Data Science Online Course Details of RIA INSTITUTE OF TECHNOLOGY, Bangalore

Course Info

Course Description:

Welcome! 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. Prerequisites 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, opensource, 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 pvalues
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, crossvalidation
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

Topics covered:

Data Science

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Institute Info

RIA INSTITUTE OF TECHNOLOGY

, Bangalore

Faculty	:	MOAHMMED
Duration	:	4 MONTHS Days
Course Fee	:	41,300
Training Type	:	Online
Batch Type	:	Regular, Weekend, Fastrack

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DATA SCIENCE Training ByRIA INSTITUTE OF TECHNOLOGY

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RIA INSTITUTE OF TECHNOLOGY

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