Python for Data Science

Our Data Science with Python training course is aimed at analysts and software developers who need to create analysis and data visualization solutions using the key functions and libraries available in and around Python. You will benefit from extensive hands-on labs, delivered by an expert Data Science practitioner who can guide you from the basics of data wrangling with Python to using sophisticated libraries to visualize and make predictions based on your data. The Data Science with Python training course comprises two modules - Data Analysis with Python, and Machine Learning with Python, which can be taken individually - let us know if you would like to split your attendance of each module.

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
►Interpreter

►Data types in Python

►String data types

►List

►Dictionary

►Tuple

►Set

►Functions

►Classes

OOPS

Inheritance
Encapsulation
Abstraction

Exceptional handling
Numpy, Pandas

Numpy Tutorial
Pandas Tutorial
Natural Language Processing

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
Creating Model using NLTK Naïve Bayes algorithm

►Recommendation System Application

Web Crawling

Scrapy Introduction
Xpath Introduction
Crawling Application
Machine learning

Basics of Machine Learning
Types of Machine Learning Algorithms
►Supervised

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
►Unsupervised

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