Data Science

Data Science Training in Marathahalli

Millions of data are produced every day. With boundless opportunities knocking, the promises offered by studying data science is not limited. You can unlock these limitless chances by enrolling yourself in the finest data science training in Marathahalli. RIA has been ranked by Google as the institute that offers the best data science courses in Bangalore, and we aid you to fulfill your dreams with the help of our dedicated mentors.

Why get data science training in Marathahalli

There has been an enormous demand for data scientists and studies have fortified the fact that this necessitates will only intensify in the near future. With tons of data produced by thousands of firms, the job requirement for data scientists is said to be limitless. Moreover, it is a highly paid job, with high security, and registering yourself in a data science course in Bangalore will help to smooth your future pathways. It lets you develop high skills in decision formulation, predictive analysis, and pattern identification.

Since individuals with quality skills are in short supply, getting data science training in Marathahalli will be your golden ticket to a limitless future.

Why Choose The RIA data science course in Bangalore

We have been in the field of instruction and mentorship for more than a decade and have already successfully trained more than two thousand students. We accelerate your learning experience by providing flexible timings, and you can cherry-pick your timings based on your easiness and comfort. In our data science course in Bangalore, there are regular classes on weekdays and weekends to select from. We impart classes to both freshers and young professionals alike. If you wish for a career shift, then joining our data science training in Marathahalli is the top preference to elect.

You will be assisted in every part of the course with our passionate mentors. Our teachers are certified professionals, who hold years of experience in education and they create a student-interest aligned teaching method that positions you as the locus point. RIA institute for the data science course in Bangalore has an engaging classroom learning environment that nourishes your knowledge and understanding of the subject through real-life examples. We will be provided answers for all your quires and tips to augment your proficiency. We believe that adopting a practical approach to apply the hypothetical knowledge will help your future studies, hence have formulated a course that encourages the real-world practice of theories. While studying our data science course in Bangalore you will be benefited from our advanced lab facilities, and project assistance. We nurture a healthy bond between students and teachers to build an all-inclusive environment.

Our novel curriculum is thoughtfully calibrated to cover all essential features of data science. Our experts and students of the data science course in Bangalore have been working with highly reputed MNCs in and around the country. The courses are designed to enhance your chance of employability and is constantly updated according to present-day relevance. Taking the RIA data science course in Bangalore will be a total save for you, as we have an affordable and unique syllabus devised for an all-encompassing audience.

Syllabus

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 - Dictionaries
Python - Built in functions
Python - Built in Modules

Module 2. Jupyter and Numpy

Python Numpy - Introduction
Python Nump - Creating an Array
Python Nump - Reading Text Files
Python Nump - Array Indexing
Python Nump - N-Dimensional Arrays
Python Nump - Data Types
Python Nump - Array Math
Python Nump - Array Comparison and Filtering
Python Nump - 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
Data Types and Variables in R
Operators 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
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 wh logistic regression is a better alternative for classification
Brief overview of probabilit, 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

Module 11. K-Means Clustering

Module 12. KNN

Module 13. Artificial Neural Network

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 -Get Data
Python Project -Data Exploration
Python Project -Data Pre-Processing
Python Project -Feature Engineering
Python Project -Model Training
Python Project -Feature Engineering
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