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Data Science Training Course in Bangalore

Learn Data Science to improve customer experience, drive revenue growth, and measure performance.

Data Science Training Course in BangaloreMaster Data Science Course Syllabus in Just 1 Hour a Day with Ria Institute's Expert Mentors

Data Science Training Course in Bangalore takes your career to the next level, all while dedicating only one hour of your time each day. RIA Institute’s team of experts will help you fit data science syllabus learning into your jam-packed schedule. You will master crucial skills and techniques that will make you shine in data science, all while keeping up with your other responsibilities. It’s like having a personal data science coach who knows how to make learning fun and easy! Through hands-on training and real-world application, you’ll gain the confidence and expertise needed to make a real impact.  With a focus on practical application and problem-solving, you’ll be able to apply your newfound skills to your current or future career right away. So don’t wait – take the first step towards transforming your career and enroll in RIA Institute’s Data Science Course Syllabus today. Just one hour a day can make all the difference.

Key Features of Data Science Training Course in Bangalore:

  1. Real-Time Projects: Data science course syllabus offers data science projects to hone your practical skills and build an impressive portfolio to showcase your expertise.
  2. Industry-Seasoned Trainers: Ria Institute’s expert mentors bring their wealth of practical experience to the classroom with the data science course syllabus.
  3. Personalized Learning Approach: Benefit from one-on-one and classroom-based training, ensuring a tailored learning experience.
  4. Comprehensive Tool Mastery: Gain proficiency in 8+ data science tools and technologies to tackle complex challenges with ease.
  5. Dedicated Job Assistance: Our team of experts will guide you through the job search process, ensuring a seamless transition into your dream data science role.

Job Responsibilities of Data Scientist 

  1. Collecting, Cleaning, and Analyzing Data: Data scientists gather, preprocess, and analyze large datasets to ensure data quality and reliability.
  2. Developing Predictive Models: They create and execute machine learning algorithms and statistical models to derive insights and make predictions.
  3. Data Visualization: Presenting results and suggestions to stakeholders using clear visuals and concise reports.
  4. Collaboration: Working with cross-functional teams to implement data-driven solutions based on analysis.
  5. Data Management: Ensuring data availability, quality, and security for analysis and modeling.
  6. Data Analysis: Investigating and interpreting data to identify patterns, trends, and relationships.
  7. Machine Learning: Using machine learning tools to select features, optimize classifiers, and develop prediction systems.
  8. Data Mining: Uncovering patterns and relationships in large datasets using advanced algorithms.
  9. Statistical Analysis: Applying statistical techniques to analyze data sets and find underlying trends.
  10. Data Visualization: Organizing findings into charts, dashboards, or reports for effective communication.

Check out our Data Science Course in Bangalore Syllabus

Section Module Topics Covered
Python 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
Python 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
Python 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
R Language Module 4. R for Data Science
  • Introduction to R Programming
  • Importance of R
  • Data Types and Variables in R
  • Operators in R
  • Conditional Statements in R
  • Loops in R
  • R script and Functions in R
SQL Module 5. 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
Statistics Module 6. Statistics
  • Inferential Statistics
    • Basics of Probability
    • Discrete and Continuous Probability Distributions
    • Central Limit Theorem
  • Hypothesis Testing
  • Exploratory Data Analysis
    • Data Sourcing
    • Data Cleaning
    • Univariate and Bivariate Analysis
    • Derived Metrics
Data Visualization Section 5. Data Visualization
  • 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
  • Denodo introduction and future role
Machine Learning Algorithms Module 7. 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
Machine Learning Algorithms Module 8. 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
Machine Learning Algorithms Module 9. 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
Machine Learning Algorithms Module 10. Support Vector Machine
  • Introduction
  • Tuning parameters
  • Kernel
  • Regularization
  • Gamma
  • Margin
  • Classification Example
Machine Learning Algorithms Module 11. Naive Bayes
  • Introduction
  • Working Example
Machine Learning Algorithms Module 12. K-Means Clustering
  • Introduction
  • Unsupervised Learning
  • K-Means Algorithm
  • Optimization Objective
  • Random Initialization
  • Choosing the number of clusters
Machine Learning Algorithms Module 13. KNN
  • Introduction
  • Working Example
Machine Learning Algorithms Module 14. Decision Trees and Random Forests
  • Introduction to Decision Trees
  • Truncation and Pruning
  • Random Forests
Machine Learning Algorithms Module 15. Natural Language Processing
  • Introduction to NLTK
  • Stop words
  • Stemming
  • Lemmatization
  • Named entity recognition
  • Text classification
  • Sentiment analysis
Model Optimization Module 16. Model Optimization and Evaluation
  • Maxima and Minima
  • Gradient Descent
  • Stochastic Gradient Descent
Introduction to AI CHAPTER 1: INTRODUCTION TO AI
  • What is Artificial Intelligence
  • Types of AI
  • Perceptron
  • Multi-Layer Perception
  • Markov Decision Process
  • Logical Agent & First Order Logic
  • AL Applications
Introduction to AI CHAPTER 2: ARTIFICIAL INTELLIGENCE FUNDAMENTALS
  • Application of AI
  • History of AI
  • Machine Learning
  • Fuzzy Logic
  • Expert Systems
  • Computer Vision
Introduction to AI CHAPTER 3: REINFORCEMENT LEARNING AND Q-LEARNING INTUITION
  • Q-Learning Introduction
  • Reinforcement Learning Concepts
  • Marcov Decision Process
  • Adding a “Living Penalty”
  • Temporal Difference
  • Q-Learning Visualization
Introduction to AI CHAPTER 4: DEEP Q-LEARNING INTUITION
  • Plan of Attack
  • Deep Q-Learning Intuition – Learning
  • Experience Replay
  • Action Selection Policies
Project Section Module 18. Project Section
  • 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

FAQsData Science Training Course in Bangalore FAQs

Ria Institute in Bangalore offers the top data science course syllabus in Bangalore. Our curriculum, delivered by industry-leading subject matter experts, provides learners with a comprehensive, hands-on education in the latest data science methodologies and tools. Through a blend of theoretical instruction and practical application, students develop robust skillsets in areas such as machine learning, data mining, predictive analytics, and data visualization. Ria Institute’s data science training course course syllabus is the gold standard for aspiring data scientists looking to launch their careers in Bangalore’s thriving tech ecosystem.

Ria Institute’s data science course syllabus is a 5-month program that equips learners with the specialized knowledge and technical proficiencies required to excel as data scientists. The curriculum is designed to accelerate skill development through an immersive, project-based learning approach, enabling students to hit the ground running upon graduation.

The average salary for a data scientist in Bangalore, India is a lucrative ₹14,50,000 per annum. This reflects the high demand for skilled data science professionals capable of leveraging advanced analytics to drive business insights and strategic decision-making. As organizations across industries increasingly prioritize data-driven transformation, data scientists with expertise in areas like predictive modeling, natural language processing, and big data engineering can command premium compensation packages.

No, data science is not inherently difficult for individuals with a strong aptitude for quantitative analysis, programming, and problem-solving. While the field does require the mastery of complex statistical techniques and technological tools, learners with a genuine interest and commitment to the discipline can develop the necessary competencies through structured training and hands-on practice. The key is to approach data science education with a growth mindset, leveraging available resources and support systems to overcome any initial challenges.

Data science is an exceptional career path with growth potential. The demand for data scientists continues to skyrocket as organizations recognize the immense value that can be extracted from data through advanced analytics. Data science professionals enjoy highly competitive salaries, diverse job opportunities, and the ability to drive impactful, data-driven decision-making across a wide range of industries. 

Absolutely, data science is a high-paying profession. With the average data scientist salary in Bangalore reaching ₹14,50,000 per annum, the field offers lucrative compensation packages that reflect the specialized skills and business-critical insights that data science practitioners can provide. 

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