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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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    Years of Expertise
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    Data Science Course Fee & Duration

    Data Science

    • Course Duration : 3.5 Months
    • Training Hours : 80 Hours
    • Type : Online & Classroom

    Data Science Course Syllabus



    I am delighted to welcome you into the course of Data Science. In this  course, you will learn both the basics of conducting data science and how  to perform data analysis in python.  


    This course is intended for learners who have a basic knowledge of  programming in any  

    language (Java, C, C++, Pascal, Fortran, JavaScript, PHP, python, etc.).  Course Overview  

    First, and foremost, you'll learn how to conduct data science by learning  how to analyse data. That includes knowing how to import data, explore it,  analyse it, learn from it, visualize it, and ultimately generate easily  shareable reports. We'll also introduce you to two powerful areas of  data analysis: machine learning and natural language processing  

    To conduct data analysis, you'll learn a collection of powerful, open source, tools including:  

    • python  
    • jupyter notebooks  
    • pandas  
    • numpy  
    • matplotlib  
    • scikit learn  
    • nltk  
    • And many other tools  
    Learning Objectives  
    • Basic process of data science  
    • Python and Jupyter notebooks  
    • An applied understanding of how to manipulate and analyse uncurated  datasets  
    • Basic statistical analysis and machine learning methods  ● How to effectively visualize results  

    By the end of the course, you should be able to find a dataset, formulate  a research question,  

    use the tools and techniques of this course to explore the answer to that  question and share your findings.  

    Course Outline  

    The course is broken into 16 weeks. The beginning of the course is heavily  focused on learning the basic tools of data science, but we firmly believe  that you learn the most about data science by doing data science. So, the  latter half of the course is a combination of working on large projects  and introductions to advanced data analysis techniques. 

    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 and Multidimensional lists  ● Python - Reading from CSV  
    • Python - Multi Line Print  
    • Python - Dictionaries  
    • Python - Built in functions  
    • Python - Built in Modules  
    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 
    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 
    • Building Web Application using Rshinny 
    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 JOINs 
    • Left Join, Right Joins, Multiple Joins 
    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. Artificial Neural Network 
    • Introduction 
    • Cost Function  
    • Backpropagation Algorithm  
    • Working Example  
    Module 14. Natural Language Processing 
    • Introduction to NLTK  
    • Stop words  
    • Stemming  
    • Lemmatization  
    • Named entity recognition  
    • Text classification  
    • Sentiment analysis  
    Module 15. Project Section  
    • Python Project -Introduction  
    • Python Project -Housing Data Set  
    • 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
    Module 16. Google Cloud for Data Science 
    • Introduction to the Data and Machine Learning on Google Cloud ● Recommending Products using Cloud SQL and Spark ● Predict Visitor Purchases Using BigQuery ML 
    • Deriving Insights from Unstructured Data using Machine Learning ● Summary
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