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What is CRM?
What is Salesforce?
What is SaaS – Software as Service ?
What is PaaS – Platform as Service ?
What is IaaS – Infrastructure as Service ?
Salesforce Architecture
Salesforce Licenses
Salesforce Editions
Salesforce Development Life cycle
Creation of Salesforce Accout
Business Hours
Company Information
Fiscal Year
Data Protection and Privacy
Language Settings
Manage Currencies
App Menu
Custom Labels
Custom Settings
Custom Metadata Types
Static Resources
Density Settings
Global Search
Themes & Branding
Creating Users
Mass Email Users
User Licenses Overview
Understanding Record Ownership and Access
Roles and Role Hierarchy
Profiles
User Permissions
Permission Sets and Permission Set Groups
Public groups
Queues
Objects
Custom Objects Vs. Standard Objects
Creation of custom object
Different Data types
Standard Data types –
Text, Email, Number, Currency, URL, Text Area, Rich Text Area, etc.,
Read only Data types
Auto Number, Formula and Rollup summary
Rollup summary Operations
*Sum(), Min(), Max() and Count
Relational Data types
Lookup Vs Master – Detail Relationship
Required, External Id, Default Value and Unique fields
Controlling field/Dependent field?
Making dependent pick list fields
Tab Creation
Types of Tabs
Custom object Tabs
Web Tabs
Visual force Tabs
Lightning Page Tabs
Page Layouts
Introduction
Customize the page layouts.
Defining Page layouts
Adding custom buttons and links
Defining required and read only fields
Adding related list
Adding Sections in Detail page
Record Types
To create and maintain record types for your organization.
Display different page layouts and picklist values based on record types.
Uses of Record Types
Assigning Record types to the Profiles.
Schema Builder
Application
Creation of Application/Custom App
Email Templates
Email Templates – Sending Single emails and Mass emails.
Types of Templates – Text, HTML (Using Letter Head), Custom (without using letter head), Visual force Email Templates.
Merging fields in Email Templates
Email Delivery Settings
Workflows
Defining Workflow
Workflow Actions
Email Alert
Task Creation
Field Updation
Outbound Message
Difference between Evaluation Criteria and Rule Criteria
Time based workflows
Approval Process
Create Approval Process
Approval Actions
Difference between workflow rule and approval process
Process Builder
Creation of Automation Process
Visual Flows and Validations
Creation of Flows
Validation Rules
Standard Validations
Custom Validations
Defining Salesforce Data Validation
Creating Custom Validation Rules
Types Of Security
OWD Access Levels
Sharing Settings and Field Accessibility
Password Policies, Session Settings
Sharing Rules and Manual Sharing
Named Credentials and Remote Site Settings
Trusted URls for redirects
View Setup Audit Trail
Session Settings and Login IP
Certificate and Key Management
File Upload and Download Security
Health Check and User Session Information
Salesforce Data Backup
What is Data Backup?
Advantages for Data Backup
Odaseva (Data Backup Tool)
Salesforce Meta Data Backup
What is Meta data ?
Advantages for Metadata backup
Alation(Meta Data Backup Tool)
Data Loader
Import using Application Import Wizard
Import using Apex Data Loader
Import Wizard Vs Data Loader
Dataloader.io
Workbench
Introduction
Installing an App
Uninstalling an App
Unmanaged Package
Managed Package
Salesforce Chatter & Slack
Public group and Private group
Stream and Feed
Chatter Email settings
Creating Report Types for the Reports
Report Types, Reports & Dashboard Overview
Different Reports
Defining Custom Reports
Advanced Filters
Defining Dashboards
Changesets
What is object oriented programming language?
What is Apex?
When Should I use Apex?
How does Apex Work?
Data Types and Variables (Primitive, Collections, Enums, Variables and Constants)
Expressions and Operators
Control Flow Statements(If-Else, Switch, Loops)
Classes, Objects, and Interface
Sobjects
Data Manipulation Language
SOQL
SOQL Basics
SOQL Optional Clauses
Using SOQL and Collections.
Relationship Queries
Complex SOQL and Collections.
Data Retrieval - SOSL
Apex Classes, Objects
Apex Triggers
Basic Triggers.
Context Variables
Trigger Frame Work.
Batch Apex
Scheduled Apex
Queueable Apex
Future Methods
Enforcing Sharing Rules
Enforcing Object and Field Permissions
Enforce User Mode for Database Operations
Filter SOQL Queries Using WITH SECURITY_ENFORCED
Apex Managed Sharing
Apex Test Classes
Best Apex Practices
Why Lightning Web Components and Why Salesforce Introduced LWC?
Advantages of using Lightning Web Components
Features of Lightning Web components
Set Up Your Development Environment (Install a Code Editor, Set
Up Linting, Set Up a Development Org, Install the Salesforce CLI,
Develop in Scratch Orgs, Develop in Non-Scratch Orgs)
LWC Supported JavaScript, Browsers, experiences and Tools.
Creation of LWC Bundle (folder, html, JavaScript, Configuration, CSS, SVG and Test files)
Fields, Properties (Reactive, Public, Tracked, Boolean, Getter and Private), and Attributes
Decorators and Directives
Component Lifecycle
Lightning Data Service
Work with Records Using Base Components
Use the Wire Service to Get Data
Call Apex Methods
Call APIs from Apex
LWC Modules
Create and Dispatch Events
Handle Events
Configure Event Propagation
Communicate Across the DOM
Enable Debug Mode in Salesforce
Debug Using Chrome DevTools
Install Jest
Run Jest Tests for Lightning Web Components
Debug Jest Tests for Lightning Web Components
Write Jest Tests for Lightning Web Components
Visual force Custom controllers
Using wrapper classes in visual force
Visual force Standard Controllers, Extensions
View State
Ajax
Course Outline
Fundamentals of Web Technology
Tools for Web Development
Basic of HTML
Basic of CSS
JavaScript Learning Path
Variables
Data Types
null vs undefined
Spread Operator
Destructuring
String interpolation
String Methods
Object/JSON Operations
Array Methods
Promise
Modules import and export
QuerySelector
Events
Arrow Function
setTimeout vs setInterval
Creat
Your First Component in LWC
Component Folder Structure
Different Naming Conventions Available in LWC
App Creation and Component Deployment
Local Properties and Data Binding
Methods And Two way Data Binding
@track properties
Getters in LWC
Conditional Rendering
Improved Conditional Rendering
Template Looping
Component Compositionp
Shadow DOM
Accessing Elements in the Componen
Inline and External CSS
Lightning Design System
SLDS design token
Shared CSS
Dynamic CSS
Styling across shadow DOM
Intro to Components Communication
Parent to Child Communication Approaches
Parent To Child Communication using Primitive Datatypes
Parent To Child Communication on action at parent component
Calling Child Method from Parent component
Child to Parent Communication Approaches
Child to Parent Communication Using Simple Event
Child to Parent Communication Using Event with Data
Child to Parent Communication Using Event Bubbling
Building Reusable Component
Our Salesforce training has precisely been developed to reach out to the demand of the learners with keeping in mind the industry standards. This DevOps course will particularly be helpful for the career advancement of the following audience
Graduates from College.
Currently working employees looking to upskill themselves.
Candidates looking for a change in the IT Field.
As such, there are no specific prerequisites for Salesforce Training in Hyderabad. If you are familiar with programming and foundation skills with a sense of curiosity and willingness to learn you are all set for the Salesforce training.
Salesforce Training Classes are conducted over the Weekdays and Weekends through classroom and online sessions. Please get in touch with the team to get exact schedule and timings.
Salesforce Course duration is 2 Months
Weekday Salesforce Training classes will be one hour long and Weekend classes will be three hours long.
Please find the detailed Salesforce course curriculum in the Salesforce training curriculum section.
Yes, we will assist our students with all the interview preparation techniques.
the best environment for upskilling and skilling your career. I've joined for DevOps training over here. The trainer was way too good! Explained everything with detailed real time examples. It has a very good infrastructure. One of the best DevOps training institutes in Hyderabad.
I had completed the DevOps Course. The DevOps training was well organized and structured. I really liked the trainer’s approach. Very supportive and relaxed. His approach has benefited me in terms of confidence. An empowering course – very encouraging with a lot of practical work. The study material, exercises and case studies provided were very good. Working on these case studies will help you a lot to with learning the topics. I would definitely recommend.
Best DevOps Training in Hyderabad with hand on Labs and great exposure to all the other Technologies .Best platform to learn DevOps course.
I took DevOps training in Hyderabad.it is the best institute if you want to make yourself better. The best part is, the classes won't be like someone standing there and giving lectures, there is a lot of interaction between the trainers and the students.
After Taking Devops Training in Hyderabad , I got placed in as a DevOps Engineer. this institute has helped me a lot to unleash my potential.
It has been a very good school of learning, trainer explains every individual concept (Theory/Practical) in-depth when I joined for DevOps training at the Gachibowli branch Hyderabad. It has an extraordinary infrastructure with un-interrupted broadband. Your doubts can be clarified with live projects. Altogether it was an awesome experience learning .
Best place to learn DevOps course in hyderabad, the trainer is well knowledged and trains in depth. Practice sessions helped me a lot and the infrastructure is superb compared to other places. I have cracked my certification exam in DevOps training course with the help of the training here.
I have finished my Devops training in hyderabad . I got the job right after the training. I'm very happy. You should definitely check out us to elevate your career prospects.
Build an XML to CSV converter using Python
This python project which will help students to brush up their basic python skills to build a real-world XML to CSV Converter.
Industry: Data Operations
Tools: Python, XML, CSV, Data Ingestion
Building a Photo Editor from scratch using Flask and Numpy
Numpy is a versatile package to do data operations on matrices, numbers and number operations. With this project we will learn doing number operations on Images and apply filters, cropping, flipping and resizing using Numpy.
Industry: Media and Photography
Tools: Python, Numpy, Image Processing, Matplotlib
Exploratory Data Analysis on Retail Shop Sales data.
Performing exploratory data analysis to find patterns in data which will determine the approach to take in Machine Learning. Exploratory data analysis will help identifying features which can be used or discarded in the Machine Learning approach.
Industry: Retail
Tools: Exploratory Data Analysis, Data Preprocessing, Scaling Techniques, Missing Value Imputations.
Fruit type prediction using K Nearest Neighbors algorithm
K Nearest Neighbor algorithm is used to predict the type of fruit given its mass, height, width and color score. We will understand on how to use KNN algorithm and how to tune the hyperparameters such as n_neighbors and k value.
Industry: Food and Beverages
Tools: Supervised Machine Learning, K Nearest Neighbor Classifier, KNN Algorithm, Pandas, Numpy, Matplotlib
Predict Malignancy in Mammographic Masses using Decision Tree Classifiers
Mammographic masses is a public dataset from UCI machine learning repository which has information on mass shape, structure, density and age of a person. With this project we will use Decision Tree classifier to detect Malignancy (either Malignant or Benign) of the Mammographic Mass
Industry: Healthcare
Tools: Decision Tree Classifier, Sklearn, Pandas, Numpy, Graphviz, Matplotlib
Predict whether a candidate will be shortlisted in H1B Visa process using Random Forest Algorithm.
Every year there are close to 1 lakh 65 thousand applications for H1B Visa processing and only few get shortlisted in the process. A lot of information is used for scrutinizing candidates in the selection process. In this project we will use Random Forest algorithm on past five years of H1B processing data to identify if a H1B application will be selected or rejected in the application process.
Industry: US H1B Visa - USCIS
Tools: Random Forest, Pandas, Sklearn, Numpy, Seaborn, Matplotlib
Predict Breast Cancer using Support Vector Machine algorithm.
Wisconsin Breast Cancer dataset has 569 sample of Breast cancer observations determining Malignancy or Benign state of breast mass. Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. In this project we will use SVM algorithm to detect malignancy in the cell.
Industry: Healthcare
Tools: SVM, Numpy, Pandas, Sklearn
HR Analytics for Attrition Prediction using Logistic Regression
Employee Attrition is an important subject to gauge the satisfaction of the employee in a company. HR departments take various measures to arrest employee attrition. In this project we will use Logistic regression to predict who is the potential employee who is in a verge of leaving the company.
Industry: Human Resources
Tools: Logistic Regression, Sklearn, Numpy, Pandas, Matplotlib
Churn Analysis in Telecommunication:
A customer can be called as a “churner” when he/she discontinue their subscription in a company and move their business to a competitor. Prediction as well as prevention of customer churn brings a huge additional revenue source for every business.
Here, we use a telecom customer data set to classify the set of possible customers who are likely to churn
Industry: Telecommunications
Tools: SVM, K Nearest Neighbors Classifier, Random Forest, Logistic Regression, Pandas, Numpy, Sklearn, Seaborn, Matplotlib
Predicting Housing prices using Regression
Description: Predict the sales price for each house based on input features provided for the house.
Industry: Real Estate
Tools: Linear Regression, Multi Linear Regression, Sklearn, Pandas, Numpy, Matplotlib
Social Network Ads based Prediction
Description: Predicting if a user buys a specific product or not based on the ad populated on the Social Network. Data uses specific demographic information about the user to do the prediction.
Industry: Social Media
Tools: Hierarchical Clustering, Scipy, Sklearn, Pandas, Numpy, Seaborn
Retail Customer segmentation based on spending patterns
Description: Customer analysis plays a crucial role in determining the profitability of Retail companies. Segmentation of the customers based on their purchase patterns helps Retail companies to cluster their user base and serve them effectively.
Industry: Retail
Tools: K-Means, Clustering, Sklearn, Pandas, Numpy, Seaborn
Market Basket Analysis using Apriori Algorithm
Market Basket Analysis is a technique which identifies the strength of association between pairs of products purchased together and identify patterns of co-occurrence. A co-occurrence is when two or more things take place together.
The technique determines relationships of what products were purchased with which other product(s).
Industry: Retail
Tools: Apriori Algorithm, Numpy, Pandas
SMS Spam Detection using Natural Language Processing
In our day-to-day lives, we receive a large number of spam/junk messages either in the form of Text (SMS) or E-mails. It is important to filter these spam messages since they are not truthful or trustworthy.
In this case study, we apply various machine learning algorithms to categorize the messages depending on whether they are spam or not.
Industry: Social Media
Tools: NLP, Sklearn, Logistic Regression
Sentiment analysis on Restaurant Reviews using Natural Language processing and Supervised Learning
It involves in identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral.
Industry: Food and Beverages, E-Commerce
Tools: NLP, Sklearn, Support Vector Machine
Image Classification using Deep Learning
Classifying Images based on the features is a tough problem. With Deep Learning algorithms like CNN it has become fairly easy. In this project we will learn to classify two faces using Convolutional Neural Networks.
Industry: Media
Tools: Deep Learning, Neural Networks, Tensorflow, Keras, CNN
Content Based Recommender Engine using Deep Learning
Content based recommender systems use the content in the data to segment items and then use them for recommending similar items. In this project we will use Deep Learning and Convolutional Neural Networks to perform content-based recommender engine.
Industry: E-Commerce, Retail
Tools: Tensorflow, Keras, CNN, Transfer learning, Deep Learning
Chatbots using Recurrent Neural Networks and Deep Learning
A chatbot is an artificial intelligence (AI) software that can simulate a conversation (or a chat) with a user in natural language through messaging applications, websites, mobile apps or through the telephone. In this project we will build a conversational chatbot using Deep Learning.
Industry: Retail, Banking, E-Commerce, Media
Tools: Tensorflow, Keras, RNN, LSTM, Numpy
Stock market price prediction
This project deals with the predictions of stock market prices using history of Data. It also considers the physical factors vs. psychological, rational and irrational behavior etc. Machine learning techniques implemented in Python acts as game changer for the predictions. Algorithms including Linear regression, LSTM and ARIMA model are used for the same.
Industry: Finance
Tools: Tensorflow, Keras, LSTM, RNN
Exploratory Data analysis of Crime records in Boston
This project analyses data using quantitative prediction of crimes in Boston and drawing visualizations of Trends in the data over the years. Exploratory Data Analysis is carried on the crimes data using lots of techniques from Linear model to Stochastic gradient boosting.
Industry: Defense and Law Enforcement
Tools: Linear Regression, XGBoost, Numpy, Sklearn, Pandas, Matplotlib
