
      • Probability Rules, Conditional Probability, Bayes' Theorem
      • Probability Distributions: Normal, Binomial, Poisson
    • Inferential Statistics
      • Hypothesis Testing: Z-Test, T-Test, Chi-Square Test
      • p-Values, Confidence Intervals
      • ANOVA (Analysis of Variance)
    • Central Limit Theorem & Sampling Techniques
    5
    ⏱ 3 Weeks

    Machine Learning – Supervised Learning

    • ML Fundamentals
      • ML Pipeline: Data → Features → Model → Evaluation
      • Train-Test Split, Cross-Validation (K-Fold)
      • Feature Engineering & Feature Selection
      • Encoding (Label, One-Hot), Scaling (MinMax, Standard)
    • Regression Algorithms
      • Simple & Multiple Linear Regression
      • Polynomial Regression, Ridge, Lasso, Elastic Net
      • Metrics: MAE, MSE, RMSE, R²
    • Classification Algorithms
      • Logistic Regression, K-Nearest Neighbors (KNN)
      • Naive Bayes, Support Vector Machine (SVM)
      • Decision Tree & Random Forest
      • Metrics: Accuracy, Precision, Recall, F1, ROC-AUC
    • Hyperparameter Tuning: GridSearchCV, RandomizedSearchCV
    • Project: House Price Prediction & Churn Classification
    6
    ⏱ 1.5 Weeks

    Machine Learning – Unsupervised Learning

    • Clustering Algorithms
      • K-Means Clustering & Elbow Method
      • Hierarchical Clustering & Dendrograms
      • DBSCAN for Density-Based Clustering
    • Dimensionality Reduction
      • Principal Component Analysis (PCA)
      • t-SNE for High-Dimensional Data Visualization
    • Anomaly Detection Techniques
    • Association Rule Mining: Apriori, FP-Growth
    • Project: Customer Segmentation for Retail Business
    7
    ⏱ 2 Weeks

    Advanced ML – Ensemble Methods & Boosting

    • Ensemble Learning Techniques
      • Bagging & Bootstrap Aggregating
      • Random Forest (in depth)
      • Stacking & Blending Models
    • Gradient Boosting Algorithms
      • Gradient Boosting Machines (GBM)
      • XGBoost: Theory, Tuning & Implementation
      • LightGBM & CatBoost
    • Model Interpretability
      • Feature Importance, SHAP Values
      • LIME for Model Explanation
    • Project: Credit Risk Scoring with XGBoost
    8
    ⏱ 2 Weeks

    Deep Learning & Neural Networks

    • Neural Networks Fundamentals
      • Perceptrons, Activation Functions (ReLU, Sigmoid, Softmax)
      • Forward Propagation & Backpropagation
      • Loss Functions, Optimizers (SGD, Adam)
    • Building Models with Keras & TensorFlow
      • Sequential & Functional API
      • Dropout, Batch Normalization, Early Stopping
    • Convolutional Neural Networks (CNN)
      • Convolution, Pooling, Flattening Layers
      • Image Classification Project (MNIST / CIFAR-10)
    • Recurrent Neural Networks (RNN) & LSTM
      • Sequence Modelling, Time Series Forecasting
      • Sentiment Analysis with LSTM
    • Introduction to Transfer Learning (VGG, ResNet)
    9
    ⏱ 1 Week

    Natural Language Processing (NLP)

    • Text Preprocessing
      • Tokenization, Stopword Removal, Stemming, Lemmatization
      • Bag of Words (BoW) & TF-IDF Vectorization
    • NLP with NLTK & spaCy
      • Named Entity Recognition (NER)
      • POS Tagging & Dependency Parsing
    • Word Embeddings
      • Word2Vec, GloVe Embeddings
      • Introduction to BERT & Transformers
    • NLP Projects: Spam Detection, Sentiment Analysis
    10
    ⏱ 2 Weeks

    Model Deployment, Big Data & Capstone Projects

    • Model Deployment with Flask & Streamlit
      • Building REST APIs for ML Models with Flask
      • Creating Interactive Web Apps with Streamlit
      • Containerization with Docker (Intro)
      • Deploying to Cloud: AWS / Google Cloud / Heroku
    • Big Data Tools Introduction
      • Introduction to Apache Spark & PySpark
      • Working with Large Datasets (Hadoop Ecosystem)
    • SQL for Data Science
      • Joins, Aggregations, Window Functions
      • Connecting SQL with Python (SQLAlchemy)
    • Capstone Projects
      • End-to-End ML Pipeline: Predict & Deploy
      • Industry Dataset Project (Healthcare / Finance / Retail)
      • Building Your Complete Data Science Portfolio

    Why Choose RIA Institute?

    🔬

    Future-Ready Curriculum

    Our data science course in Marathahalli covers the latest technologies like Python, TensorFlow, and Big Data to ensure you stay ahead of industry trends.

    👨‍🏫

    Expert Mentors

    Learn from data scientists and engineers working in top tech companies who bring real-world problem-solving experience.

    🏢

    Marathahalli Center

    Our training center in Marathahalli is equipped with high-end labs to run machine learning algorithms smoothly.

    💼

    Capstone Projects

    Work on real-world datasets and build a portfolio that showcases your data science skills to recruiters.

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