PYTHON FOR DATA SCIENCE HANDS ON 2 IN 1
Python and Data Science Professional Institute
Working Professionals and Freshers
Regular Offline and Online Live Training
Week Days and Week Ends
60 Days
•Learn How To Use Python and Data Science.
•You learn how to use Python and Data Science code.
•How to write clean production-ready code using Python and Data Science.
•How to Make and design Web apps Using Python and Data Science.
•Learn or brush up with the basics of Python and Data Science
•Become a Python and Data Science Certified Developer! Learn all Python and Data Science Developer topics
•Learn all the hooks and crooks of Python and Data Science at your pace.
•How to handle different types of data inside a workflow using Python and Data Science.
•Learn to build applications on the most flexible enterprise platform for Distributed applications.
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•Additional Sessions for Doubt Clearing
•Get Training from Certified Professionals
•Learn Core concepts from Leading Instructors
•Immersive hands-on training on Python Programming
•Assignments and test to ensure concept absorption.
•Repeating of lectures allowed (based on seat availability)
•Live project based on any of the selected use cases, involving implementation of the concepts
•Lifetime access to our 24×7 online support team who will resolve all your technical queries, through ticket based tracking system.
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•.Net Developer, PL SQL developer, UI Designer, Data Analyst, Business Analyst
•Iot, Embedded Systems, Bluetooth Low Energy, Bluetooth, Web Designing, Responsive Web Design, Visual Web Developer, Aws, Cloud Computing, Algorithm
•Java Developers, Dot Net Developers, Network Administration, As400, Msbi, C++, Web Services, Webmethods, Software Testing, Manual Testing, Selenium Testing
•Peoplesoft, Business Intelligence, Cloud, Msbi, Cognos, Sharepoint, Db2, Qlikview, Inside Sales
•Web Apps, ios/android/windows, Ux Designers, web/mobile developer, html5/css3/javascript/mobile code, testing, automation, manual, mobile, web, ui
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•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
•D 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
•Introduction to Scikitlearn
•Linear Regression
•Logistic Regression
•KNearest Neighbors
•Decision Trees
•Random Forest
•KMeans Clustering
•Preparing Data for Machine Learning
•Performance Metrics
•BiasVariance Tradeoff
•CrossValidation
•Grid Search
•Wrap Up
•Test Your Knowledge
•Python Data Science Essentials
•Introducing Data Science and Python
•Getting Ready
•A Glance at the Essential Packages
•Introducing the Jupyter Notebook
•Scikitlearn Toy Datasets
•Data Loading and Preprocessing
•Working with Categorical and Text Data
•Creating NumPy Arrays
•NumPys 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
•What Is Data Science?
•1D Arrays with NumPy
•2D Arrays with NumPy
•K-Nearest Neighbors
•K-Means Clustering
•Bias-Variance Tradeoff
•Cross-Validation
•Scikit-learn Toy Datasets
•NumPy’s Fast Operations and Computations
•Introduction to Scikit-learn
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