Best Data Science Course in Bangalore

COURSE CONTENT/SYLLABUS FOR DATA SCIENCE COURSE : Duration - 80 Hours

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, 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