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Best Data Science Course in Bangalore

Learn Data Science from Expert Trainers at RIA Institute of Technology

RIA institute provides Best Data Science training Institute in Marathahalli, Bangalore by experienced industry professionals and the Data Science Training Institute in Bangalore is well equipped with advanced labs. Trainers working in Data Science for more than 5 years are carefully chosen to conduct high quality Data Science Training in Marathahalli, Bangalore so that the students can benefit from real time scenarios. Instructors offering Data Science Training in Bangalore have practical knowledge as they implement their knowledge and expertise in day to day work.

Course content by best Data Science Training Institute in Bangalore is carefully crafted to match the industry requirements. The topics covered in Data Science Training include latest and best real-time examples that are aimed to help students in getting the right job so after the completion of Training. Our expert instructors will highlight the Key topics from Data Science Training based on the questions that can be possible asked by the interviewer during the job selection process; this provides confidence to the students while facing job interviews.

RIA Institute provides Class room trainings, online training, Weekend classed and Fast track course for Data Science Training in Bangalore. Students have the option to select the course timings according to their convenience. Once the Data Science Training course timings are fixed with the instructor, students are required to complete the course in the same schedule. Our schedule for Data Science Training in Bangalore is very flexible as we provide training in weekdays in morning and evening for students who cannot attend Data Science Training in Bangalore during weekends due to their work schedule.

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Data Science Training Course in Bangalore
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    RIA Institute of Technology

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    Data Science Course Fee & Duration

    Data Science & AI

    53100/-
    • Course Duration : 4.5 Months
    • Training Hours : 80 Hours
    • Type : Online & Classroom

    Data Science Course Syllabus

    Duration – 4.5 Months
    Probable Hours – 140 Hours
    Training Sections
    • Python
    • SQL
    • Statistics
    • Data visualization with Power BI ,Tableau
    • Machine Learning Algorithms
    • Model Optimization
    • AWS for data science
    • Devops
    • AI introduction & usecases
    • GenAI & LLM
    • NLP
    • Deep Learning
    • Image & Video processing
    • ChatGPT
    • Project Section
    • Resume building
    • Mock Interview
    SECTION 1: PYTHON Expected Hours: 15 Hours
    Module 1.
    Introduction
    • Python – Variables and data types
    • Python – Data Structures in Python ● Python – Functions and methods
    • Python – If statements
    • Python – Loops
    • Python – Python syntax essentials
    • Python – Writing/Reading/Appending to a file ● Python – Common pythonic
    errors
    • Python – Getting user Input
    • Python – Stats with python
    • Python – Module Import
    • Python – List, Multidimensional lists and Tuples ● Python – Reading from CSV
    • Python – Multi Line Print
    • List Comprehension
    • Python – Dictionaries
    • Python – Built in functions
    • Error handling
    • OS module
    • Python memory utilization
    Module 2. Jupyter and Numpy
    • Python Numpy – Introduction
    • Python Numpy – Creating an Array
    • Python Numpy – Reading Text Files
    • Python Numpy – Array Indexing
    • Python Numpy – N-Dimensional Arrays ● Python Numpy – Data Types
    • Python Numpy – Array Math
    • Python Numpy – Array Methods
    • Python Numpy – Array Comparison and Filtering ● Python Numpy –
    Reshaping and Combining Arrays
    Module 3. Pandas and Matplotlib
    • Python Pandas – Introduction
    • Introduction to Data Structures
    • Python Pandas – Series
    • Python Pandas – DataFrame
    • Python Pandas – Basic Functionality ●Python Pandas – Descriptive
    Statistics ●Python Pandas – Indexing and Selecting Data ●Python Pandas –
    Function Application ●Python Pandas – Reindexing
    • Python Pandas – Iteration
    • Python Pandas – Sorting
    • Python Pandas – Working with Text Data ●Python Pandas – Options and
    Customization ●Python Pandas – Missing Data
    • Python Pandas – GroupBy
    • Python Pandas – Merging/Joining
    • Python Pandas – Concatenation
    • Python Pandas – IO Tools
    • Python Pandas – Dates Conversion
    • One industry case study analysis as EDA (exploratory data analytics) in pandas
    SECTION 2: SQL Expected Hours: 10 Hours
    Module 4. SQL for Data Science
    • Install SQL packages and Connecting to DB
    • Basics of SQL DB, Primary key, Foreign Key
    • SELECT SQL command, WHERE Condition
    • Retrieving Data with SELECT SQL command and WHERE Condition to
    Pandas Data frame
    • SQL Functions (Max, Min, Count …)
    • SQL Wildcards
    • SQL JOINs
    • Left Join, Right Joins, Multiple Joins
    • SQL Select and Insert Functions
    • SQL Stored Procedures
    • SQL Create and Drop Database
    • SQL Create, Update, Alter, Delete and Drop Table
    • SQL Constraints
    • Theoretical and windows(analytical function) intro in greSQL
    SECTION 3: STATISTICS Expected Hours: 20 Hours
    Module 5. Statistics
    • Inferential Statistics
    o Basics of Probability
    o Discrete and Continuous Probability Distributions
    o Central Limit Theorem
    • Hypothesis Testing
    • Exploratory Data Analysis
    o Data Sourcing
    o Data Cleaning
    o Univariate and Bivariate Analysis
    o Derived Metrics
    SECTION 4. Data Visualization Hours: 7 Hours

    • Significance of different Data visualization tool in industry for telling stories
    with DATA
    • PowerBI data model creation for analysis, Data connection , data points
    • One complete case study with PowerBI data analysis
    • Tableau advantage of data visualization
    SECTION 5: Machine Learning Algorithms Expected Hours: 35 Hours
    Module 6. Machine Learning – Introduction
    • What is Machine Learning
    • Types of Machine Learning
    • Applications of Machine Learning
    • Supervised vs Unsupervised learning
    • Classification vs Regression
    • Training and testing Data
    • features and labels
    Module 7. Linear Regression
    • Introduction
    • Introducing the form of simple linear regression
    • Estimating linear model coefficients
    • Interpreting model coefficients
    • Using the model for prediction
    • Plotting the “least squares” line
    • Quantifying confidence in the model
    • Identifying “significant” coefficients using hypothesis testing and p values
    • Assessing how well the model fits the observed data
    • Extending simple linear regression to include multiple predictors ●
    Comparing feature selection techniques: R-squared, p-values, cross
    validation
    • Creating “dummy variables” (using pandas) to handle categorical predictors
    Module 8. Logistic Regression
    • Refresh your memory on how to do linear regression in scikit-learn ● Attempt
    to use linear regression for classification
    • Show you why logistic regression is a better alternative for classification
    • Brief overview of probability, odds, e, log, and log-odds ● Explain the form of
    logistic regression
    • Explain how to interpret logistic regression coefficients ● Demonstrate how
    logistic regression works with categorical features
    • Compare logistic regression with other models
    Module 9. Support Vector Machine
    • Introduction
    • Tuning parameters
    • Kernel
    • Regularization
    • Gamma
    • Margin
    • Classification Example
    Module 10. Naive Bayes
    • Introduction
    • Working Example
    Module 11. K-Means Clustering
    • Introduction
    • Unsupervised Learning
    • K-Means Algorithm
    • Optimization Objective
    • Random Initialization
    • Choosing the number of clusters
    Module 12. KNN
    • Introduction
    • Working Example
    Module 13. Decision Trees and Random Forests
    • Introduction to Decision Trees
    • Truncation and Pruning
    • Random Forests
    SECTION 6: MODEL OPTIMIZATION Expected Hours: 2 Hours
    Module 15. Model Optimization and Evaluation
    • Maxima and Minima
    • Gradient Descent
    • Stochastic Gradient Descent
    SECTION 7 : Data science with AWS tools : 5 Hours

    • Introduction
    • Diff tools available in AWS for data science
    • Comparision with other Cloude platforms( GCP, Azure)
    SECTION 8: Devops : 5 Hours
    • Version Control with GIT
    • Module deployment
    • Model Integration
    • Continuous monitoring and feedback loops
    SECTION 9: AI introduction & usecases Expected Hours: 5 Hours
    • Fundamentals
    • Fuzzy logic
    • RL Intro and algorithms
    • Robotics Intro and end to end architecture
    • Q-Learning
    • Use-cases
    SECTION 10: GenAI & LLM Expected Hours: 5 Hours
    • Fundamentals
    • Architecture
    • What is LLM
    • Setup: open source LLM download and setup
    • Working example with open source LLM
    SECTION 11. Natural Language Processing
    • Introduction to NLTK
    • Stop words
    • Stemming
    • Lemmatization
    • Named entity recognition
    • Text classification
    • Sentiment analysis
    SECTION 12: DEEP LEARNING Expected Hours: 8 Hours
    • Basics
    • Neural network
    • Encoding & decoding
    • TensorFlow
    • Working example: Image classification
    • Langchain
    SECTION 13: Image & vedio processing Expected Hours: 5 Hours
    • Basics
    • Feature extraction
    • Working example: Image classification
    SECTION 14: Chat GPT Expected Hours: 10 Hours
    • Introduction
    • Chat GPT Architecture
    • Prompt engineering Intro
    • Creating working prototype with ChatGPT
    • Project : Live chat bot
    SECTION 15: Prompt Engineering Expected Hours: 5 Hours
    • Introduction
    • Basic programs
    • Use cases
    SECTION 16: Project Section Expected Hours: 20 Hours
    • Python Project -Introduction
    • Python Project -Housing Data Set or specific Data Set from Kaggle
    • Python Project -Understand the problem ● Python Project -Hypothesis
    Generation ● Python Project -Get Data
    • Python Project -Data Exploration
    • Python Project -Data Pre-Processing ● Python Project -Feature Engineering ●
    Python Project -Model Training
    • Python Project -Model Evaluation

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      Frequently Asked Questions

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      Why should i choose RIA Institute of Technology

      RIA Institute of Technology offers 63+ IT & Non IT training courses in marathahalli, Bangalore with 10+ years of Experienced Expert level Trainers.

      • Fully hands-on training
      • 30+ hours course duration
      • Industry expert faculties
      • Completed 1500+ batches
      • 100% job oriented training
      • Certification guidance
      • Own course materials
      • Resume editing
      • Interview preparation
      • Affordable fees structure
      Who is my trainer and how are they selected?
      • All Our trainers are more than 10+ years of experience in course relavent technologies.
      • Trainers are expert level and fully up-to-date in the subjects they teach because they continue to spend time working on real-world industry applications.
      • Trainers have experienced on multiple real-time projects in their industries.
      • Are working professionals working in multinational companies such as Oracle, TCS, HCL Technologies, ZOHO, Birlasoft, IBM, Microsoft, HP, Scope, Philips Technologies, etc…
      • Trained more than 2000+ students in a year.
      • Strong theoretical & practical knowledge.
      • Are certified professionals with high grade.
      • Are well connected with hiring HRs in multinational companies.
      What if I miss a session?

      No worries. RIA institute of Technology assures that no one misses single lectures topics. We will reschedule the classes as per your convenience within the stipulated course duration with all such possibilities. If required you can even attend that topic with any other batches.

      What are the different modes of training Provided?

      RIA Institute of Technology provides many suitable modes of training to the students like

      • Classroom training
      • One to One training
      • Fast track training
      • Live Instructor LED Online training
      • Customized training
      What certification will I receive after course completion?

      You will receive globally recognized course completion certificate issued from RIA Institute of Technology.

      What are the payment options?

      We accept all major kinds of payment options. Cash, Card (Master, Visa, and Maestro, etc), Net Banking and etc.

      I have more queries?

      Please Contact our course advisor/consultant on +91 8050601060. Or you can share your queries through info@riainstitute.in

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