CORE PYTHON TRAINING IN NOIDA

Python is a popular high-level, open source programming language with a wide range of applications in automation, big data, Data Science, Data Analytics development of games and web applications. It is a flexible, powerful object-oriented and interpreted language. Python is considered to be a programming language with the highest number of job opportunities. Researchers have named Python ‘the next big thing’ as it is being preferred by many young and experienced developers and is a highly paid skill among all the programming languages in the IT space. Ducat offers a comprehensive Core Python training that will help you master fundamentals, advanced theoretical concepts like writing scripts, sequence and file operations in Python while getting hands-on practical experience with the functional applications as the training is blended with hands on assignments, live projects by industry experts having 13+ years experienced and provide placement assistance.

Introduction

• What is Python..?
• A Brief history of Python
• Why Should I learn Python..?
• Installing Python
• How to execute Python program
• Write your first program/li>

Variables & Data Types

• Variables
• Numbers
• String
• Lists ,Tuples & Dictionary

Conditional Statements & Loops

• if…statement
• if…else statement
• elif…statement
• The while…Loop
• The for….Loop

Control Statements

• continue statement
• break statement
• pass statement

Functions

• Define function
• Calling a function
• Function arguments
• Built-in functions

Modules & Packages

• Modules
• How to import a module…?
• Command line arguments
• Packages
• Creating custom packages

Classes & Objects

• Introduction about classes & objects
• Creating a class & object
• Inheritance
• Methods Overriding
• Data hiding

Files & Directories

• Writing data to a file
• Reading data from a file
• Working with csv file
• The os module
• Working with files and directories

Introduction to Sqlite database

• Overview
• Create Database
• Create Table
• Drop Table
• Insert query
• Select query
• Delete and Update query
• WHERE, AND & OR Clause

GUI Programming with Tkinter

• Introduction
• Saying Hello with Labels
• Buttons
• Message Widget
• Entry Widget
• Dialogs
• Radiobutton and Checkboxes
• Creating Menus
• Events and Binds

PYTHON FULL STACK DEVELOPER | FULL STACK DEVELOPER COURSE | PYTHON FULL STACK TRAINING IN NOIDA

📷 4.9 out of 5 based on 4254 Votes.

How to become a python full stack developer?

Table of Contents

• What Is Python Full Stack Developer?
• What You Will Need To Learn To Become A Python Full Stack Developer?
• Tips To Become A Python Full Stack Developer
• What Are The Career Opportunities for Python Full Stack Developers?
• Reasons To Choose Ducat For Full Stack Training
• Frequently Asked Questions(FAQ)
• Frequently Asked Python Full Stack Interview Questions and Answer

Python is one of the most used programming languages for web development. It is the language which is popularly used for back-end development while it is also used for front-end development. It is a very powerful high-level, object-oriented programming language. Python is an interpreted language and interpreters are available for many operating systems, allowing Python code to run on a wide variety of systems. It is a language which has code which can be packaged into stand-alone executable programs. It is one of the most demanded programming languages with thousands of python developers all over the globe who are uplifting their careers and becoming python full-stack developers.

Python is a very powerful high-level, object-oriented programming language. Python is an interpreted language. Python interpreters are available for many operating systems, allowing Python code to run on a wide variety of systems. Using third-party tools, such as Py2exe or Pyinstaller Python code can be packaged into stand-alone executable programs. Django is an extremely widely used framework, and because it’s open source. Django is a web framework which written in python & follows the MVC architectural pattern. It is maintained by the Django software foundation, an independent organization. There are many other frameworks like Pyramid,web2py, Flask, etc. which support developers in the design & maintenance of complex applications. Pyjamas & IronPython can be used to develop the client side of ajax-based applications.

Important: Python works best for prototyping, machine learning apps, OS, language development, games, and graphic designing/ image processing where they can easily develop both client and server software.

Where everyday students want to uplift their career and learn something more to get some better future aspects. They look for an institute which offers a python full-stack developer course where they can get better career prospects for the future. Ducat is the institute which offers the best python full stack developer training where they offer IT training from 20+ years. Where they have professionally trained and experienced faculty members which offers quality education. The institute offers online and offline classes where students can choose according to their preferences. It is an institute where they offer theoretical and practical training and make the students expertise in the field. It has a placement cell which helps the students to build the resume and help the young talent to get a job at top companies with a good salary package.

What Is Python Full Stack Developer?

Python is a versatile high-level programming language which is used for scientific data and has structured as well as unstructured data. Where a python full stack developer has expertise in using Python language for all the applications. It is a language which permits the computer system which executes software and makes it easy to communicate with each other. Python has code which easily interacts with the code which is written with many other languages like C, JavaScript and others which provide an entire web stack. A python full stack developer needs to work on the frontend and backend. While in simple words full stack developers help to create a website which has effective look and efficient functionality.

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Fast Facts: Python is a great programming language to support you in full-stack development. A full-stack web developer is a person who can develop both client and server software. In addition to mastering HTML and CSS, he/she also knows how to: Program a browser (like using JavaScript, jQuery, Angular, or Vue) and Program a server (like using PHP, ASP, Python, or Node).

What You Will Need To Learn To Become A Python Full Stack Developer?

While you are a beginner you need to become an expert on both sides to acquaint yourself and become an expert.

Front End Development:

• It has a minimal list covering where you need to know and started with front-end development:
• It has web fundamentals of HTML, JavaScript, and CSS.
• JavaScript library like jQuery.
• Where front-end JavaScript frameworks like AngularJS, ReactJS, and VueJS.
• It has a CSS framework like Bootstrap.

Back End Development:

• It also has a minimal list covering where you need to get started with back-end development:
• A database like MySQL, MongoDB, PostgreSQL, or SQLite.
• Python back-end frameworks such as Django or Flask.
• Git is a source code management and version control.
• Designing and building application programming interfaces.
• CRUD operation.

Tips To Become A Python Full Stack Developer

Start Learning Front End Web Development: You can start by learning Python and back-end stuff where you have the reverse way as well. If you are a beginner in web development, it is better to start by learning with front-end technology. HTML is a markup language which helps to create the basic part of a website like a header, image, paragraph, and others.

Master The Fundamentals Of Python: Python is a popular, powerful, and syntax-friendly programming language. It is necessary to know the basics of python while it is relatively easy to understand the programming language. It has an understanding of basic syntax, how to write code, how the loop works, how to write functions, how to write a conditional statement, and others. Where you need to start learning with basics and then you need to move to the next step ahead.

Learn A Web Development Framework (Django or Flask): It is time to learn back-end development framework. It is a language which has two frameworks and is available on web development Django and Flask. Django is difficult to learn where they have a rocket science and you need to become a master if you are spending enough time working on these tools. Django has a model view template which has unique features. It is difficult to start and it is simple to become a master of it.

Plan Your Progress With Small Achievable Goals: While learning the python full stack course you need to plan things accordingly where you need to progress on small goals where it involves various languages, frameworks, and techniques. Where you have various concepts to learn and help the structure of a plan which decomposes on the goal and needs to meet the goals with realistic deadlines. It is necessary for you to make an achievable goal and meet the targets.

Build A Web Development Portfolio: For each web developer and engineer as front end, back end, or haystack needs an online portfolio which showcases their work. It helps to update the strongest project where you start applying. It is necessary to learn with front-end development and back-end development where you can add projects and portfolios based on the work and need to build a portfolio entitled about your learning. Where clients know about you when they can easily run the codes and count.

What Are The Career Opportunities for Python Full Stack Developers?

A python full stack developer is one of the excellent career choices where many people look for a career in this platform. There are many job options after python full stack developer training and becoming an expert in the field. They have job options like software engineer, Microsoft developer, process project manager, computer vision engineer, application developer, and others. Where python full stack developer salary is 4.4 LPA which goes to 14.5 LPA in India. While they can easily lookout for a career outside the career and get good salary packages.

Key Takeaways:

• The demand for full-stack developers is high.
• The average salary of full-stack developers in India is around 6 LPA.
• You know about multiple aspects of development.
• It has better productivity.

Reasons To Choose Ducat For Full Stack Training

• Ducat is one of the best institutes for full stack courses and offers quality education for 20+ years.
• We offer online and offline classes where students can choose according to their preferences.
• It has experienced and professional faculty members.
• Students get training according to industry standards and focus on live projects.
• Institute has a well-equipped infrastructure with properly ventilated classes and facility labs and a proper wifi system.
• Students get a properly detailed course syllabus through our expert counselor and easily help to clarify their doubts.
• Easy access to class after completion of the course and easily clear the doubts without any charges.
• We teach the students to build resumes.
• We also offer internships for students.
• We have a placement cell which helps the students to get jobs at top companies with a high salary package.

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Frequently Asked Questions(FAQ)

1: Which institute is best for a python full stack developer?

Ans: Ducat is one of the best institutes for learning python full stack developers.

2: How long does it take to become a python, full stack developer?

Ans: To learn python full stack developer course you can generally learn in a minimum of three months.

3: Is a python full stack developer a good career?

Ans: Yes, python full stack developer is one of the best career choices,s and get many career opportunities with a good salary.

4: What is the average salary of a full-stack developer in India?

Ans: The average salary of a full stack developer is 4-5 LPA.

5: Is python a full stack developer in demand?

Ans: Yes, it is one of the trendiest profiles which is high in demand in 2022.

6: Is a full-stack developer a good career in 2022?

Ans: Yes it is one of the best career choices in 2022.

7: How can I become a full-stack developer in 3 months?

Ans: To become a professional full stack developer enroll yourself at Ducat and become an expert.

8: Can anyone be a full-stack developer?

Ans: To become a full stack developer you need to have knowledge of computer science and basic knowledge of the programming language.

9: Do we offer certificates after completion of the course?

Ans: Yes, we offer certificates after completion of the course.

10: How does your placement team help the students to get the job?

Ans: We have experts in our team who help the students to build their resumes and organize campus drives for young talents to get the job.

Introduction to WEB

• What is Web?
• Web Features?
• W3C and W3C Members
• Introduction to What WG

Core HTML

• Introduction
• Parts in HTML Document
• Version Information
• Head Section
• Meta Information
• Favicons
• Body Section
• HTML FORMS
• Anchors, Images

Advance HTML5

• Introduction
• HTML5 HISTORY
• Why HTML5?
• New Features and Groups
• Structure of HTML5 Document
• Power of HTML5 and Features
• Semantics and Block Level Elements
• HTML5 Forms
• HTML5 Multimedia
• HTML5 Graphics

Core CSS

• Introduction
• CSS Basics
• CSS Introduction
• CSS Syntax
• CSS Versions
• CSS Id & Class
• CSS Styling
• Styling Backgrounds
• Styling Text
• Styling Fonts
• CSS Borders

Advance CSS

• Introduction
• CSS3 Modules
• Selectors
• Box Model
• Backgrounds and Borders
• Text Effects
• 2D/3D Transformations
• Core & Advanced Animations
• Multiple Column Layout
• User Interface

Core JavaScript

• What is Script? Types of Scripts?
• Introduction to JavaScript
• Comments and Types of Comments
• Popup Boxes
• Variables & Operators
• JavaScript Functions and Events
• Conditional Statements
• Looping Control Statement

Advance JavaScript

• Types of Errors
• Exception Handling
• Java Script Objects
• Browser Objects
• Validations in JS

Python

• Introduction to Python
• What is Python?
• History of Python
• Python Versions
• Features of Python
• How to Install Python
• Install Python with Diff IDEs
• Creating Your First Python Program
• Printing to the Screen
• Reading Keyboard Input
• Using Command Prompt and GUI or IDE

Different Modes in Python

• Execute the Script
• Interactive Mode
• Script Mode
• Python Comments
• Working with Python in Unix/Linux/Windows/Mac/Android
• Python New IDEs
• PyCharm IDE
• How to Work on PyCharm
• PyCharm Components
• Debugging process in PyCharm
• SublimeText IDE
• What is PIP?

Variables in Python

• What is Variable?
• Variables in Python
• Constants in Python
• Standard Data Types
• Operators and Operands
• Swap variables
• Type Conversion
• String Handling

Python Conditional Statements

• How to use “if condition” in conditional structures
• if statement (One-Way Decisions)
• if .. else statement (Two-way Decisions)
• How to use “else condition”
• if ..elif .. else statement (Multi-way)
• When “else condition” does not work
• How to use “elif” condition
• How to execute conditional statement with minimal code
• Nested IF Statement

Python LOOPS

• How to use “While Loop”
• How to use “For Loop”
• How to use For Loop for set of other things besides numbers
• Break statements in For Loop
• Continue statement in For Loop
• Enumerate function for For Loop

Python Lists

• Lists are mutable
• Getting to Lists
• List indices
• Traversing a list
• List operations
• List slices
• List methods
• Map, filter and reduce

Python TUPLE

• Advantages of Tuple over List
• Packing and Unpacking
• Comparing tuples
• Creating nested tuple
• Using tuples as keys in dictionaries
• Deleting Tuples
• Slicing of Tuple
• Tuple Membership Test

Python Sets

• How to create a set?
• Iteration Over Sets
• Python Set Methods
• Python Set Operations
• Union of sets
• Built-in Functions with Set
• Python Frozenset

Python Dictionary

• How to create a dictionary?
• Python Hashing?
• Python Dictionary Methods
• Copying dictionary
• Updating Dictionary
• Delete Keys from the dictionary
• Dictionary items() Method
• Sorting the Dictionary
• Python Dictionary in-built Functions

Python Functions

• What is a Function?
• How to define and call a function in Python
• Types of Functions
• Significance of Indentation (Space) in Python
• How Function Return Value?
• Types of Arguments in Functions
• Default Arguments
• Non-Default Arguments
• Keyword Arguments
• Non-keyword Arguments
• Arbitrary Arguments
• Rules to define a function in Python
• Various Forms of Function Arguments
• Scope and Lifetime of variables
• Anonymous Functions/Lambda functions
• Map(), filter(), reduce() functions
  What is a Docstring?
• Python Iterator, Generator,Closer, and Decorator

Advanced Python

• Python Exception Handling
• Python Errors
• Common RunTime Errors in PYTHON
• Abnormal termination
• Chain of importance Of Exception
• Exception Handling
• Try … Except
• Try ..Except .. else
• Try … finally

Python Class and Objects

• Introduction to OOPs Programming
• Object Oriented Programming System
• OOPS Principles
• The basic concept of Object and Classes
• Access Modifiers
• How to define Python classes
• Self-variable in python
• What is Inheritance? Types of Inheritance?
• How does Inheritance work?

Python Regular Expressions

• What is Regular Expression?
• Regular Expression Syntax
• Understanding Regular Expressions
• Regular Expression Patterns
• Literal characters

Bootstrap (Powerful Mobile Front-End Framework)

• What is Responsive Web Designing?
• Typography Features
• Bootstrap Tables, Buttons, Dropdowns, Navbars
• Bootstrap Images
• Bootstrap Responsive utilities
• Bootstrap Glyph icons

Bootstrap Grid System

• What is a Grid?
• What is Bootstrap Grid System?
• MOBILE FIRST STRATEGY
• Working of Bootstrap Grid System
• Media Queries

Grid Options

• Responsive column resets
• Offset columns
• Nested columns

Django Web Framework

• What is a Framework
• Introduction to Django
• Django – Design Philosophies
• History of Django
• Why Django and Features
• Environment setup
• Web Server

MVC Pattern

• MVC Architecture vs MVT Architecture
• Django MVC – MVT Pattern

Getting Started with Django

• Creating the first Project
• Integrating the Project to sublime text
• The Project Structure
• Running the server
• Solving the issues and Migrations
• Database Setup
• Setting Up Your Project

Create an Application

• What Django Follows
• Structure of Django framework
• Model Layer
• What are models
• Model fields
• Querysets

Django – Admin Interface

• Starting the Admin Interface
• Migrations

Views Layer

• Simple View
• Basic view(displaying hello world)
• Functional views, class-based views

Django – URL Mapping

• Organizing Your URLs
• Role of URLs in Django
• Working URLs
• Forms
• Sending Parameters to Views
• Templates layer
• The Render Function

Django Template Language (DTL)

• Role of template layer in Django
• Filters, Tags, Tag if, Tag for, Block and Extend Tags
• Comment Tag, Usage of templates
• Extending base template

Django – Models

• Creating a Model
• Manipulating Data (CRUD)
• Linking Models
• Django – Page Redirection

Django – Sending E-mails

• Sending a Simple E-mail
• Sending Multiple Mails with send_mass_mail
• Sending HTML E-mail
• Sending HTML E-mail with Attachments

Django – Form Processing

• Using Form in a View
• Usage of forms
• Crud operations using forms
• Crispy forms in Django

Django – File Uploading

• Uploading an Image
• Django – Apache Setup

Django – Cookies Handling

• Django – Sessions
• Django – Comments

Django Admin

• Creating Super User
• Using admin in Django
• Adding models to admin
• Adding model objects using admin
• Displaying in cmd using querysets
• Admin interface Customization

DjangoORM(Object Relational Mapping)

DjangoAPI(Application Program Interface)

• Creating a serializer.
• Working with API views.
• Filtering back ends.
• Enabling pagination.
• Executing CRUD operations.
• Managing serializer fields.
• Testing API views.

Static files

• Loading CSS files into templates
• Loading js files into templates
• Uploading images using models
• User authentication

Sample Projects and Websites

• BLOGs Forums
• Ecommerce Web Site
• ToDo

Frequently Asked Python Full Stack Interview Questions and Answers:

1: What is Python?

Ans: Python is an interpreted, high-level, general-purpose programming language. Python is a cross-platform programming language, meaning, it runs on multiple platforms like Windows, Mac OS X, Linux, etc.

2: What is a negative index in Python?

Ans: Negative index is used in python to index starting from the last element of the list, tuple, or any other container class which supports indexing. Python sequences can be indexed in positive and negative numbers. positive index – 0,1,2,3 etc and negative index – -1,-2,-3, etc. -1 refers to the last index, -2 refers to the second last index, and so on.

3: What is the difference between Django and Flask?

Ans: Django can also be used for larger applications. It includes an ORM. Flask is a “microframework” primarily built for a small application with simpler requirements. In a flask, you have to use external libraries. Flask is ready to use.

4: What is docstring in Python?

Ans: Python documentation string is known as docstring, it is a way of documenting Python functions, modules, and classes.

5: What is Multi-Threading?

Ans: The process of improving the performance of the CPU is known as Multi-Threading. Usually, it is seen as the ability of a program to be managed by multiple users at a single time. It is done by the execution of multiple processes that are supported by the operating system.

6: What is a RESTful API?

Ans: The term REST represents Representational State Transfer. It is a compositional style that is utilized to make Web Services. It utilizes HTTP solicitations to access and utilize the information. We can make, update, read, and erase information.

7: How do you keep yourself updated about the new trends in the industry?

Ans: This is a typical question to understand your involvement in technology. A good way to demonstrate your involvement in continuous learning would be by speaking about the community meetups you visit.

8: What should a full-stack developer know?

Ans: A full-stack developer should present with the accompanying:

• Programming Languages: A full-stack developer should have a capacity for more than one programming language like Java, Python, Ruby, C++, and so on. One must become acquainted with various ways of organizing, configuring, executing, and testing the task-dependent on the programming language.
• Front End: One must be acquainted with the front-end advancements like HTML5, CSS3, Angular, and so on. The comprehension of outsider libraries like jQuery, Ajax, SASS, adds more benefits.
• Structures: Proficiency in words that are joined by advancement systems like Spring, Spring Boot, MyBatis, Django, PHP, Hibernate, js, yin, and that’s only the tip of the iceberg.
• Data sets: One should be acquainted with somewhere around one information base. Assuming that you know about MySQL, Oracle, and MongoDB it is adequate.
• Design Ability: The information on model plans like UI and UX configuration is additionally fundamental.

9: How is multithreading used?

Ans: The main purpose of multithreading is to provide multiple threads of execution concurrently for maximum utilization of the CPU. It allows multiple threads to exist within the context of a process such that they execute individually but share their process resources.

10: What is pass in Python?

Ans: Pass means, no-operation Python statement, or in other words, it is a placeholder in a compound statement, where there should be a blank left and nothing has to be written there.

DATA SCIENCE & ML USING PYTHON TRAINING IN NOIDA

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Are you Looking for the Best Institute for Data Science ML using Python training in Noida? DUCAT offers Data Science ML using Python training classes with live projects by expert trainers in Noida. Our Data science machine learning with Python training program in Noida is specially designed for Under-Graduates (UG), Graduates, working professionals, and also for Freelancers. We provide end-to-end learning on Machine learning with Python Domain with deeper dives for creating a winning career for every profile.

This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science! Python is a general-purpose interpreted, interactive, object-oriented, and high-level programming language. Currently, Python is the most popular Language in IT. Python adopted as a language of choice for almost all the domains in IT including Web Development, Cloud Computing (AWS, OpenStack, VMware, Google Cloud, etc.. ), Infrastructure Automations, Software Testing, Mobile Testing, Big Data, and Hadoop, Data Science, etc. This course sets you on a journey in python by playing with data, creating your own application, and also testing the same.

Introduction To Python

• Why Python
• Application areas of python
• Python implementations
• Cpython
• Jython
• Ironpython
• Pypy
• Pythonversions
• Installingpython
• Python interpreter architecture
• Python byte code compiler
• Python virtual machine(pvm)

Writing and Executing First Python Program

• Using interactive mode
• Using script mode
• General text editor and commandwindow
• Idle editor and idleshell
• Understanding print() function
• How to compile python programexplicitly

Python Language Fundamentals

• Character set
• Keywords
• Comments
• Variables
• Literals
• Operators
• Reading input fromconsole
• Parsing string to int, float

Python Conditional Statements

• If statement
• If else statement
• If elif statement
• If elif else statement
• Nested if statement

Looping Statements

• While loop
• For loop
• Nested loops
• Pass, break and continuekeywords

Standard Data Types

• Int, float, complex, bool,nonetype
• Str, list, tuple,range
• Dict, set, frozenset

String Handling

•  
• What is string
• String representations
• Unicode string
• String functions, methods
• String indexing andslicing
• String formatting

Python List

• Creating and accessinglists
• Indexing and slicinglists
• List methods
• Nested lists
• List comprehension

Python Tuple

• Creating tuple
• Accessing tuple
• Immutability of tuple

Python Set

• How to create a set
• Iteration over sets
• Python set methods
• Python frozenset

Python Dictionary

• Creating a dictionary
• Dictionary methods
• Accessing values fromdictionary
• Updating dictionary
• Iterating dictionary
• Dictionary comprehension

Python Functions

• Defining a function
• Calling a function
• Types offunctions
• Function arguments
• Positional arguments, keywordarguments
• Default arguments, non-defaultarguments
• Arbitrary arguments, keyword arbitraryarguments
• Function return statement
• Nested function
• Function as argument
• Function as return statement
• Decorator function
• Closure
• Map(), filter(), reduce(), any()functions
• Anonymous or lambdafunction

Modules & Packages

• Why modules
• Script v/smodule
• Importingmodule
• Standard v/s third partymodules
• Why packages
• Understanding pip utility

File I/O

• Introduction to filehandling
• File modes
• Functions and methods related to filehandling
• Understanding with block

Object Oriented Programming

• Procedural v/s object orientedprogramming
• OOP principles
• Defining a class &objectcreation
• Object attributes
• Inheritance
• Encapsulation
• Polymorphism

Exception Handling

• Difference between syntax errors andexceptions
• Keywords used in exceptionhandling
• try, except, finally, raise,assert
• Types of exceptblocks

Regular Expressions(Regex)

• Need of regularexpressions
• Re module
• Functions /methods related toregex
• Meta characters &specialsequences

GUI Programming

• Introduction to tkinterprogramming
• Tkinter widgets
• Tk, label, Entry, Textbox,Button
• Frame, messagebox, filedialogetc
• Layout managers
• Event handling
• Displaying image

Multi-Threading Programming

• Multi-processing v/s Multi-threading
• Need of threads
• Creating child threads
• Functions /methods related tothreads
• Thread synchronization andlocking

SQL

Introduction to Database

• Database Concepts
• What is DatabasePackage?
• Understanding DataStorage
• Relational Database (RDBMS)Concept

SQL (Structured Query Language)

• SQLbasics
• DML, DDL & DQL
• DDL: create, alter, drop
• SQLconstraints:
• Not null, unique,
• Primary & foreign key, compositekey
• Check, default
• DML: insert, update, delete andmerge
• DQL : select
• Select distinct
• SQLwhere
• SQLoperators
• SQLlike
• SQL orderby
• SQLaliases
• SQLviews
• SQLjoins
• Inner join
• Left (outer) join
• Right (outer) join
• Full (outer) join
• Mysql functions
• Stringfunctions
• Char_length
• Concat
• Lower
• Reverse
• Upper
• Numericfunctions
• Max, min, sum
• Avg, count,abs
• Date functions
• Curdate
• Curtime
• Now

Statistics, Probability &Analytics:

Introduction to Statistics

• Sample or population
• Measures of central tendency
• Arithmetic mean
• Harmonic mean
• Geometric mean
• Mode
• Quartile
• First quartile
• Second quartile(median)
• Third quartile
• Standard deviation

Probability Distributions

• Introduction to probability
• Conditional probability
• Normal distribution
• Uniform distribution
• Exponential distribution
• Right & left skeweddistribution
• Random distribution
• Centrallimittheorem

HypothesisTesting

• Normality test
• Mean test
• T-test
• Z-test
• ANOVA test
• Chi square test
• Correlation and covariance

Numpy Package

• Difference between list and numpyarray
• Vector and matrixoperations
• Array indexing andslicing

Panda Package

Introduction to pandas

• Labeled and structureddata
• Series and dataframe objects

How to load datasets

• From excel
• From csv
• From html table

Accessing data from Data Frame

• at &iat
• loc&iloc
• head() & tail()

Exploratory Data Analysis (EDA)

• describe()
• groupby()
• crosstab()
• boolean slicing /query()

Data Manipulation & Cleaning

• Map(), apply()
• Combining data frames
• Adding/removing rows &columns
• Sorting data
• Handling missing values
• Handling duplicacy
• Handling data error

Handling Date and Time

Data Visualization using matplotlib and seaborn packages

• Scatter plot, lineplot, barplot
• Histogram, pie chart,
• Jointplot, pairplot, heatmap
• Outlier detection usingboxplot

Machine Learning:

Introduction To Machine Learning

• Traditional v/s Machine LearningProgramming
• Real life examples based onML
• Steps of MLProgramming
• Data Preprocessing revised
• Terminology related toML

Supervised Learning

• Classification
• Regression

Unsupervised Learning

• Clustering

KNN Classification

• Math behind KNN
• KNN implementation
• Understanding hyperparameters

Performance metrics

• Math behind KNN
• KNN implementation
• Understanding hyperparameters

Regression

• Math behind regression
• Simple linear regression
• Multiple linear regression
• Polynomial regression
• Boston price prediction
• Cost or loss functions
• Mean absolute error
• Mean squared error
• Root mean squarederror
• Least square error
• Regularization

Logistic Regression for classification

• Theory of logistic regression
• Binary and multiclassclassification
• Implementing titanic dataset
• Implementing iris dataset
• Sigmoid and softmaxfunctions

Support Vector Machines

• Theory of SVM
• SVM Implementation
• kernel, gamma, alpha

Decision Tree Classification

• Theory of decision tree
• Node splitting
• Implementation with iris dataset
• Visualizingtree

Ensemble Learning

• Random forest
• Bagging and boosting
• Voting classifier

Model Selection Techniques

• Cross validation
• Grid and random search for hyper parametertuning

Recommendation System

• Content based technique
• Collaborative filteringtechnique
• Evaluating similarity based oncorrelation
• Classification-based recommendations

Clustering

• K-means clustering
• Hierarchical clustering
• Elbow technique
• Silhouette coefficient
• Dendogram

Text Analysis

• Install nltk
• Tokenize words
• Tokenizing sentences
• Stop words customization
• Stemming and lemmatization
• Feature extraction
• Sentiment analysis
• Count vectorizer
• Tfidfvectorizer
• Naive bayes algorithms

Dimensionality Reduction

• Principal componentanalysis(pca)

Open CV

• Reading images
• Understanding gray scaleimage
• Resizing image
• Understanding haar classifiers
• Face, eyes classification
• How to use webcam in opencv
• Building image dataset
• Capturing video
• Face classification invideo
• Creating model for genderprediction

Tableau

Tableau – Home

• Tableau -overview
• Tableau – environmentsetup
• Tableau – getstarted
• Tableau -navigation
• Tableau – designflow
• Tableau – filetypes
• Tableau – datatypes
• Tableau – showme
• Tableau – dataterminology

Tableau – Data Sources

• Tableau – custom dataview
• Tableau – datasources
• Tableau – extractingdata
• Tableau – fieldsoperations
• Tableau – editingmetadata
• Tableau – datajoining
• Tableau – datablending

Tableau – Work Sheet

• Tableau – addworksheets
• Tableau – renameworksheet
• Tableau – save &deleteworksheet
• Tableau – reorderworksheet
• Tableau – pagedworkbook

Tableau – Calculation

• Tableau -operators
• Tableau -functions
• Tableau – numericcalculations
• Tableau – stringcalculations
• Tableau – datecalculations
• Tableau – tablecalculations
• Tableau – lodexpressions

Tableau – Sorting & Filter

• Tableau – basicsorting
• Tableau – basicfilters
• Tableau – quickfilters
• Tableau – contextfilters
• Tableau – conditionfilters
• Tableau – topfilters
• Tableau – filteroperations

Tableau – Charts

• Tableau – barchart
• Tableau – linechart
• Tableau – piechart
• Tableau -crosstab
• Tableau – scatterplot
• Tableau – bubblechart
• Tableau – bulletgraph
• Tableau – boxplot
• Tableau – treemap
• Tableau – bumpchart
• Tableau – ganttchart
• Tableau -histogram
• Tableau – motioncharts
• Tableau – waterfallcharts
• Tableau –dashboard

Projects

• One project using python &sql
• One project using python &ml
• One dashboard usingtableau

AI USING PYTHON TRAINING IN NOIDA

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Are you Looking for the Best Institute for AI using Python training in Noida? DUCAT offers AI using Python training classes with live projects by the expert trainers in Noida. Our AI using Python training program in Noida is specially designed for Under-Graduates (UG), Graduates, working professionals and also for Freelancers. We provide end-to-end learning on AI using Python Domain with deeper dives for creating a winning career for every profile.

Why To Enroll In Our AI using Python Training Course in Noida?

We Focus on Innovative ideas, High-quality Training, Smart Classes, 100% job assistance, and Opening the doors of opportunities. Our AI using Python Trainees are working across the nation. We at Ducat India, No#1 AI using Python Course in Noida with 100% Placement. Certified Trainers with Over 10,000 Students Trained in AI using Python Course in Noida.

What Our Students Will Get During AI using Python Training Course?

Get dedicated student support, career services, industry expert mentors, and real-world projects. Career Counselling. Timely Doubt Resolution. 50% Salary Hike, Career Counselling Case Studies + Tools + Certificate.

Why learn AI using Python?

It’s continued to be a great option for data scientists who use it for building Machine learning applications or using them and other scientific computations. AI Using Python Training in Noida cuts development time in half with its simple to read syntax and easy compilation feature with easy to learn concepts. Debugging any type of program is a breeze in this language with its built-in debugger. It runs on every famous type of platforms like Windows, Linux/Unix, and Mac OS and has been ported to Java and .NET virtual machines. Python is free to use language, even for commercial products, because of its OSI-approved open source license, so anyone can use it for free. It has been opted as the most preferred Language for AI and the increasing search trends on Python every day also indicates that it is the “Next Big Thing” and a must for aspirants in the AI field.

Job Opportunities after Python Training:

The demand for this language is increasing at a rapid rate in the market. Organizations have been searching for skilled candidates to develop web applications. With AI Using Python Training in Delhi NCR, India, a candidate can easily gain some programming skills. The carrier scope in this field is very large. A candidate can be hired as a Python Developer or a Data Analyst or more. Python plays an important role in your resume. Some other job profiles by learning this are Research Analyst, Software Engineer, Software Developer, and so on. Training is a very important part to be a successful developer or researcher. The good part is that Ducat IT Certification is provided on completing the training successfully. It opens a lot of opportunities for aspiring candidates.

Why Ducat?

Ducat has a dedicated team of highly expert trainers to identify, evaluate, implement, and providing Best AI Using Python Training Institute in Noida for our students. Our Trainers leverage a defined methodology that helps identify opportunities, develop the most optimal resolution and maturely execute the solution. We have the best trainers across the world to provide the Best AI Using Python Training in Noida who are highly qualified and are the best in their field. The Training & Placement cell is committed to providing all attainable help to the students in their efforts to seek out employment and internships in every field. The placement department works beside alternative departments as a team in molding the scholars to the necessities of varied industries. We got proactive and business clued-in Placement Cells that pride itself on a robust skilled network across numerous sectors. It actively coordinates with every student and ensures that they get placed with purported MNCs among six months of graduating. We are the Best AI Using Python Training in Noida.

Introduction To Python

• Why Python
• Application areas of python
• Python implementations
• Cpython
• Jython
• Ironpython
• Pypy
• Python versions
• Installing python
• Python interpreter architecture
• Python byte code compiler
• Python virtual machine(pvm)

Writing and Executing First Python Program

• Using interactive mode
• Using script modeGeneral text editor and command window
  Idle editor and idle shell
• Understanding print() function
• How to compile python program explicitly

Python Language Fundamentals

• Character set
• Keywords
• Comments
• Variables
• Literals
• Operators
• Reading input from console
• Parsing string to int, float

Python Conditional Statements

• If statement
• If else statement
• If elif statement
• If elif else statement
• Nested if statement

Looping Statements

• While loop
• For loop
• Nested loops
• Pass, break and continue keywords

Standard Data Types

• Int, float, complex, bool, nonetype
• Str, list, tuple, range
• Dict, set, frozenset

String Handling

• What is string
• String representations
• Unicode string
• String functions, methods
• String indexing and slicing
• String formatting

Python List

• Creating and accessing lists
• Indexing and slicing lists
• List methods
• Nested lists
• List comprehension

Python Tuple

• Creating tuple
• Accessing tuple
• Immutability of tuple

Python Set

• How to create a set
• Iteration over sets
• Python set methods
• Python frozenset

Python Dictionary

• Creating a dictionary
• Dictionary methods
• Accessing values from dictionary
• Updating dictionary
• Iterating dictionary
• Dictionary comprehension

Python Functions

• Defining a function
• Calling a function
• Types of functions
• Function arguments
• Positional arguments, keyword arguments
• Default arguments, non-default arguments
• Arbitrary arguments, keyword arbitrary arguments
• Function return statement
• Nested function
• Function as argument
• Function as return statement
• Decorator function
• Closure
• Map(), filter(), reduce(), any() functions
• Anonymous or lambda function

Modules & Packages

• Why modules
• Script v/s module
• Importing module
• Standard v/s third party modules
• Why packages
• Understanding pip utility

File I/O

• Introduction to file handling
• File modes
• Functions and methods related to file handling
• Understanding with block

Object Oriented Programming

• Procedural v/s object oriented programming
• OOP principles
• Defining a class & object creation
• Object attributes
• Inheritance
• Encapsulation
• Polymorphism

Exception Handling

• Difference between syntax errors and exceptions
• Keywords used in exception handling
• try, except, finally, raise, assert
• Types of except blocks

Regular Expressions(Regex)

• Need of regular expressions
• Re module
• Functions /methods related to regex
• Meta characters & special sequences

GUI Programming

• Introduction to tkinter programming
• Tkinter widgets
• Tk, label, Entry, Textbox, Button
• Frame, messagebox, filedialogetc
• Layout managers
• Event handling
• Displaying image

Multi-Threading Programming

• Multi-processing v/s Multi- threading
• Need of threads
• Creating child threads
• Functions /methods related to threads
• Thread synchronization and locking

SQL

Introduction to Database

• Database Concepts
• What is Database Package?
• Understanding Data Storage
• Relational Database (RDBMS) Concept

SQL (Structured Query Language)

• SQL basics
• DML, DDL & DQL
• DDL: create, alter, drop
• SQL constraints:
• Not null, unique,
• Primary & foreign key, composite key
• , default
• DML: insert, update, delete and merge
• DQL : select
• Select distinct
• SQL where
• SQL operators
• SQL like
• SQL order by
• SQL aliases
• SQL views
• SQL joins
• Inner join
• Left (outer) join
• Right (outer) join
• Full (outer) join
• Mysql functions
• String functions
• Char_length
• Concat
• Lower
• Reverse
• Upper
• Numeric functions
• Max, min, sum
• Avg, count, abs
• Date functions
• Curdate
• Curtime
• Now

Statistics, Probability &Analytics:

Introduction to Statistics

• Sample or population
• Measures of central tendency
• Arithmetic mean
• Harmonic mean
• Geometric mean
• Mode
• Quartile
• First quartile
• Second quartile(median)
• Third quartile
• Standard deviation

Probability Distributions

• Introduction to probability
• Conditional probability
• Normal distribution
• Uniform distribution
• Exponential distribution
• Right & left skewed distribution
• Random distribution
• Central limit theorem ●

Hypothesis Testing

• Normality test ●
• Mean test●
• T-test●
• Z-test ●
• ANOVA test●
• Chi square test●
• Correlation and covariance●

Numpy Package

• Difference between list and numpy array ●
• Vector and matrix operations ●
• Array indexing and slicing ●

Pandas Package

Introduction to pandas

• Labeled and structured data●
• Series and dataframe objects●

How to load datasets

• From excel●
• From csv●
• From html table ●

Accessing data from Data Frame

• at &iat●
• loc&iloc●
• head() & tail()●

Exploratory Data Analysis (EDA)

• describe()●
• groupby()●
• crosstab()●
• boolean slicing / query()●

Data Manipulation & Cleaning

• Map(), apply()
• Combining data frames
• Adding/removing rows & columns
• Sorting data
• Handling missing values
• Handling duplicacy
• Handling data error

Handling Date and Time

Data Visualization using matplotlib and seaborn packages

• Scatter plot, lineplot, bar plot
• Histogram, pie chart,
• Jointplot, pairplot, heatmap
• Outlier detection using boxplot

Machine Learning:

Introduction To Machine Learning

• Traditional v/s Machine Learning Programming
• Real life examples based on ML
• Steps of ML Programming
• Data Preprocessing revised
• Terminology related to ML

Supervised Learning

• Classification
• Regression

Unsupervised Learning

• Clustering

KNN Classification

• Math behind KNN
• KNN implementation
• Understanding hyper parameters

Performance metrics

• Math behind KNN
• KNN implementation
• Understanding hyper parameters

Regression

• Math behind regression
• Simple linear regression
• Multiple linear regression
• Polynomial regression
• Boston price prediction
• Cost or loss functions
• Mean absolute error
• Mean squared error
• Root mean squared error
• Least square error
• Regularization

Logistic Regression for classification

• Theory of logistic regression
• Binary and multiclass classification
• Implementing titanic dataset
• Implementing iris dataset
• Sigmoid and softmax functions

Support Vector Machines

• Theory of SVM
• SVM Implementation
• kernel, gamma, alpha

Decision Tree Classification

• Theory of decision tree
• Node splitting
• Implementation with iris dataset
• Visualizing tree

Ensemble Learning

• Random forest
• Bagging and boosting
• Voting classifier

Model Selection Techniques

• Cross validation
• Grid and random search for hyper parameter tuning

Recommendation System

• Content based technique
• Collaborative filtering technique
• Evaluating similarity based on correlation
• Classification-based recommendations

Clustering

• K-means clustering
• Hierarchical clustering
• Elbow technique
• Silhouette coefficient
• Dendogram

Text Analysis

• Install nltk
• Tokenize words
• Tokenizing sentences
• Stop words customization
• Stemming and lemmatization
• Feature extraction
• Sentiment analysis
• CountVectorizer
• TfidfVectorizer
• Naive bayes algorithms

Dimensionality Reduction

• Principal component analysis(PCA)

Open CV

• Reading images
• Understanding gray scale image
• Resizing image
• Understanding haar classifiers
• Face, eyes classification
• How to use webcam in open cv
• Building image data set
• Capturing video
• Face classification in video
• Creating model for gender prediction

Deep Learning & Neural Networks:

Introduction To Artificial Neural Network

• What is artificial neural network (ANN)?
• How neural network works?
• Perceptron
• Multilayer perceptron
• Feedforward
• Back propagation

Introduction To Deep Learning

• What is deep learning?
• Deep learning packages
• Deep learning applications
• Building deep learning environment
• Installing tensor flow locally
• Understanding google colab

Tensor Flow Basics

• What is tensorflow?
• Tensorflow 1.x v/s tensorflow 2.x
• Variables, constants
• Scalar, vector, matrix
• Operations using tensorflow
• Difference between tensorflow and numpy operations
• Computational graph

Optimizers

• What does optimizers do?
• Gradient descent (full batch and min batch)
• Stochastic gradient descent
• Learning rate , epoch

Activation Functions

• What does activation functions do?
• Sigmoid function,
• Hyperbolic tangent function (tanh)
• ReLU –rectified linear unit
• Softmax function
• Vanishing gradient problem

Building Artificial Neural Network

• Using scikit implementation
• Using tensorflow
• Understanding mnist dataset
• Initializing weights and biases
• Gradient tape
• Defining loss/cost function
• Train the neural network
• Minimizing the loss by adjusting weights and biases

Modern Deep Learning Optimizers and Regularization

• SGD with momentum
• RMSprop
• AdaGrad
• Adam
• Dropout layers and regularization
• Batch normalization

Building Deep Neural Network Using Keras

• What is keras?
• Keras fundamental for deep learning
• Keras sequential model and functional api
• Solve a linear regression and classification problem with example
• Saving and loading a keras model

Convolutional Neural Networks (CNNs)

• Introduction to CNN
• CNN architecture
• Convolutional operations
• Pooling, stride and padding operations
• Data augmentation
• Building,training and evaluating first CNN model
• Model performance optimization
• Auto encoders for CNN
• Transfer learning and object detection using pre-trained CNN models
• LeNet
• AlexNet
• VGG16
• ResNet50
• Yolo algorithm

Word Embedding

• What is word embedding?
• Word2vec embedding
• CBOW
• Skipgram
• Keras embedding layers
• Visualize word embedding
• Google word2vec embedding
• Glove embedding

Recurrent Neural Networks (RNNs)

• Introduction to RNN
• RNN architecture
• Implementing basic RNN in tensorflow
• Need for LSTM and GRU
• Deep RNN/LSTM/GRU
• Text classification using LSTM
• Prediction for time series problem
• Seq-2-seq modeling
• Encoder-decoder model

Generative Adversarial Networks (GANs)

• Introduction to GAN
• Generator
• Discriminator
• Types of GAN
• Implementing GAN using neural network

Speech Recognition APIs

• Text to speech
• Speech to text
• Automate task using voice
• Voice search on web

Projects(Any Four)

• Stock Price Prediction Using LSTM
• Object Detection
• Attendance System Using Face Recognition
• Facial Expression and Age Prediction
• Neural Machine Translation
• Hand Written Digits& Letters Prediction
• Number Plate Recognition
• Gender Classification
• My Assistant for Desktop
• Cat v/s Dog Image Classification

DATA ANALYTICS USING PYTHON TRAINING IN NOIDA | DATA ANALYTICS WITH PYTHON

📷 4.9 out of 5 based on 4952 Votes.

Are you Looking for the Best Institute for Data Analytics using Python training in Noida? DUCAT offers Data Analytics using Python training classes with live projects by expert trainers in Noida. Our Data Analytics using Python training program in Noida is specially designed for Under-Graduates (UG), Graduates, working professionals, and also for Freelancers. We provide end-to-end learning on Data Analytics using Python Domain with deeper dives for creating a winning career for every profile.

Why learn Data Analytics using Python?

It’s continued to be a great option for data scientists who use it for building Machine learning applications or using them and other scientific computations. Data Analytics Using Python Training in Noida cuts development time in half with its simple-to-read syntax and easy compilation feature with the easy-to-learn concept. Debugging any type of program is a breeze in this language with its built-in debugger. It runs on every famous type of platform like Windows, Linux/Unix, and Mac OS and has been ported to Java and .NET virtual machines. Python is free to use language, even for commercial products, because of its OSI-approved open-source license, so anyone can use it for free. It has been opted as the most preferred Language for Data Analytics and the increasing search trends on Python every day also indicates that it is the “Next Big Thing” and a must for aspirants in the Data Analytics field.

Why To Enroll In Our Data Analytics Using Python Training Course in Noida?

We Focus on Innovative ideas, High-quality Training, Smart Classes, 100% job assistance, and Opening the doors of opportunities. Our Data Analytics using Python Trainees are working across the nation. We at Ducat India, No#1 Data Analytics using Python Course in Noida with 100% Placement. Certified Trainers with Over 10,000 Students Trained in Data Analytics using Python Course in Noida.

What Our Students Will Get During Data Analytics using Python Training Course?

Get dedicated student support, career services, industry expert mentors, and real-world projects. Career Counselling. Timely Doubt Resolution. 50% Salary Hike, Career Counselling Case Studies + Tools + Certificate.

Why Ducat?

Ducat has a dedicated team of highly expert trainers to identify, evaluate, implement, and provide the Best Data Analytics Using Python Training Institute in Noida for our students. Our Trainers leverage a defined methodology that helps identify opportunities, develop the most optimal resolution and maturely execute the solution. We have the best trainers across the world to provide Best Data Analytics Using Python Training in Noida who are highly qualified and are the best in their field.

The Training & Placement cell is committed to providing all attainable help to the students in their efforts to seek out employment and internships in every field. The placement department works beside alternative departments as a team in molding the scholars to the necessities of varied industries. We got proactive and business-clued-in Placement Cells that pride themselves on a robust skilled network across numerous sectors. It actively coordinates with every student and ensures that they get placed with purported MNCs within six months of graduating. We are the Best Data Analytics Using Python Training Institute in Noida.

Introduction To Python

• Why Python
• Application areas of python
• Python implementations
• Cpython
• Jython
• Ironpython
• Pypy
• Python versions
• Installing python
• Python interpreter architecture
• Python byte code compiler
• Python virtual machine(pvm)

Writing and Executing First Python Program

• Using interactive mode
• Using script mode
• General text editor and command window
• Idle editor and idle shell
• Understanding print() function
• How to compile python program explicitly

Python Language Fundamentals

• Character set
• Keywords
• Comments
• Variables
• Literals
• Operators
• Reading input from console
• Parsing string to int, float

Python Conditional Statements

• If statement
• If else statement
• If elif statement
• If elif else statement
• Nested if statement

Looping Statements

• While loop
• For loop
• Nested loops
• Pass, break and continue keywords

Standard Data Types

• Int, float, complex, bool, nonetype
• Str, list, tuple, range
• Dict, set, frozenset

String Handling

• What is string
• String representations
• Unicode string
• String functions, methods
• String indexing and slicing
• String formatting

Python List

• Creating and accessing lists
• Indexing and slicing lists
• List methods
• Nested lists
• List comprehension

Python Tuple

• Creating tuple
• Accessing tuple
• Immutability of tuple

Python Set

• How to create a set
• Iteration over sets
• Python set methods
• Python frozenset

Python Dictionary

• Creating a dictionary
• Dictionary methods
• Accessing values from dictionary
• Updating dictionary
• Iterating dictionary
• Dictionary comprehension

Python Functions

• Defining a function
• Calling a function
• Types of functions
• Function arguments
• Positional arguments, keyword arguments
• Default arguments, non-default arguments
• Arbitrary arguments, keyword arbitrary arguments
• Function return statement
• Nested function
• Function as argument
• Function as return statement
• Decorator function
• Closure
• Map(), filter(), reduce(), any() functions
• Anonymous or lambda function

Modules & Packages

• Why modules
• Script v/s module
• Importing module
• Standard v/s third party modules
• Why packages
• Understanding pip utility

File I/O

• Introduction to file handling
• File modes
• Functions and methods related to file handling
• Understanding with block

Regular Expressions(Regex)

• Need of regular expressions
• Re module
• Functions /methods related to regex
• Meta characters & special sequences

SQL

Introduction to Database

• Database Concepts
• What is Database Package?
• Understanding Data Storage
• Relational Database (RDBMS) Concept

SQL (Structured Query Language)

• SQL basics
• DML, DDL & DQL
• DDL: create, alter, drop
• SQL constraints:
• Not null, unique
• ,
• Primary & foreign key, composite key
• Check, default
• DML: insert, update, delete and merge
• DQL : select
• Select distinct
• SQL where
• SQL operators
• SQL like
• SQL order by
• SQL aliases
• SQL views
• SQL joins
• Inner join
• Left (outer) join
• Right (outer) join
• Full (outer) join
• Mysql functions
• String functions
• Char_length
• Concat
• Lowe
• r
• Reverse
• Uppe
• r
• Numeric functions
• Max, min, sum
• Avg, count, abs
• Date functions
• Curdate
• Curtime●
• Now

Statistics, Probability &Analytics:

Introduction to Statistics

• Sample or population
• Measures of central tendency
• Arithmetic mean
• Harmonic mean
• Geometric mean
• Mode
• Quartile
• First quartile
• Second quartile(median)
• Third quartile
• Standard deviation

Probability Distributions

• Introduction to probability
• Conditional probability
• Normal distribution
• Uniform distribution
• Exponential distribution
• Right & left skewed distribution
• Random distribution
• Central limit theorem
• ●

Hypothesis Testing

• Normality test
• Mean test
• T-test
• Z-test
• ANOVA test
• Chi square test
• Correlation and covariance

Numpy Package

• Difference between list and numpy array
• Vector and matrix operations
• Array indexing and slicing

Pandas Package

Introduction to pandas

• Labeled and structured data
• Series and dataframe objects

How to load datasets

• From excel
• From csv
• From html table

Accessing data from Data Frame

• at &iat
• loc&iloc
• head() & tail()

Exploratory Data Analysis (EDA)

• describe()
• groupby()
• crosstab()
• boolean slicing / query

Data Manipulation & Cleaning

• Map(), apply()
• Combining data frames
• Adding/removing rows & columns
• Sorting data
• Handling missing values
• Handling duplicacy
• Handling data error

Handling Date and Time

Data Visualization using matplotlib and seaborn packages

• Scatter plot, lineplot, bar plot
• Histogram, pie chart,
• Jointplot, pairplot, heatmap
• Outlier detection using boxplot

Advanced Excel

Advanced Excel Course – Overview of the Basics of Excel

• Customizing common options in Excel
• Absolute and relative cells
• Protecting and un-protecting worksheets and cells

– Working with Functions

• Writing conditional expressions (using IF)
• Using logical functions (AND, OR, NOT)
• Using lookup and reference functions (VLOOKUP, HLOOKUP, MATCH, INDEX)
• VlookUP with Exact Match, Approximate Match
• Nested VlookUP with Exact Match
• VlookUP with Tables, Dynamic Ranges
• Nested VlookUP with Exact Match
• Using VLookUP to consolidate Data from Multiple Sheets

Advanced Excel Course – Data Validations

• Specifying a valid range of values for a cell
• Specifying a list of valid values for a cell
• Specifying custom validations based on formula for a cell

Advanced Excel Course – Working with Templates

• Designing the structure of a template
• Using templates for standardization of worksheets

Advanced Excel Course – Sorting and Filtering Data

• Sorting tables
• Using multiple-level sorting
• Using custom sorting
• Filtering data for selected view (AutoFilter)
• Using advanced filter options

Advanced Excel Course – Working with Reports

• Creating subtotals
• Multiple-level subtotals
• Creating Pivot tables
• Formatting and customizing Pivot tables
• Using advanced options of Pivot tables
• Pivot charts
• Consolidating data from multiple sheets and files using Pivot tables
• Using external data sources
• Using data consolidation feature to consolidate data
• Show Value As ( % of Row, % of Column, Running Total, Compare with Specific Field)
• Viewing Subtotal under Pivot
• Creating Slicers ( Version 2010 & Above)

Advanced Excel Course – More Functions

• Date and time functions
• Text functions
• Database functions
• Power Functions (CountIf, CountIFS, SumIF, SumIfS)

Advanced Excel Course – Formatting

• Using auto formatting option for worksheets
• Using conditional formatting option for rows, columns and cells

Advanced Excel Course – Macros

• Relative & Absolute Macros
• Editing Macro’s

Advanced Excel Course – WhatIf Analysis

• Goal Seek
• Data Tables
• Scenario Manager

Advanced Excel Course – Charts

• Using Charts
• Formatting Charts
• Using 3D Graphs
• Using Bar and Line Chart together
• Using Secondary Axis in Graphs
• Sharing Charts with PowerPoint / MS Word, Dynamically
• (Data Modified in Excel, Chart would automatically get updated)

Advanced Excel Course – New Features Of Excel

• Sparklines, Inline Charts, data Charts
• Overview of all the new features

Advanced Excel Course – Final Assignment

• The Final Assignment would test contains questions to be solved at the end of the Course

VBA (VISUAL BASIC FOR APPLICATION) & MACROS

Create a Macro:

• Swap Values, Run Code from a Module, Macro Recorder, Use Relative References,
• FormulaR1C1, Add a Macro to the Toolbar, Macro Security, Protect Macro.

MsgBox:

• MsgBox Function, Input Box Function.

Workbook and Worksheet Object:

• Path and Full Name, Close and Open, Loop through Books and Sheets, Sales Calculator, Files in a Directory, Import Sheets, Programming Charts.

Range Object:

• Current Region, Dynamic Range, Resize, Entire Rows and Columns, Offset, From Active Cell to Last Entry, Union and Intersect, Test a Selection, Possible Football Matches, Font, Background Colors, Areas Collection, Compare Ranges.

Variables:

• Option Explicit, Variable Scope, Life of Variables.

If Then Statement:

• Logical Operators, Select Case, Tax Rates, Mod Operator, Prime Number Checker, Find Second Highest Value, Sum by Color, Delete Blank Cells.

Loop:

• Loop through Defined Range, Loop through Entire Column, Do Until Loop, Step Keyword, Create a Pattern, Sort Numbers, Randomly Sort Data, Remove Duplicates, Complex Calculations, Knapsack Problem.

Macro Errors:

• Debugging, Error Handling, Err Object, Interrupt a Macro, Macro Comments.

String Manipulation:

• Separate Strings, Reverse Strings, Convert to Proper Case, Count Words.

Date and Time:

• Compare Dates and Times, DateDif Function, Weekdays, Delay a Macro, Year Occurrences, Tasks on Schedule, Sort Birthdays.

Events:

• Before DoubleClick Event, Highlight Active Cell, Create a Footer Before Printing, Bills and Coins, Rolling Average Table
• .

Array:

• Dynamic Array, Array Function, Month Names, Size of an Array.

Function and Sub:

• User Defined Function, Custom Average Function, Volatile Functions, ByRef and ByVal.

Application Object:

• Status Bar, Read Data from Text File, Write Data to Text File.

ActiveX Controls:

• Text Box, List Box, Combo Box, Check Box, Option Buttons, Spin Button, Loan Calculator.

User form:

• User form and Ranges, Currency Converter, Progress Indicator, Multiple List Box Selections, Multicolumn Combo Box, Dependent Combo Boxes, Loop through Controls, Controls Collection, User form with Multiple Pages, Interactive User form

Tableau

Tableau – Home

• Tableau – overview
• Tableau – environment setup
• Tableau – get started
• Tableau – navigation
• Tableau – design flow
• Tableau – file types
• Tableau – data types
• Tableau – show me
• Tableau – data terminology

Tableau – Data Sources

• Tableau – custom data view
• Tableau – data sources
• Tableau – extracting data
• Tableau – fields operations
• Tableau – editing metadata
• Tableau – data joining
• Tableau – data blending

Tableau – Work Sheet

• Tableau – add worksheets
• Tableau – rename worksheet
• Tableau – save & delete worksheet
• Tableau – reorder worksheet
• Tableau – paged workbook

Tableau – Calculation

• Tableau – operators
• Tableau – functions
• Tableau – numeric calculations
• Tableau – string calculations
• Tableau – date calculations
• Tableau – table calculations
• Tableau – lod expressions

Tableau – Sorting & Filter

• Tableau – basic sorting
• Tableau – basic filters
• Tableau – quick filters
• Tableau – context filters
• Tableau – condition filters
• Tableau – top filters
• Tableau – filter operations

Tableau – Charts

• Tableau – bar chart
• Tableau – line chart
• Tableau – pie chart
• Tableau – crosstab
• Tableau – scatter plot
• Tableau – bubble chart
• Tableau – bullet graph
• Tableau – box plot
• Tableau – tree map
• Tableau – bump chart
• Tableau – gantt chart
• Tableau – histogram
• Tableau – motion charts
• Tableau – waterfall charts
• Tableau – dashboard

Projects

• One project using python &sql
• One dashboard using tableau