Python for Data Science

What is Data science and Data Mining?

• Data science

It's a study of all information, where we represents and turns into valuable resource for business and their strategies.

• Data Mining

Large amounts of structured and unstructured data to identify patterns can help an organization rein in costs, increase efficiencies, recognize new market opportunities and increase the organization's competitive advantage.

Why to learn Python for Data Science?
Generally, Employers looking for candidates to fill data scientist positions that have knowledge capabilities into: 

• Experience with programming languages like Python, Perl, C/C++, and Java
• Proficiency in big data software platforms like Hadoop
• In-depth knowledge of at least a few analytical tools like SAS and R

Acquire new skills with our Python for Data Science

• You can expert in developing predictive systems and creating efficient algorithms to improve data quality
• Identifying, evaluating, designing, and implementing statistical analyses of gathered data to create analytic metrics and tools
• Practicle knowledge over Designing, building, and deploying data analysis systems for large data sets
• You can able to Create algorithms to extract information from large data sets
• Efficiently deploying or establishing, automated processes for model development, validation, implementation, and large-scale data analysis
• Developing metrics and prototypes used to drive business decisions
• Identifying emergent trends and opportunities for future client growth and development

Course Overview
In this curriculum, you will learn about basic concepts of Python, how to use Python both interactively and through a script. You can Create your first variables and acquaint yourself with Python's basic data types, powerful ways to store and manipulate data as well as cool data science tools to start your own analyses. This Course will also help you in master of Python programming concepts such as data operations,  file operations, object-oriented programming and various Python libraries such as Pandas, Numpy, Matplotlib which are essential for Data Science.

Introduction to Python for Data Science Training Course Curriculum

Introduction to Data science

• Basics of Data Science
• What is data science
• AI vs DS vs Machine learning
• Fields of data science
• Applications of Data Science

Big Data

• Definition of Big data
• Applications of Big data
• Hadoop and Spark

Natural Language Processing

• Definition of NLP
• Application of NLP
• Tools and Language

Machine learning

• Definition of Machine learning
• Types of Machine Learning
• Applications of Machine learning
• Tools and Languages

NoSQL Data bases

• Definition
• SQL vs NoSQL Databases
• NoSQL databases tools
• Search Engine technologies

Python Basics

• Installation
• Anaconda
• Environment creation
• Pycharm

â–ºInterpreter

â–ºData types in Python

â–ºString data types

â–ºList

â–ºDictionary

â–ºTuple

â–ºSet

â–ºFunctions

â–ºClasses

OOPS

• Inheritance
• Encapsulation

Abstraction

• Exceptional handling

Numpy, Pandas

• Numpy Tutorial
• Pandas Tutorial

Natural Language Processing- Overview

• Basics of NLP
• Applications of NLP
• Tokenization
• Stopwords
• Stemming and lemmatization
• Part of Speech tagging
• Named entity recognition
• Custom NER system using OpenNLP (java)
• Phrase Handling Application
• Sentiment Analysis Application

Feature Extraction process

• True/False model
• Count Vectorizer
• TF-IDF Vectorizer

â–ºRecommendation System Application

Web Crawling

• Scrapy Introduction
• Xpath Introduction
• Crawling Application

Machine learning

• Basics of Machine Learning
• Types of Machine Learning Algorithms

â–ºSupervised

Classification

• Logistic Regression
• K Nearest Neighbors
• SVM
• Decision Tree
• Random Forest
• Gradient Boosting
• Naïve Bayes

Regression

• Linear Regression
• Polynomial Regression
• SVR
• Decision Tree Regressor
• Random Forest Regressor

â–ºUnsupervised

Clustering

• K Means Clustering
• Hierarchical Clustering

Machine Learning Model Evaluation

• Backward elimination Process
• P value
• R Squared

Class Delievery

• Live Interactive classes with expert

Delievery  Methodology

• We are using an experiential delievering methodology that blends theoretical concepts with hands-on practical learning to ensure a holistic understanding of the subject or course