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Data Science: Why It's Important and How to Get Started

data-science-training-in-faridabad

Data science has quickly become one of the most sought-after skills in today’s job market. Large and small businesses are looking to hire professionals who can help them make sense of their data.
Data science helps businesses understand their customers better, allows them to improve their products and services, and it helps companies make more money. But why exactly is data science so important?
In this guide, we’ll look at what data science is, how you can get started learning it, and why it’s such an in-demand skill set these days.

Why is it essential to learn Data Science

Data science is a relatively new branch of study. It came about when computers were getting faster, and people realized an incredible amount of information was generated by them every day. Still, no one was able to understand it properly.

Today, data science is used in almost every industry. It helps companies make more informed decisions because they can now draw on mountains of information they were previously unable to access in any helpful way.

Growth in the field of Data Science

As companies continue to accumulate, compile, and analyze more data about their customers and business operations, the need for professionals with a high-level understanding of how to manipulate and work with that data will grow exponentially.
It may seem far off, but it’s only a matter of time before every industry has someone on staff specializing in data science. And if you want to be at the forefront of your field, it makes sense to start learning now.
Not sure where to begin? Start the Best Data Science Training Institute at Faridabad, only at Dreamer Info Tech.

Applications of Data Science

Data science has been used in various applications, from predicting lottery numbers to discovering genes in human DNA. Below are a few notable examples of how data science is being used
• The number of people who could potentially be diagnosed with Alzheimer’s disease by 2040 can be predicted based on geographical location and other factors. This kind of analysis helps governments allocate resources for research and treatment services more effectively.
• Credit card companies use advanced machine learning algorithms to detect fraud, making it possible to shut down accounts before any money is stolen from customers’ bank accounts.
• The amount of time a customer spends on a website can predict how likely they are to buy something and where they will click next. This information helps marketers create more effective landing pages and ad campaigns.
• Companies like Google use data science to analyze search results so that users get exactly what they want when they type in a query.
• Retailers like Amazon recommend products based on your past purchases, making it easier for you to find what you need while also providing valuable insights into your shopping habits.
• Personalized medicine is becoming increasingly popular as researchers discover new ways to apply data science techniques to healthcare applications. For example, doctors can use genetic information to determine which treatments will work best for individual patients, leading to more effective treatments and better outcomes.
• Insurance companies use machine learning algorithms to predict how likely you are to file a claim to determine your insurance premiums.
• The IRS uses advanced data mining techniques to identify tax fraud before it occurs, allowing them to recover more money from criminals and provide more accurate refunds for law-abiding citizens.

Learn Data Science from Dreamer InfoTech

Dreamer Info Tech is committed to building skills in data science and related fields. We have an experienced team of professionals who can deliver customized training in data science for students and professionals.
Our training programs equip students with solid foundations in programming and systems administration and hands-on experience with tools such as Apache Hadoop, Spark, SAS, R, etc.
All our courses are designed by industry experts with real-world knowledge and a practical approach to learning. If you are looking for a complete package that includes theoretical knowledge and practical application, then look no further than us!
Benefits of taking a Data Science Course from Dreamer InfoTech
1. Hands-on Experience: When you take a data science course from Dreamer Info Tech, you’ll learn how to analyze real data sets from organizations of all sizes. No more experimenting with fake data – that never works! Instead, you’ll be working directly with information about companies, products, and services to produce clear visualizations and statistical results.
2. Learn How to Use Tools like Python & R: The most crucial aspect of data science is understanding how to use tools like Python and R to manipulate large datasets. After taking our courses, you’ll know exactly how these powerful tools work so that you can incorporate them into your projects.
3. Get Hired as a Data Scientist: As we mentioned earlier, employers are looking for professionals who have experience using Python and R and basic knowledge of SQL queries.
4. Make More Money: If you want to make more money at your job or start your own business, then learning data science skills will help you achieve those goals faster than anything else out there.

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AGGREGATE FUNCTIONS
  • MySQL count()
  • MySQL sum()
  • MySQL avg()
  • MySQL min()
  • MySQL max()
MYSQL JOIN

MySQL JOIN

DATA ANALYTICS

DATA PRE-PROCESSING/WRANGLING

NUMPY
  • Fundamentals of NumPy
  • Types of arrays in NumPy
        Ones
        Empty
        Zeros
        Create Random Array
  • Reshaping of the array
         1-D to 2-D array
         2-D to 3-D array
         3-D to 1-D array
  • Arithmetic operations on  Array
         Addition
         Subtraction
         Multiplication
         Exponential
  • Slicing of the array
        How to slice array
        Change in properties during slicing
  • Transpose of a matrix
        How to take transpose
        Rows and columns  while taking transpose
  • Dot product of the matrix
       How to take Dot product of the Matrix
PANDAS
  • Data Frame
         What is Pandas Dataframe
         Different ways to create a Dataframe
        Read data from different sources and convert them to the Dataframe.
  • Pandas Series
        What is panda Series
        How to create a Panda Series.
  • Data inspection
         Using Count, info, head, tail.
         Using Shape, describe, unique, value etc
  • Data Slicing
        Data Slicing loc and iloc
  • Dataframe operations
         How to create a column or row
        How to delete a column or row
    Operations on Rows and Columns
  • Arithmetic operations on rows and
    columns
  • Handle Duplicate value and Null value
  • Handle outlier data
  • Group by Operations
  • Data Reshaping
  • Merging, Joining ,concatenation and
    append
  • Time series
MATPLOTLIB
  • Matplotlib for 3-D visualization
  • Bar, Pie, line, histogram
  • Countplot, boxplot, heatmap
SCIKIT-LEARN
  • Scikit models
  • Preprocessing using Scikit learn
  • Classification using Scikit-learn
  • Clustering using Scikit-learn
  • Regression using Scikit-learn
STATISTICS
  • Scalar and vector
  • Introduction to linear algebra
  • Measure of central tendency mean
  • Measure of central tendency mode
  • Measure of central tendency mode
  • Variance
  • Standard deviation
    Measure of shape skewness
    Measure of shape kurtosis
    Covariance and correlation
PROBABILITY
  • Importance of probability
  • Discrete and continuous variable
  • Bayes Theorem
  • Central Limit theorem
  • Normal Distribution
  • Bernoulli distribution
  • Uniform Distribution
HYPOTHESIS TESTING
  • Hypothesis testing and mechanism
  • Confidence interval
  • Margin of Errors
  • Confidence levels
  • T test and P values using python
  • Z test and P values using python
  • Chi Squared Distribution using python
LINEAR REGRESSION
  • Types of variables
  • Types of Linear variables
  • Mathematics Behind it
  • Implementation of the model
  • Testing and Check
  • Performance of the model
  • Assumptions of the Linear Regression
POLYNOMIAL REGRESSION
  • Need of polynomial Regression
  • Types of Variables in the Polynomial regression
  • Mathematics behind it
  • Implementation of the model
  • Testing and check performance of the
    model
MULTIPLE REGRESSION
  • Need of multiple Regression
  • Mathematics behind it
  • Implementation of the model
  • Testing and check performance of the model
RANDOM FOREST ALGORITHM
  • Why to use random Forest
  • Assumptions in random Forest
  • Working and Implementation of the Model
  • Data Pre-processing in Random Forest
SUPPORT VECTOR MACHINES
  • Types of SVM
  • Implementation of the SVM
  • Creating confusion matrix
NAIVE BAYES CLASSIFIER ALGORITHM
  • Bayes Theoram
  • Implementation of Naïve Bayes
    Classifier
  • Type of Naïve Bayes Model

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JAVASCRIPT EVENTS

Event listeners and handlers
Mouse events
Keyboard events

DOCUMENT OBJECT MODEL (DOM)

Accessing and manipulating HTML elements
DOM navigation and manipulation
Creating and removing HTML elements

1. INTRODUCTION TO REACT.JS

What is React.js?
History and evolution of React.js
React.js components and their role in
building web applications
Setting up the development environment

JSX

Introduction to JSX
Basic syntax and rules of JSX
Embedding expressions in JSX
Conditional rendering in JSX
Working with lists in JSX

REACT COMPONENTS

Understanding components
Creating class components
Creating functional components
Props and state in React components
Lifecycle methods in React components

REACT EVENTS

Handling events in React
Binding event handlers in React
Passing data to event handlers in React
Conditional rendering based on events in
React

REACT FORMS

Creating forms in React
Handling form submission in React
Controlled and uncontrolled components
in React

REACT ROUTING

Introduction to React Router
Setting up routing in React
Creating routes in React

INTRODUCTION TO MONGODB

What is MongoDB?
Advantages of MongoDB over RDBMS
MongoDB data model and architecture
Setting up the development environment

CRUD OPERATIONS

Creating and inserting documents in
MongoDB
Reading documents from MongoDB
Updating documents in MongoDB
Deleting documents from MongoDB

QUERYING MONGODB

Querying MongoDB using find() method
Querying MongoDB using comparison and
logical operators
Querying MongoDB using regular
expressions
Querying MongoDB using aggregation
framework

INTRODUCTION TO NODE.JS

What is Node.js?
Advantages of Node.js over other serverside technologies
Node.js architecture and event-driven
programming model
Setting up the development environment

NODE.JS MODULES

Introduction to Node.js modules
Creating and using built-in modules
Creating and using custom modules
Working with NPM (Node Package
Manager)

NODE.JS HTTP

Creating HTTP server in Node.js
Understanding HTTP methods and status
codes
Handling HTTP requests and responses
Serving static files in Node.js

EXPRESS.JS FRAMEWORK

Introduction to Express.js framework
Creating and configuring Express.js
application
Handling HTTP requests and responses in
Express.js
Implementing middleware in Express.js

NODE.JS DATABASE CONNECTIVITY

Introduction to database connectivity in
Node.js
Connecting to MongoDB database in
Node.js
Performing CRUD operations in Node.js
using MongoDB
Using Mongoose ORM with MongoDB in
Node.js

NODE.JS SECURITY

Introduction to security in Node.js
Understanding and implementing
authentication and authorization in
Node.js
Implementing HTTPS in Node.js
Preventing common security vulnerabilities
in Node.js

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