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Data Science With Python A Complete Guide 3 In 1

Course

DATA SCIENCE WITH PYTHON A COMPLETE GUIDE 3 IN 1

Category

Python and Data Science Certification Course

Eligibility

Freshers and Career Changers

Mode

Online and Offline Classes

Batches

Week Days and Week Ends

Duration :

Daily 2 hrs during Weekdays

Python and Data Science What will you learn?

•Learn about Python and Data Science Practices and guidelines.
•You will learn how to write Python and Data Science.
•Learn how to build an app in Python and Data Science.
•How to Make and design Web apps Using Python and Data Science.
•Learn Python and Data Science in the most efficient and easy way
•Learn Python and Data Scienceat a minimal cost and enjoy the instructor support.
•Learn How to code in Python and Data Science in simple and easy way.
•Go through all the steps to designing a game from start to finish.with this time saving course you will Learn Python and Data Science and ready to use it

data science with python a complete guide 3 in 1 Training Features

•You Get Real Time Project to practice
•Certificate after completion of the course
• Helps you stand out in a competitive market
•Create hands-on projects at the end of the course
•60+ Hours of Intensive Classroom & Online Sessions
•Courseware that is curated to meet the global requirements
•Make aware of code competence in building extensive range of applications using Python
•This Instructor-led classroom course is designed with an aim to build theoretical knowledge supplemented by ample hands-on lab exercises

Who are eligible for Python and Data Science

•c++, React.js, Java Fullstack, Core Java Data Structure, Java Micro-services, Devops, Microsoft Azure, Cloud Computing, Machine Learning, Automation Testing
•Java Developer, Php, Sales Management, Product Management, Software Services, Ui Development, MySQL, MongoDB, Nginx, NoSQL, Solr, Elastic Search, ApacheJava, J2ee, Spring, Hibernate, Microservices, Node.js, Angularjs, Servlets, Sql, Cloud, Python, Ui, Ux, .Net, Asp.net, Peoplesoft, Devops, Php, Javascript
•Qa, Ui/ux, Java Developer, Java Architect, C++/qt, Php, Lamp, Api, J2ee, Java, Soa, Esb, Middleware, Bigdata Achitect, Hadoop Architect, Deep
•Xml Publisher, Php Developer, Android Application Development, Html Tagging, E-publishing, Software Development

DATA SCIENCE WITH PYTHON A COMPLETE GUIDE 3 IN 1 Topics

Learning Python for Data Science
•The Course Overview
•What Is Data Science?
•Python Data Science Ecosystem
•Installing Anaconda
•Starting Jupyter
•Basics of Jupyter
•Markdown Syntax
•1D Arrays with NumPy
•2D Arrays with NumPy
•Functions in NumPy
•Random Numbers and Distributions in NumPy
•Create DataFrames
•Read in Data Files
•Subsetting DataFrames
•Boolean Indexing in DataFrames
•Summarizing and Grouping Data
•Matplotlib Introduction
•Graphs with Matplotlib
•Graphs with Seaborn
•Graphs with Pandas
•Machine Learning
•Types of Machine Learning
•Linear Regression
•Logistic Regression
•K-Nearest Neighbors
•Decision Trees
•Random Forest
•K-Means Clustering
•Preparing Data for Machine Learning
•Performance Metrics
•Bias-Variance Tradeoff
•Cross-Validation
•Grid Search
•Wrap Up
•Python Data Science Essentials
•Introducing Data Science and Python
•Getting Ready
•A Glance at the Essential Packages
•Introducing the Jupyter Notebook
•Scikit-learn Toy Datasets
•Data Loading and Preprocessing
•Working with Categorical and Text Data
•Creating NumPy Arrays
•NumPy’s Fast Operations and Computations
•Introducing EDA
•Building New Features
•Dimensionality Reduction
•The Detection and Treatment of Outliers
•Validation Metrics
•Testing and Validating
•Hyperparameter Optimization
•Feature Selection
•Wrapping Everything in a Pipeline
•Preparing Tools and Datasets
•Linear and Logistic Regression
•Naive Bayes
•An Overview of Unsupervised Learning
•Practical Python Data Science Techniques
•Loading Data into Python
•A New Data Set – Exploratory Analysis
•Getting Data in the Right Shape – Preprocessing and Cleaning
•Tokenization – From Documents to Words
•Stop-Words and Punctuation Removal
•Text Normalization
•Calculating Word Frequencies
•Brief Overview of scikit-learn
•Regression Analysis – Predicting a Quantity
•Binary Classification – Predicting a Label (Out of Two)
•Multi-Class Classification – Predicting a Label (Out of Many)
•Cluster Analysis – Grouping Similar Items
•Time Series Analysis with Pandas
•Building a Movie Recommendation System
•Introduction to Scikit-learn