Best Data Science Training in Marathahalli
Best 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 best data science training in Marathahalli. RIA has been ranked by Google as the institute that offers the best data science courses in Marathahalli, and we aid you to fulfill your dreams with the help of our dedicated mentors. Also we offer best full stack training in Marathahalli

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 Marathahalli 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 the best data science training in Marathahalli will be your golden ticket to a limitless future. We also provide best java training in Marathahalli
Why Choose The RIA data science course in Marathahalli ?
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 Marathahalli, 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 Marathahalli 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 Marathahalli 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 Marathahalli 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 Marathahalli 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
- 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 -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