For whom SQL is? : IT or NON IT people can easily enter into leading Database Technology which is commonly used for all domain application software as back-end. There are so many reason to go ahead : Oracle SQL is basic learning platform to get in IT, which is easy to learn. On completion Oracle SQL training you can attempt certification exam. Certified candidate can take benefit as IT document and can get good opportunity in IT world. Oracle SQL is easy for any support project for initial stage which get into IT carrier. In case you want to grow for development environment than it’s entry for programming. Now a days SQL is basic skill set for any IT/NON IT folks, It’s always helpful for any functional, support and technical consultant.

Highlights : Python is a widely used general-purpose, high-level programming language. Python supports multiple programming paradigms, including object-oriented, imperative and functional programming or procedural styles. It features a dynamic type system and automatic memory management and has a large and comprehensive standard library.The official repository of third-party software for Python contains more than 38,000 packages covering a wide range of functionality. Audience : Application programmers, automation engineer, testers, system administrators, web-crawlers and UNIX/NT power users. Pre-requisite : Basic of UNIX or Windows For whom Python is? IT folks who want to excel or change their profile in a most demanding language which is in demand by almost all clients in all domains because of below mentioned reasons- Python is open source (Cost saving) Python has relatively few keywords, simple structure, and a clearly defined syntax. This allows the student to pick up the language in a relatively short period of time. Python comes with a large collection of prebuilt and portable functionality known as the standard library. Python has more than 20 Thousand modules. Every new development comes very early in Python like Hadoop interface, Raspberry Pi and many more! Python can run on a wide variety of hardware platforms and has the same interface on all platforms. You can add low-level modules to the Python interpreter. These modules enable programmers to add to or customize their tools to be more efficient. Django framework might be the most famous Python web framework, there is also a host of successful small and micro-frameworks. Who use Python? Google makes extensive use of Python in its web search system, and employs Python’s creator Guido van Rossum. The YouTube video sharing service is largely written in Python. Disney uses Python in many of their creative processes. Mozilla uses Python to explore their extensive code base and releases tons of open source packages built in python. Dropbox file hosting service is implemented using Python, Guido van Rossum now working here. The popular Bit Torrent peer-to-peer file sharing system is a Python program. Intel, Cisco, Hewlett-Packard, Seagate, Qualcomm, and IBM use Python for hardware testing. JPMorgan Chase, UBS, Getco, and Citadel apply Python for financial market forecasting. NASA, Los Alamos, JPL, use Python for scientific programming tasks. iRobot uses Python to develop commercial robotic vacuum cleaners. The NSA uses Python for cryptography and intelligence analysis. And Many More

Why Python for data science? Overview of Python- Starting with Python Introduction to installation of Python Introduction to Python Editors & IDE’s(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…) Understand Jupyter notebook & Customize Settings Concept of Packages/Libraries – Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc) Installing & loading Packages & Name Spaces Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries) List and Dictionary Comprehensions Variable & Value Labels – Date & Time Values Basic Operations – Mathematical – string – date Reading and writing data Simple plotting Control flow & conditional statements Debugging & Code profiling How to create class and modules and how to call them? Scientific Distributions Used In Python For Data Science NumPy, pandas, scikit-learn, stat models, nltk

DATA SCIENCE COMBO

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Overview

The Syllabus

Curriculum Designed by Experts

ORACLE SQL

For whom SQL is? :

IT or NON IT people can easily enter into leading Database Technology which is commonly used for all domain application software as back-end. There are so many reason to go ahead :
Oracle SQL is basic learning platform to get in IT, which is easy to learn.
On completion Oracle SQL training you can attempt certification exam.
Certified candidate can take benefit as IT document and can get good opportunity in IT world.
Oracle SQL is easy for any support project for initial stage which get into IT carrier. In case you want to grow for development environment than it’s entry for programming.
Now a days SQL is basic skill set for any IT/NON IT folks, It’s always helpful for any functional, support and technical consultant.

Oracle 12c SQL : SQL Basic

WEEK-01 :

[1] Introduction to Oracle DB 12c

Oracle Database 12c: Focus Areas, Fusion Middle-ware, Oracle Cloud, Services, Deployment Methods, RDBMS, Data Model, ER Diagram, Relation DB Terminology, SQL Statements and Development Environment, HR Schema Tables, DB Documentation.

[2] Retrieving Data using SQL SELECT statement

Basic Select Statement, Arithmetic Expression, Defining Null values, Concatenation Operator & Literal, Duplicate Rows, Displaying table structure. Practice Overview.

[3] Restricting & Sorting Data

Use of Where clause, Character Strings & Date Data, Comparison, Logical, Range, Pattern, & Other operators, Rules Precedence, Order By Clause, Substitution variables. Practice Overview .

WEEK-02 :

[4] Single Row Function & Customized Output

SQL function, Single Row Function, Character/Case Functions, Number Functions, Nesting Function, Date Functions etc. Practice Overview.

[5] Conversion & Conditional Expressions

Conversion Function, Implicit/Explicit Data Conversion, General Function with covering NVL, NVL2, NULLIF, COALESCE, Conditional Expression with Decode function and Case Expression. Practice Overview.

[6] Reporting Aggregated Data using Group Function

Usages of Group function, Nesting Group Function, Creating Group Data, Restricting group data using Having Clause. Practice Overview.

[7] Displaying Data from Multiple Tables using Join

Type of Joins, Explaining with Natural Join, Using Clause, On Clause, SQL 99 Syntax, Three-Way Join, Self Join, NonEqui Join, Inner Versus Outer Join, Cross Join etc. Practice overview.

WEEK-03 :

[8] Using Sub-queries to solve queries

Scenario to use Sub-query, Rules for Sub-queries, Type of Sub-queries, Single Row Sub-queries and Multi Row Sub-queries. Null value in Sub-query etc. Practice Overview.

SQL Advanced

[9] Using Set Operator

Set Operator Rules, Covering Set Operator as UNION, UNION ALL, INTERSECT, MINUS etc. Practice Overview.

[10] Managing Tables using DML Statements

Data Manipulation Language, Covering INSERT, UPDATE, DELETE & DB Transaction control using COMMIT, ROLLBACK, SAVEPOINT. Use of For update clause in SELECT Statement.

[11] Introduction to Data Definition Language

Database Object, Naming Rules, Various Data Types, CREATE TABLE statement, Constraint guidelines, Defining constraints as NOT NULL, UNIQUE, PRIMARY KEY, FOREIGN KEY & CHECK etc. ALTER TABLE, DROP TABLE Statement. Create Table using Sub-query. Practice Overview.

WEEK-04 :

[12] Introduction to Data Dictionary Views

Data Dictionary Structure, How to use Data Dictionary, USER_OBJECTS, ALL_OBJECT, USER_CONSTRAININTS. TABLE Information, Column Information etc. Practice overview.

[13] Creating Sequence, Synonyms & Indexes

Importance of Sequence, Synonyms & Indexes. Defining Sequence, Synonyms & Indexes & DROP statement etc. Practice overview.

[14] Creating Views

What’s View? Advantage of Views, CREATE Simple & Complex views, Rules for performing DML operation on views. Modifying & Removing a view etc. Practice Overview.

[15] Retrieving Data by using Sub-Query

Multiple Column Sub-query, Column Comparison, Pair & Non Pair Sub-queries, Scalar Sub queries, Co-related Sub-queries, Use of WITH clause. Practice Overview.

PYTHON SCRIPTING

Highlights :

Python is a widely used general-purpose, high-level programming language. Python supports multiple programming paradigms, including object-oriented, imperative and functional programming or procedural styles. It features a dynamic type system and automatic memory management and has a large and comprehensive standard library.The official repository of third-party software for Python contains more than 38,000 packages covering a wide range of functionality.

Audience :

Application programmers, automation engineer, testers, system administrators,
web-crawlers and UNIX/NT power users.

Pre-requisite : Basic of UNIX or Windows

For whom Python is?

IT folks who want to excel or change their profile in a most demanding language which is in demand by almost all clients in all domains because of below mentioned reasons-

Python is open source (Cost saving)
Python has relatively few keywords, simple structure, and a clearly defined syntax. This allows the student to pick up the language in a relatively short period of time.
Python comes with a large collection of prebuilt and portable functionality known as the standard library. Python has more than 20 Thousand modules. Every new development comes very early in Python like Hadoop interface, Raspberry Pi and many more!
Python can run on a wide variety of hardware platforms and has the same interface on all platforms.
You can add low-level modules to the Python interpreter. These modules enable programmers to add to or customize their tools to be more efficient.

Django framework might be the most famous Python web framework, there is also a host of successful small and micro-frameworks.

Who use Python?

Google makes extensive use of Python in its web search system, and employs Python’s creator Guido van Rossum.
The YouTube video sharing service is largely written in Python.
Disney uses Python in many of their creative processes.
Mozilla uses Python to explore their extensive code base and releases tons of open source packages built in python.
Dropbox file hosting service is implemented using Python, Guido van Rossum now working here.
The popular Bit Torrent peer-to-peer file sharing system is a Python program.
Intel, Cisco, Hewlett-Packard, Seagate, Qualcomm, and IBM use Python for hardware testing.
JPMorgan Chase, UBS, Getco, and Citadel apply Python for financial market forecasting.
NASA, Los Alamos, JPL, use Python for scientific programming tasks.
iRobot uses Python to develop commercial robotic vacuum cleaners.
The NSA uses Python for cryptography and intelligence analysis.
And Many More

PYTHON – CORE

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
Overview on Jupyter Notebook
Overview on Command Line

Variables & Data Types
Variables
Numbers
String
Boolean
List
Set
Tuple
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…?
Built-in Module – OS
Built-in Module – sys
Built-in Module – argparse
Built-in Module – statistics
Built-in Module – math

Packages
How to create packages

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

Files & Exception Handling
Writing data to a file
Reading data from a file
Read and Write data from csv file
try…except
try…except…else
finally

Project Work – PYTHON : DATA ANALYSIS

Getting started with Python Libraries
what is data analysis ?
why python for data analysis ?
Essential Python Libraries
Installation and setup
Ipython
2.7 VS 3.5

NumPy Arrays
Creating multidimensional array
NumPy-Data types
Array attributes
Indexing and Slicing
Creating array views and copies
Manipulating array shapes
I/O with NumPy

Working with Pandas
Installing pandas
Pandas dataframes
Pandas Series
Data aggregation with Pandas DataFrames
Concatenating and appending DataFrames
Joining DataFrames
Handling missing data

Data Loading,Storage and file format
Writing CSV files with numpy and pandas
HDF5 format
Reading and Writing to Excel with pandas
JSON data
Parsing HTML with Beautiful Soup
PyTables

Python Regular Expressions
What are regular expressions?
The match Function
The search Function
Matching vs searching
Search and Replace
Extended Regular Expressions
Wildcard

Python Oracle Database Access
Install the cx_Oracle and other Packages
Create Database Connection
CREATE, INSERT, READ, UPDATE and DELETE Operation
DML and DDL Oepration with Databases
Performing Transactions
Handling Database Errors
Disconnecting Database

PROJECT WORK :

Random password generator	Mini
CLI based scientific calculator	Mini
Instagram bot	Mini
Expense Tracker	Mini
Site connectivity checker	Mini
Lawn Tennis Match Highlight (Can be extended to any sport)	Major
NLP library	Major

Introduction to Data Science with Python

What is analytics & Data Science?
Common Terms in Analytics
Analytics vs. Data warehousing, OLAP, MIS Reporting
Relevance in industry and need of the hour
Types of problems and business objectives in various industries
How leading companies are harnessing the power of analytics?
Critical success drivers
Overview of analytics tools & their popularity
Analytics Methodology & problem solving framework
List of steps in Analytics projects
Identify the most appropriate solution design for the given problem statement
Project plan for Analytics project & key milestones based on effort estimates
Build Resource plan for analytics project

Python Essentials

- Why Python for data science?
- Overview of Python- Starting with Python
- Introduction to installation of Python
- Introduction to Python Editors & IDE’s(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
- Understand Jupyter notebook & Customize Settings
- Concept of Packages/Libraries – Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
- Installing & loading Packages & Name Spaces
- Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
- List and Dictionary Comprehensions
- Variable & Value Labels – Date & Time Values
- Basic Operations – Mathematical – string – date
- Reading and writing data
- Simple plotting
- Control flow & conditional statements
- Debugging & Code profiling
- How to create class and modules and how to call them?
Scientific Distributions Used In Python For Data Science
NumPy, pandas, scikit-learn, stat models, nltk

Accessing/Importing And Exporting Data Using Python Modules

- Importing Data from various sources (Csv, txt, excel, access etc)
- Database Input (Connecting to database)
- Viewing Data objects – subsetting Data, methods
- Exporting Data to various formats
- Important python modules : Pandas, beautiful soup

Data Manipulation – Cleansing – Munging using python modules

Cleansing Data with Python
Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
Python Built-in Functions (Text, numeric, date, utility functions)
Python User Defined Functions
Stripping out extraneous information
Normalizing data
Formatting data
Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc.)

Data Analysis – Visualization Using Python

Introduction exploratory data analysis
Descriptive statistics, Frequency Tables and summarization
Univariate Analysis (Distribution of data & Graphical Analysis)
Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
Creating Graphs – Bar/pie/line chart/histogram/boxplot/scatter/density etc.)
Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas and SciPy. Stats etc.)

Introduction to Statistics

Basic Statistics – Measures of Central Tendencies and Variance
Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
Inferential Statistics -Sampling – Concept of Hypothesis Testing Statistical Methods – Z/t-tests (One sample, independent, paired), Analysis of variance, Correlations and Chi-square
Important modules for statistical methods : NumPy, SciPy, Pandas

Introduction to Predictive Modelling

Concept of model in analytics and how it is used?
Common terminology used in analytics & Modelling process
Popular modelling algorithms
Types of Business problems – Mapping of Techniques
Different Phases of Predictive Modelling

Data Exploration For Modelling

Need for structured exploratory data
EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
Identify missing data
Identify outliers data
Visualize the data trends and patterns

Data Preparation

Need of Data preparation
Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction
Variable Reduction Techniques – Factor & PCA Analysis

Segmentation : Solving Segmentation Problems

Introduction to Segmentation
Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
Behavioural Segmentation Techniques (K-Means Cluster Analysis)
Cluster evaluation and profiling – Identify cluster characteristics
Interpretation of results – Implementation on new data

Linear Regression : Solving Regression Problems

Introduction – Applications
Assumptions of Linear Regression
Building Linear Regression Model
Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
Assess the overall effectiveness of the model
Validation of Models (Re running Vs. Scoring)
Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
Interpretation of Results – Business Validation – Implementation on new data

Logistic Regression : Solving Classification Problems

Introduction – Applications
Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
Building Logistic Regression Model (Binary Logistic Model)
Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
Validation of Logistic Regression Models (Re running Vs. Scoring)
Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
Interpretation of Results – Business Validation – Implementation on new data

Time Series Forecasting : Solving Forecasting Problems

Introduction – Applications
Time Series Components (Trend, Seasonality, Cyclicity and Level) and Decomposition
Classification of Techniques (Pattern based – Pattern less)
Basic Techniques – Averages, Smoothening, etc
Advanced Techniques – AR Models, ARIMA, etc
Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc

Machine Learning : Predictive Modelling

Introduction to Machine Learning & Predictive Modelling
Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting
Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
Different Phases of Predictive Modelling (Data Pre-processing, Sampling, Model Building, Validation)
Overfitting (Bias-Variance Trade off) & Performance Metrics
Feature engineering & dimension reduction
Concept of optimization & cost function
Overview of gradient descent algorithm
Overview of Cross validation(Bootstrapping, K-Fold validation etc)
Model performance metrics (R-square, Adjusted R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics)

Unsupervised Learning : Segmentation

What is segmentation & Role of ML in Segmentation?
Concept of Distance and related math background
K-Means Clustering
Expectation Maximization
Hierarchical Clustering
Spectral Clustering (DBSCAN)
Principle component Analysis (PCA)

Supervised Learning : Decision Trees

Decision Trees – Introduction – Applications
Types of Decision Tree Algorithms
Construction of Decision Trees through Simplified Examples; Choosing the “Best” attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules
Decision Trees – Validation
Overfitting – Best Practices to avoid

Supervised Learning : Ensemble Learning

Concept of Ensembling
Manual Ensembling Vs. Automated Ensembling
Methods of Ensembling (Stacking, Mixture of Experts)
Bagging (Logic, Practical Applications)
Random forest (Logic, Practical Applications)
Boosting (Logic, Practical Applications)
Ada Boost
Gradient Boosting Machines (GBM)
XGBoost

Supervised Learning : Artificial Neural Network – ANN

Motivation for Neural Networks and Its Applications
Perceptron and Single Layer Neural Network, and Hand Calculations
Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques
Neural Networks for Regression
Neural Networks for Classification
Interpretation of Outputs and Fine tune the models with hyper parameters
Validating ANN models

Supervised Learning : Support Vector Machines

Motivation for Support Vector Machine & Applications
Support Vector Regression
Support vector classifier (Linear & Non-Linear)
Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
Interpretation of Outputs and Fine tune the models with hyper parameters
Validating SVM models

Supervised Learning : KNN

What is KNN & Applications?
KNN for missing treatment
KNN For solving regression problems
KNN for solving classification problems
Validating KNN model
Model fine tuning with hyper parameters

Supervised Learning : Naive Bayes

Concept of Conditional Probability
Bayes Theorem and Its Applications
Naïve Bayes for classification
Applications of Naïve Bayes in Classifications

Text Mining And Analytics

Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Creating Term-Document (TxD); Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)
Finding patterns in text: text mining, text as a graph
Natural Language processing (NLP)
Text Analytics – Sentiment Analysis using Python
Text Analytics – Word cloud analysis using Python
Text Analytics – Segmentation using K-Means/Hierarchical Clustering
Text Analytics – Classification (Spam/Not spam)
Applications of Social Media Analytics
Metrics(Measures Actions) in social media analytics
Examples & Actionable Insights using Social Media Analytics
Important python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk etc)
Fine tuning the models using Hyper parameters, grid search, piping etc.

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Enrolling in training for the AWS Certified Developer Associate course provides you with in-demand skills such as developing, deploying, and debugging cloud-based applications. The training focuses on AWS services, best practices, and troubleshooting, enhancing your expertise in cloud development and career opportunities.

Career Opportunities

Enrolling in AWS Certified Developer Associate Course Training opens doors to career opportunities like cloud developer, DevOps engineer, and solutions architect. The certification validates your AWS skills, giving you an excellent choice for jobs using the cloud in today's competitive IT market.

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Power Platform Combo FAQs

What are PowerApps?

PowerApps is a suite of apps, services, connectors, and a data platform provided by Microsoft that enables rapid development of custom business applications. These apps can be run on web, mobile, and tablet devices.

What is the difference between Canvas Apps and Model-Driven Apps in PowerApps?

Canvas Apps allow for a highly customizable user experience where you design the app by dragging and dropping elements. Model-Driven Apps, on the other hand, focus on adding your data and business processes to an app, with the layout largely determined by the data model.

How do I use PowerApps with SharePoint?

PowerApps can be used to create custom forms and applications that integrate with SharePoint lists and libraries. You can create, read, update, and delete SharePoint data using PowerApps.

Can I integrate PowerApps with non-Microsoft services?

Yes, PowerApps can integrate with various non-Microsoft services using connectors. There are built-in connectors for popular services like Salesforce, Dropbox, Google Drive, and many others.

How do I manage permissions and security in PowerApps?

Permissions and security in PowerApps are managed through Azure Active Directory and the Common Data Service (CDS). You can assign user roles, define security roles, and control access to apps and data based on these roles.

What is Power Automate?

Power Automate, formerly known as Microsoft Flow, is a service that helps automate workflows between different applications and services to synchronize files, get notifications, collect data, and more.

How do I create a flow in Power Automate?

To create a flow, go to the Power Automate portal, select “Create,” choose a template or start from scratch, add the necessary triggers and actions, and configure them to meet your workflow requirements.

What types of triggers are available in Power Automate?

Power Automate offers a variety of triggers, including automated triggers (e.g., when an email arrives), button triggers (manual triggers), scheduled triggers (recurring flows), and more.

How can I handle errors in Power Automate workflows?

Errors in Power Automate can be handled using the “Configure run after” settings, which allow you to specify what should happen if an action fails, times out, or is skipped. You can also use the “Scope” action to group actions and handle errors at a group level.

Can I use Power Automate with third-party applications?

Yes, Power Automate supports integration with numerous third-party applications through its extensive library of connectors. These connectors enable you to create flows that interact with services like Twitter, Dropbox, Google Drive, and more.

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Most Probable Interview Questions Asked for Power Platform Combo

How do you use PowerApps to create a custom business application? Walk me through the process.
What are the different types of PowerApps (Canvas Apps and Model-Driven Apps), and how do they differ?
How do you integrate PowerApps with other Microsoft services like SharePoint, Dynamics 365, and Power BI?
Explain the concept of connectors in PowerApps. Can you give examples of some commonly used connectors?
How do you manage user permissions and security roles in PowerApps?

What is Power Automate, and how does it help in automating business processes?
Can you describe the process of creating a flow in Power Automate? What are the different types of triggers and actions available?
How do you handle errors and exceptions in Power Automate workflows?
Explain the concept of approval workflows in Power Automate. How would you set one up?
How do you integrate Power Automate with other Microsoft services and third-party applications?

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About AWS Certified Developer Associate Certification

The AWS Certified Developer Associate Certification is a highly sought-after credential for professionals aiming to validate their expertise in developing, deploying, and maintaining applications on the Amazon Web Services (AWS) platform. Designed for individuals with experience in software development and cloud solutions, this certification demonstrates a developer’s ability to create and optimize scalable applications using AWS services.

Overview of AWS Certified Developer Associate Certification

This certification is specifically crafted to equip developers with the knowledge to build robust, secure, and efficient cloud applications. AWS offers a comprehensive suite of tools, and through this certification, developers learn to integrate various AWS services seamlessly into their projects. From writing code that interacts with AWS APIs to understanding how to use key services like Lambda, DynamoDB, and S3, this certification ensures candidates are fully equipped to tackle modern cloud development challenges.

Key Objectives of the Certification

AWS Fundamentals: A strong grasp of core AWS services such as compute (EC2, Lambda), storage (S3, EFS), and databases (RDS, DynamoDB).
Development with AWS Services: Learn to develop, deploy, and debug cloud applications that use AWS services such as SQS, SNS, and API Gateway.
Security Best Practices: Secure cloud applications using AWS Identity and Access Management (IAM), encryption techniques, and data protection mechanisms.
Application Deployment: Understand different deployment strategies using AWS Elastic Beanstalk, CodePipeline, and other CI/CD services for continuous integration and delivery.
Monitoring and Optimization: Learn to monitor and optimize applications using AWS tools like CloudWatch and X-Ray, ensuring cost-effective and high-performance solutions.

Who Should Pursue AWS Certified Developer Associate Certification?

This certification is ideal for:

Software Developers: Who want to enhance their skills in building cloud-based applications and APIs on AWS.
DevOps Engineers: Looking to streamline application deployment and management processes in the cloud.
Cloud Architects: Seeking to integrate various AWS services in their development workflows.
IT Professionals: Interested in migrating traditional applications to the cloud using AWS services.

Prerequisites for AWS Certified Developer Associate

While there are no mandatory prerequisites, it is recommended that candidates have:

1+ years of hands-on experience in AWS application development.
Knowledge of at least one high-level programming language (Python, Java, etc.).
Familiarity with AWS services, their APIs, and use cases in real-world application development.

Benefits of AWS Certified Developer Associate Certification

Career Advancement: This certification is a stepping stone for professionals aspiring to take on higher-level roles such as AWS Solutions Architect or Cloud Developer, making it easier to climb the career ladder.
Competitive Edge: With the growing demand for cloud-based solutions, holding an AWS certification demonstrates your expertise in a field that is becoming increasingly crucial to businesses across industries.
Higher Earning Potential: AWS Certified Developer Associates are in high demand, and companies are willing to pay competitive salaries for certified professionals with proven skills in cloud development.
Comprehensive Cloud Knowledge: By preparing for this certification, you not only learn about AWS but also gain insight into best practices for cloud computing, security, and application development, which can be applied across different cloud platforms.

The AWS Certified Developer Associate Certification is a powerful validation of your ability to develop and maintain applications in the AWS environment. By earning this certification, you are not just demonstrating your technical skills but also positioning yourself as a forward-thinking professional ready to embrace the future of cloud computing.

Applications of AWS Certified Developer Associate

The AWS Certified Developer Associate Certification opens doors to a wide range of applications in cloud development and beyond, making certified professionals highly valuable across multiple industries. Here are key areas where AWS Developer Associates can apply their skills:

1. Building Scalable Applications

AWS Certified Developer Associates are proficient in developing highly scalable, flexible, and reliable applications using AWS cloud services. They leverage tools such as Amazon EC2, AWS Lambda, Elastic Beanstalk, and Amazon RDS to design applications that scale automatically based on demand, ensuring optimal performance and resource utilization.

2. Serverless Application Development

One of the most in-demand skills for cloud developers is serverless computing. AWS Developer Associates use services like AWS Lambda and API Gateway to build applications without managing servers. These serverless architectures reduce operational overhead, optimize costs, and improve application response times by automatically scaling in real-time.

3. Cloud Migration

Certified developers are involved in migrating on-premise applications to the AWS cloud. This involves rewriting legacy code to work with AWS services, ensuring compatibility, security, and efficiency. AWS tools like AWS Migration Hub and CloudEndure enable developers to seamlessly transition applications and data to the cloud with minimal disruption.

4. Integrating Third-Party Services

AWS Developer Associates often work on integrating third-party services and APIs with AWS-powered applications. Using AWS SDKs, developers connect external services, such as payment gateways, messaging platforms, or data analytics tools, with AWS services like S3, DynamoDB, and SNS to create fully integrated solutions.

5. Automated CI/CD Pipelines

Professionals with this certification are skilled in automating the development lifecycle using AWS’s Continuous Integration/Continuous Deployment (CI/CD) tools. By setting up pipelines with AWS CodePipeline, CodeBuild, and CodeDeploy, developers ensure rapid, reliable updates to applications without manual intervention. This allows businesses to deliver features faster and maintain a high level of code quality.

6. Mobile and Web Application Development

AWS Certified Developer Associates play a crucial role in developing mobile and web applications by using services such as Amazon API Gateway, AWS Amplify, and AWS AppSync. They build APIs that serve as the backbone for mobile apps or single-page applications (SPAs), providing backend functionality like user authentication, real-time data sync, and content delivery with CloudFront.

7. Developing Secure Cloud Applications

Cloud security is a critical component of any application. AWS Developer Associates implement security measures using AWS Identity and Access Management (IAM), AWS Shield, and Amazon VPC to ensure data protection, secure APIs, and restrict access to sensitive resources. Their ability to integrate encryption and key management tools further strengthens application security.

8. Microservices Development

Microservices architecture is a popular approach in cloud application development, and AWS Developer Associates are skilled in building, deploying, and managing microservices using AWS tools. They utilize services like Amazon ECS and AWS Fargate to manage containers and run microservices independently, ensuring modular and scalable application architecture.

9. Monitoring and Optimization

Certified AWS Developers use tools like Amazon CloudWatch and AWS X-Ray to monitor application performance, track logs, and trace requests. They are responsible for identifying bottlenecks, troubleshooting issues, and optimizing resource utilization to maintain cost-effective, high-performance cloud environments.

10. Data-Driven Applications

Certified developers are capable of building data-driven applications by integrating services like Amazon RDS, DynamoDB, and Redshift into their solutions. These developers design systems that store, process, and analyze vast amounts of data, enabling organizations to make real-time, data-driven decisions.

11. AI/ML Integration

AWS Developer Associates can also integrate Artificial Intelligence (AI) and Machine Learning (ML) functionalities into applications using services like Amazon SageMaker, AWS Rekognition, and AWS Lex. They incorporate these services to enhance applications with capabilities such as image recognition, predictive analytics, natural language processing, and chatbots.

12. IoT Applications

With the rise of the Internet of Things (IoT), AWS Developer Associates apply their skills to develop IoT applications using services like AWS IoT Core and AWS Greengrass. These applications collect, process, and analyze data from connected devices in real-time, powering smart devices and industrial automation.

13. Cost Optimization for Applications

Cost management is a key part of cloud application development. AWS Certified Developer Associates understand the nuances of AWS Pricing and can configure applications to optimize resource usage and reduce costs. They employ features such as Auto Scaling, EC2 Spot Instances, and S3 Lifecycle Policies to ensure that resources are utilized efficiently.

Data Science Combo Course Certification with Training in Pune

Q: Oracle 12c SQL : SQL Basic

WEEK-01 : [1] Introduction to Oracle DB 12c Oracle Database 12c: Focus Areas, Fusion Middle-ware, Oracle Cloud, Services, Deployment Methods, RDBMS, Data Model, ER Diagram, Relation DB Terminology, SQL Statements and Development Environment, HR Schema Tables, DB Documentation. [2] Retrieving Data using SQL SELECT statement Basic Select Statement, Arithmetic Expression, Defining Null values, Concatenation Operator & Literal, Duplicate Rows, Displaying table structure. Practice Overview. [3] Restricting & Sorting Data Use of Where clause, Character Strings & Date Data, Comparison, Logical, Range, Pattern, & Other operators, Rules Precedence, Order By Clause, Substitution variables. Practice Overview . WEEK-02 : [4] Single Row Function & Customized Output SQL function, Single Row Function, Character/Case Functions, Number Functions, Nesting Function, Date Functions etc. Practice Overview. [5] Conversion & Conditional Expressions Conversion Function, Implicit/Explicit Data Conversion, General Function with covering NVL, NVL2, NULLIF, COALESCE, Conditional Expression with Decode function and Case Expression. Practice Overview. [6] Reporting Aggregated Data using Group Function Usages of Group function, Nesting Group Function, Creating Group Data, Restricting group data using Having Clause. Practice Overview. [7] Displaying Data from Multiple Tables using Join Type of Joins, Explaining with Natural Join, Using Clause, On Clause, SQL 99 Syntax, Three-Way Join, Self Join, NonEqui Join, Inner Versus Outer Join, Cross Join etc. Practice overview. WEEK-03 : [8] Using Sub-queries to solve queries Scenario to use Sub-query, Rules for Sub-queries, Type of Sub-queries, Single Row Sub-queries and Multi Row Sub-queries. Null value in Sub-query etc. Practice Overview.

Q: PYTHON – CORE

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 Overview on Jupyter Notebook Overview on Command Line Variables & Data Types Variables Numbers String Boolean List Set Tuple 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…? Built-in Module – OS Built-in Module – sys Built-in Module – argparse Built-in Module – statistics Built-in Module – math Packages How to create packages Classes & Objects Introduction about classes & objects Creating a class & object Inheritance Methods Overriding Data hiding Files & Exception Handling Writing data to a file Reading data from a file Read and Write data from csv file try…except try…except…else finally

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Our curriculum encompasses a wide range of courses tailored to meet the needs of both beginners and advanced learners. Among our most sought-after programs are the Power Automate Course, Power Automate Training, Power Apps Training, and Powerapps Training in Pune. We also offer specialized courses such as Power Platform Training, Powerapps Training, Microsoft Power Apps Training, and Microsoft Powerapps Training.

Our Powerapps Course and Powerapps Training Course are meticulously designed to cover all essential aspects, ensuring participants gain in-depth knowledge and practical experience. Additionally, we provide Microsoft Power Automate Training, Microsoft Power Apps Course, and Power Apps Training Course to cater to the diverse needs of our students.

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