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Enrolling in a Data Science Course Online opens door to a world driven by information. The quest for the Best Online Data Science Courses is crucial for those aspiring to delve into Data Science and Machine Learning. Let's explore why these courses are essential and what they offer to those aspiring to Become a Data Scientist.
Why Choose a Data Science Course:

In-Demand Skills: Acquiring expertise in Data Science and Machine Learning is crucial for securing lucrative Data Science Jobs in today's market.

Career Growth: Mastery of tools like Machine Learning, Python, and R amplifies your potential for career advancement in the field.

Best Online Data Science Courses:

Comprehensive Learning: Optimal courses cover Data Science essentials, catering to Data Science Course for Freshers and seasoned professionals.

Flexibility: These courses offer the flexibility needed to accommodate diverse schedules, ideal for those seeking remote learning.

The Job Market for Data Science Professionals:

High Demand: The demand for individuals skilled in Data Science is rising across industries, making these courses an essential choice for those seeking an In-Demand Data Science Course to become a data scientist and grab data science jobs quickly.

Lucrative Opportunities: Companies are actively seeking proficient Data Science practitioners for high-paying roles.

Whether you're starting fresh or looking to complement your existing skillset, a quality Data Science Online Course is the gateway to unlocking myriad career opportunities. Equip yourself with these in-demand skills and prepare for a promising future in the data landscape.

This Data Science Course is a comprehensive journey into the world of Data, designed for aspiring Data Scientists and Data Engineers seeking to master the art of data analysis and management.
Data Science Management Course Description:
This course immerses you in the fundamentals of Data Science, covering topics from statistical analysis to machine learning. You'll delve into data interpretation, predictive modeling, and database management. Practical projects and case studies provide hands-on experience in applying theories to real-world scenarios.
Key Highlights for Data Science Management Course:

Data Science Fundamentals: Grasp the core concepts and methodologies essential for analyzing complex data sets.

Machine Learning Techniques: Learn and apply various machine learning algorithms for predictive analysis.

Database Management: Acquire skills in organizing, storing, and retrieving large data sets efficiently.

Practical Applications: Engage in real-world projects to solve data-centric challenges and enhance problem-solving abilities.

Data Science Management: Gain insights into managing data effectively for informed decision-making in various industries.

This In-Demand Data Science Course equips you with the knowledge and practical skills required to thrive in the dynamic field of data science and management. It is the stepping stone towards becoming a proficient Data Scientist or a skilled Data Engineer in today's data-driven landscape.

Pursuing a Data Science Course, particularly the online best Data Science courses in India, can lead to promising career opportunities and lucrative salaries in India. Here is a glimpse into the salary expectations for Data Scientists at various career stages:
Experience Level - Average Annual Salary (INR)
Entry-Level (<1 year) - 500,000
Early Career (1-4 years) - 610,811
Mid-Level (5-9 years) - 1,004,082
Senior-Level (>10 years) - 1,700,000

Entry-Level Data Scientists: Beginners who become a Data Scientist after completing a Data Science course typically start with an average salary of 500,000 annually. This stage is about building foundational skills in Data Science.

Early Career Data Scientists: After gaining some experience, those who have pursued Data Science training can expect to earn around 610,811 per annum. This is a critical phase for applying the learnings from the online best Data Science courses in India.

Mid-Level Data Scientists: With 5-9 years of experience, professionals who continued their journey in Data Science after initially deciding to become a Data Scientist can earn 1,004,082 annually. This period often involves more complex Data Science projects.

Senior-Level Data Scientists: Experienced practitioners in Data Science, especially those who have taken some of the best online Data Science courses, can expect salaries above 1,700,000 annually, reflecting their high level of expertise and experience.

Embarking on a data science course can be a pivotal step in your career. These best online data science courses are tailored to impart essential skills in Python, R, and other analytical tools, crucial for aspiring data scientists. The practical, real-world applications provided in the online best data science courses with placement are invaluable for understanding the complexities of data science.
Key benefits of Data Science Course:
Best Online Data Science Courses not only deliver the required skills but also help you to become a data scientist. Let us see some of the key benefits:

Comprehensive Skill Set: Learn in-demand skills like Python and R, crucial for data science.

Real-World Application: Gain hands-on experience with projects that mimic industry challenges.

Networking Opportunities: Connect with industry professionals and peers through these in-demand data science courses.

Career Versatility: Explore various roles in tech with the adaptable skills acquired.

Increased Earning Potential: Data science professionals are known for their competitive salaries.

A data science course not only equips you with technical prowess but also opens numerous doors for professional advancement. For those aiming to become a data scientist, these courses are a significant investment, leading to a rewarding and dynamic career path in data science.

At Croma Campus, recognized for its comprehensive IT training, we offer one of the online best data science courses with placement tailored for career advancement. This job-oriented data science online course is an excellent choice for those aiming to secure lucrative jobs in data science.
Key features of our online data science course include:

Industry-Relevant Curriculum: Focused on the latest trends and skills demanded in data science.

Hands-on Learning: Practical experience with real-world data science problems.

Placement Support: Specialized data science courses with placement assistance, helping students find data scientist jobs in India and abroad.

Professional Networking: Opportunities to connect with industry experts and alumni, crucial for career growth in data science.

By enrolling in our data science course, students can expect:

Job Market Preparation: The course is designed to meet the specific needs of jobs in data science.

Skill Development: Emphasis on in-demand tools and techniques used in online data science.

Career Guidance: Personalized mentorship and advice to navigate the Data Science jobs in India market effectively.

Our job-oriented data science online course at Croma Campus is an excellent pathway for aspiring data scientists, offering a blend of practical skills and placement assistance to pave the way for a successful career in data science.

The domain of Data Science is evolving at a rapid pace, offering a plethora of opportunities for aspiring professionals. Central to this evolution are Data Science courses, particularly those offered online, which have become a cornerstone for those seeking to enter or advance in this field.
Key Insights into Data Science Education and Career Prospects:

The surge in Data Science relevance has led to a diverse range of Data Science courses, providing foundational and advanced knowledge in the field.

Best online Data Science courses have emerged as a preferred choice for learners worldwide, offering flexibility and a comprehensive curriculum.

With the rise of digital platforms, online Data Science education has become more accessible and practical.

There's a notable increase in jobs in Data Science, reflecting the growing demand across various sectors.

In India, Data Scientist jobs are growing rapidly, making it a lucrative career path for many.

Some Data Science courses with placement guarantee not just learning but also assistance in securing relevant job positions.

The roles of Data Engineer and Data Scientist are extensively covered in these courses, preparing students for specific career paths in the industry.

For those aiming to secure jobs in Data Science or to excel as a Data Scientist in India, enrolling in the best online Data Science courses is a strategic step. These courses not only impart crucial skills but also open doors to a multitude of career opportunities in this dynamic and expanding field.

The current job market is witnessing a remarkable demand for Data Science expertise, reflecting the critical role of data in business decision-making. This trend is driving a surge in jobs in Data Science, spanning various industries and sectors. To cater to this growing need, a range of Data Science courses have been developed, with a special emphasis on online Data Science education, making it accessible to a wider audience.
Current Trends in Data Science:

Data Science Course: These courses lay a solid foundation for understanding and applying data science principles.

The best online Data Science courses provide an immersive learning experience, blending theory with practical applications.

The demand for Data Scientist jobs in India is soaring, reflecting the country's rapidly expanding digital economy.

Jobs in Data Science are not limited to tech companies but are now essential in sectors like healthcare, finance, and retail.

Data Science courses with placement focus on bridging the gap between academic learning and industry requirements.

The evolving roles of Data Engineer and Data Scientist are increasingly highlighted in modern curricula.

Both aspiring and experienced professionals looking to navigate the Data Science field will find that the best online Data Science courses are key to unlocking lucrative career opportunities. These courses not only provide the required technical knowledge but also offer insights into real-world applications, preparing individuals for diverse and challenging roles in the data-driven world.

In today's data-driven world, Data Science has emerged as a pivotal field, and the Best Data Science courses in India are playing a crucial role in shaping future data scientists. These courses offer a blend of theoretical knowledge and practical skills essential for mastering Data Science.

Online Best Data Science Courses in India: These courses are designed to offer a comprehensive understanding of Data Science, integrating the latest tools and techniques.

Data Science Online Course: Each course typically covers areas like machine learning, data analysis, and predictive modeling, providing a well-rounded education.

Online Data Science Learning: These courses are known for their flexibility, allowing learners to balance their studies with other commitments.

Data Science Course Online: Courses often include interactive elements like live sessions, forums, and hands-on projects.

Data Science Certification Course: Many courses also offer certifications upon completion, adding value to the learner's professional profile.

The best online Data Science courses are not just about imparting knowledge; they are about creating a new generation of data professionals capable of handling complex data challenges. Whether for career advancement or personal growth, these Data Science online courses provide an excellent platform for learning and development in the field of Data Science.

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Data Science Certification Training

Data Science is a powerful analytics platform to make discoveries. By using different aspects of computer science, data visualisations, data analytics, statistics, R and Python Programming in data science, you may convert voluminous data into meaningful contents. It's a 9 months Master’s Program in Data Science with Machine Learning, and Deep Learning (including Data Analytics & Cloud Implementation) which includes a 6 months online project internship.

In this program you will learn:

Python for Data Science

Data Analysis and Visualization

Databases – MS SQL and SQL Queries

Statistics for Data Science

Mastering Machine Learning

Understanding Deep Learning

Microsoft Power BI

Mastering Tableau

Cloud: AWS(Amazon Web Services)

Cloud: Microsoft Azure Fundamentals

Data Science - Live Projects

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Data Science is a powerful analytics platform to make discoveries. By using different aspects of computer science, data visualisations, data analytics, statistics, R and Python Programming in data science, you may convert voluminous data into meaningful contents.

Introduction To Python

Installation and Working with Python

Understanding Python variables

Python basic Operators

Understanding the Python blocks.

Python Keyword and Identiers

Python Comments, Multiline Comments.

Python Indentation

Understating the concepts of Operators

Arithmetic
Relational
Logical
Assignment
Membership
Identity

Introduction To Variables

Variables, expression condition and function

Global and Local Variables in Python

Packing and Unpacking Arguments

Type Casting in Python

Byte objects vs. string in Python

Variable Scope

Python Data Type

Declaring and using Numeric data types

Using string data type and string operations

Understanding Non-numeric data types

Understanding the concept of Casting and Boolean.

Strings

List

Tuples

Dictionary

Sets

Control Structure & Flow

Statements – if, else, elif

How to use nested IF and Else in Python

Loops

Loops and Control Statements.

Jumping Statements – Break, Continue, pass

Looping techniques in Python

How to use Range function in Loop

Programs for printing Patterns in Python

How to use if and else with Loop

Use of Switch Function in Loop

Elegant way of Python Iteration

Generator in Python

How to use nested Loop in Python

Use If and Else in for and While Loop

Examples of Looping with Break and Continue Statement

How to use IN or NOT IN keyword in Python Loop.

Python Function, Modules and Packages

Python Syntax

Function Call

Return Statement

Arguments in a function – Required, Default, Positional, Variable-length

Write an Empty Function in Python –pass statement.

Lamda/ Anonymous Function

*args and **kwargs

Help function in Python

Scope and Life Time of Variable in Python Function

Nested Loop in Python Function

Recursive Function and Its Advantage and Disadvantage

Organizing python codes using functions

Organizing python projects into modules

Importing own module as well as external modules

Understanding Packages

Random functions in python

Programming using functions, modules & external packages

Map, Filter and Reduce function with Lambda Function

More example of Python Function

Python Date Time and Calendar

Day, Month, Year, Today, Weekday

IsoWeek day

Date Time

Time, Hour, Minute, Sec, Microsec

Time Delta and UTC

StrfTime, Now

Time stamp and Date Format

Month Calendar

Itermonthdates

Lots of Example on Python Calendar

Create 12-month Calendar

Strftime

Strptime

Format Code list of Data, Time and Cal

Locale’s appropriate date and time

List

What is List.

List Creation

List Length

List Append

List Insert

List Remove

List Append & Extend using “+” and Keyword

List Delete

List related Keyword in Python

List Revers

List Sorting

List having Multiple Reference

String Split to create a List

List Indexing

List Slicing

List count and Looping

List Comprehension and Nested Comprehension

Tuple

What is Tuple

Tuple Creation

Accessing Elements in Tuple

Changing a Tuple

Tuple Deletion

Tuple Count

Tuple Index

Tuple Membership

TupleBuilt in Function (Length, Sort)

Dictionary

Dict Creation

Dict Access (Accessing Dict Values)

Dict Get Method

Dict Add or Modify Elements

Dict Copy

Dict From Keys.

Dict Items

Dict Keys (Updating, Removing and Iterating)

Dict Values

Dict Comprehension

Default Dictionaries

Ordered Dictionaries

Looping Dictionaries

Dict useful methods (Pop, Pop Item, Str , Update etc.)

Sets

What is Set

Set Creation

Add element to a Set

Remove elements from a Set

PythonSet Operations

Frozen Sets

Strings

What is Set

Set Creation

Add element to a Set

Remove elements from a Set

PythonSet Operations

Python Exception Handling

Python Errors and Built-in-Exceptions

Exception handing Try, Except and Finally

Catching Exceptions in Python

Catching Specic Exception in Python

Raising Exception

Try and Finally

Python File Handling

Opening a File

Python File Modes

Closing File

Writing to a File

Reading from a File

Renaming and Deleting Files in Python

Python Directory and File Management

List Directories and Files

Making New Directory

Changing Directory

Python Database Interaction

SQL Database connection using

Creating and searching tables

Reading and Storing cong information on database

Programming using database connections

Contacting user Through Emails Using Python

Installing SMTP Python Module

Sending Email

Reading from le and sending emails to all users

Reading an excel

Working With Excel

Reading an excel le using Python

Writing to an excel sheet using Python

Python| Reading an excel le

Python | Writing an excel le

Adjusting Rows and Column using Python

ArithmeticOperation in Excel le.

Play with Workbook, Sheets and Cells in Excel using Python

Creating and Removing Sheets

Formatting the Excel File Data

More example of Python Function

Complete Understanding of OS Module of Python

Check Dirs. (exist or not)

How to split path and extension

How to get user prole detail

Get the path of Desktop, Documents, Downloads etc.

Handle the File System Organization using OS

How to get any les and folder’s details using OS

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Data visualization is the graphical way to representation of information and data. By using visual elements like graphs, maps and charts. Data visualization tools provide an accessible easy way to see and understand the data.

Data Analysis and Visualization using Pandas.

Statistics

Categorical Data
Numerical Data
Mean
Median
Mode
Outliers
Range
Interquartile range
Correlation
Standard Deviation
Variance
Box plot

Pandas

Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to les
How to get record specic records Using Pandas Adding & Resetting Columns, Mapping with function
Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalue’s
Exploring the Data Plotting, Correlations, and Histograms
Getting statistical information about the data Analysis Concepts, Handle the None Values
Reading les with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
Applying formulas on the columns Basic Grouping, Concepts of Aggre gate Function
Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (Setting Indices)
Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
Exporting the results to Excel Joins
Python | Pandas Data Frame Inner Join
Under sting the properties of Data Frame Left Join (Left Outer Join)
Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
Pandas | Find Missing Data and Fill and Drop NA Appending Data Frame and Data
Pandas | How to Group Data How to apply Lambda / Function on Data Frame
Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)

Data Analysis and Visualization using NumPy and MatPlotLib

NumPy

Introduction to NumPy Numerical Python
Importing NumPy and Its Properties
NumPy Arrays
Creating an Array from a CSV
Operations an Array from a CSV
Operations with NumPy Arrays
Two-Dimensional Array
Selecting Elements from 1-D Array
Selecting Elements from 2-D Array
Logical Operation with Arrays
Indexing NumPy elements using conditionals
NumPy’s Mean and Axis
NumPy’s Mode, Median and Sum Function
NumPy’s Sort Function and More

MatPlotLib

Bar Chart using Python MatPlotLib
Column Chart using Python MatPlotLib
Pie Chart using Python MatPlotLib
Area Chart using Python MatPlotLib
Scatter Plot Chart using Python MatPlotLib
Play with Charts Properties Using MatPlotLib
Export the Chart as Image
Understanding plt. subplots () notation
Legend Alignment of Chart using MatPlotLib
Create Charts as Image
Other Useful Properties of Charts.
Complete Understanding of Histograms
Plotting Different Charts, Labels, and Labels Alignment etc.

Introduction to Data Visualization with Seaborn

Introduction to Seaborn

Introduction to Seaborn
Making a scatter plot with lists
Making a count plot with a list
Using Pandas with seaborn
Tidy vs Untidy data
Making a count plot with a Dataframe
Adding a third variable with hue
Hue and scattera plots
Hue and count plots

Visualizing Two Quantitative Variables

Introduction to relational plots and subplots
Creating subplots with col and row
Customizing scatters plots
Changing the size of scatter plot points
Changing the style of scatter plot points
Introduction to line plots
Interpreting line plots
Visualizing standard deviation with line plots
Plotting subgroups in line plots

Visualizing a Categorical and a Quantitative Variable

Current plots and bar plots
Count plots
Bar plot with percentages
Customizing bar plots
Box plots
Create and interpret a box plot
Omitting outliers
Adjusting the whisk
Point plots
Customizing points plots
Point plot with subgroups

Customizing Seaborn Plots

Changing plot style and colour
Changing style and palette
Changing the scale
Using a custom palette
Adding titles and labels Part 1
Face Grids vs. Axes Subplots
Adding a title to a face Grid object
Adding title and labels Part 2
Adding a title and axis labels
Rotating x-tics labels
Putting it all together
Box plot with subgroups
Bar plot with subgroups and subplots
Well done! What’s next

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Microsoft SQL Server is a relational database management system (RDBMS) that supports a wide variety of transaction processing, business intelligence and analytics applications in corporate IT environments. In order to experiment with data through the creation of test environments, data scientists make use of SQL as their standard tool, and to carry out data analytics with the data that is stored in relational databases like Oracle, Microsoft SQL, MySQL, we need SQL.

SQL Server Fundamentals

SQL Server 2019 Installation

Service Accounts & Use, Authentication Modes & Usage, Instance Congurations

SQL Server Features & Purpose

Using Management Studio (SSMS)

Conguration Tools & SQLCMD

Conventions & Collation

SQL Server 2019 Database Design

SQL Database Architecture

Database Creation using GUI

Database Creation using T-SQL scripts

DB Design using Files and File Groups

File locations and Size parameters

Database Structure modications

SQL Tables in MS SQL Server

SQL Server Database Tables

Table creation using T-SQL Scripts

Naming Conventions for Columns

Single Row and Multi-Row Inserts

Table Aliases

Column Aliases & Usage

Table creation using Schemas

Basic INSERT

UPDATE

DELETE

SELECT queries and Schemas

Use of WHERE, IN and BETWEEN

Variants of SELECT statement

ORDER BY

GROUPING

HAVING

ROWCOUNT and CUBE Functions

Data Validation and Constraints

Table creation using Constraints

NULL and IDENTITY properties

UNIQUE KEY Constraint and NOT NULL

PRIMARY KEY Constraint & Usage

CHECK and DEFAULT Constraints

Naming Composite Primary Keys

Disabling Constraints & Other Options

Views and Row Data Security

Benets of Views in SQL Database

Views on Tables and Views

SCHEMA BINDING and ENCRYPTION

Issues with Views and ALTER TABLE

Common System Views and Metadata

Common Dynamic Management views

Working with JOINS inside views

Indexes and Query tuning

Need for Indexes & Usage

Indexing Table & View Columns

Index SCAN and SEEK

INCLUDED Indexes & Usage

Materializing Views (storage level)

Composite Indexed Columns & Keys

Indexes and Table Constraints

Primary Keys & Non-Clustered Indexes

Stored Procedures and Benets

Why to use Stored Procedures

Types of Stored Procedures

Use of Variables and parameters

SCHEMABINDING and ENCRYPTION

INPUT and OUTPUT parameters

System level Stored Procedures

Dynamic SQL and parameterization

System functions and Usage

Scalar Valued Functions

Types of Table Valued Functions

SCHEMABINDING and ENCRYPTION

System Functions and usage

Date Functions

Time Functions

String and Operational Functions

ROW_COUNT

GROUPING Functions

Triggers, cursors, memory limitations

Why to use Triggers

DML Triggers and Performance impact

INSERTED and DELETED memory tables

Data Audit operations & Sampling

Database Triggers and Server Triggers

Bulk Operations with Triggers

Cursors and Memory Limitations

Cursor declaration and Life cycle

STATIC

DYNAMIC

SCROLL Cursors

FORWARD_ONLY and LOCAL Cursors

KEYSET Cursors with Complex SPs

Transactions Management

ACID Properties and Scope

EXPLICIT Transaction types

IMPLICIT Transactions and options

AUTOCOMMIT Transaction and usage

SAVEPOINT and Query Blocking

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This module offers knowledge to introduce you to the basic principles based on statistical methods and procedures followed in data analysis. This course will help you to understand the work process involved with summarizing the data, data storage, visualizing the data results, and a hands-on approach with statistical analysis with python.

Introduction to Data Science

What is Analytics & Data Science

Common Terms in Data Science

What is data

Classication of data

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 Data Science tools & their popularity.

Data Science Methodology & problem-solving framework.

List of steps in Data Science projects

Identify the most appropriate solution design for the given problem statement

Project plan for Data Science project & key milestones based on effort estimates

Build Resource plan for Data Science project

Why Python for data science

Accessing/Importing and Exporting Data

Importing Data from various sources (Csv, txt, excel, access etc)

Database Input (Connecting to database)

Viewing Data objects - sub setting, methods

Exporting Data to various formats

Important python modules Pandas

Data Manipulation Cleansing - Munging Using Python Modules

Cleansing Data with Python

Filling missing values using lambda function and concept of Skewness.

Data Manipulation steps (Sorting, ltering, duplicates, merging, append ing, sub setting, derived variables, sampling, Data type conversions, renaming, formatting.

Normalizing data

Feature Engineering in Data Science

Feature Engineering

Feature Selection

Feature scaling using Standard Scaler/Min-Max scaler/Robust Scaler.

Label encoding/one hot encoding

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/ densi ty etc.)

Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas etc.)

Introduction to Statistics

Descriptive Statistics

Sample vs Population Statistics

Random variables

Probability distribution functions

Expected value

Normal distribution

Gaussian distribution

Z-score

Spread and Dispersion

Correlation and Co-variance

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

EDA (Exploratory Data Analysis)

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

Imbalanced Data Techniques

Data Pre-Processing & Data Mining

Data Preparation

Feature Engineering

Feature Scaling

Datasets

Dimensionality Reduction

Anomaly Detection

Parameter Estimation

Data and Knowledge

Selected Applications in Data Mining

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Machine learning courses help to understand the complete concepts behind the processing of Artificial intelligence and Computer science. With the Machine learning course, you will cover topics based on supervised and unsupervised learning along with the development of software and algorithms to extract predictions based on data.

Introduction to Machine Learning

Machine Learning

Machine Learning Algorithms

Algorithmic models of Learning

Applications of Machine Learning

Large Scale Machine Learning

Computational Learning theory

Techniques of Machine Learning

Supervised Learning

Unsupervised Learning

Semi-supervised and Reinforcement Learning

Bias and variance Trade-off

Representation Learning

Regression

Regression and its Types

Logistic Regression

Linear Regression

Polynomial Regression

Classication

Meaning and Types of Classication

Probability and Bayes Theorem

Support Vector Machines

Naive Bayes

Decision Tree Classier

Random Forest Classier

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Deep learning is the most effective skill in AI. The course is intended to provide a complete foundation over the deep learning algorithms that help you to understand the process to build neural networks. The course of deep learning will help you to successfully handle the Machine learning projects needed by the organization today.

Introduction to Deep Learning

What are the Limitations of Machine Learning

What is Deep Learning

Advantage of Deep Learning over Machine learning

Reasons to go for Deep Learning

Real-Life use cases of Deep Learning

Deep Learning Networks

What is Deep Learning Networks

Why Deep Learning Networks

How Deep Learning Works

Feature Extraction

Working of Deep Network

Training using Backpropagation

Variants of Gradient Descent

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The Power BI course assists the user to understand the way to install Power BI desktop also by understanding and developing the workshop and insights using the data. It offers tools and techniques that are used to visualize and analyze data. The course will help you to learn and grab insights on everything an organization need; to manage the data with Power BI.

Introduction to Power BI

Overview of BI concepts

Why we need BI

Introduction to SSBI

SSBI Tools

Why Power BI

What is Power BI

Building Blocks of Power BI

Getting started with Power BI Desktop

Get Power BI Tools

Introduction to Tools and Terminology

Dashboard in Minutes

Interacting with your Dashboards

Sharing Dashboards and Reports

Power BI Desktop

Power BI Desktop

Extracting data from various sources

Workspaces in Power BI

Power BI Data Transformation

Data Transformation

Query Editor

Connecting Power BI Desktop to our Data Sources

Editing Rows

Understanding Append Queries

Editing Columns

Replacing Values

Formatting Data

Pivoting and Unpivoting Columns

Splitting Columns

Creating a New Group for our Queries

Introducing the Star Schema

Duplicating and Referencing Queries

Creating the Dimension Tables

Entering Data Manually

Merging Queries

Finishing the Dimension Table

Introducing the another DimensionTable

Creating an Index Column

Duplicating Columns and Extracting Information

Creating Conditional Columns

Creating the FACT Table

Performing Basic Mathematical Operations

Improving Performance and Loading Data into the Data Model

Modelling with Power BI

Introduction to Modelling

Modelling Data

Manage Data Relationship

Optimize Data Models

Cardinality and Cross Filtering

Default Summarization & Sort by

Creating Calculated Columns

Creating Measures & Quick Measures

Data Analysis Expressions (DAX)

What is DAX

Data Types in DAX

Calculation Types

Syntax, Functions, Context Options

DAX Functions

Date and Time
Time Intelligence
Information
Logical
Mathematical
Statistical
Text and Aggregate

Measures in DAX

Measures and Calculated Columns

ROW Context and Filter Context in DAX

Operators in DAX - Real-time Usage

Quick Measures in DAX - Auto validations

In-Memory Processing DAX Performance

Power BI Desktop Visualisations

How to use Visual in Power BI

What Are Custom Visuals

Creating Visualisations and Colour Formatting

Setting Sort Order

Scatter & Bubble Charts & Play Axis

Tooltips and Slicers, Timeline Slicers & Sync Slicers

Cross Filtering and Highlighting

Visual, Page and Report Level Filters

Drill Down/Up

Hierarchies and Reference/Constant Lines

Tables, Matrices & Conditional Formatting

KPI's, Cards & Gauges

Map Visualizations

Custom Visuals

Managing and Arranging

Drill through and Custom Report Themes

Grouping and Binning and Selection Pane, Bookmarks & Buttons

Data Binding and Power BI Report Server

Introduction to Power BI Dashboard and Data Insights

Why Dashboard and Dashboard vs Reports

Creating Dashboards

Conguring a Dashboard Dashboard Tiles, Pinning Tiles

Power BI Q&A

Quick Insights in Power BI

Direct Connectivity

Custom Data Gateways

Exploring live connections to data with Power BI

Connecting directly to SQL Server

Connectivity with CSV & Text Files

Excel with Power BI Connect Excel to Power BI, Power BI Publisher for Excel

Content packs

Update content packs

Publishing and Sharing

Introduction and Sharing Options Overview

Publish from Power BI Desktop and Publish to Web

Share Dashboard with Power BI Service

Workspaces (Power BI Pro) and Content Packs (Power BI Pro)

Print or Save as PDF and Row Level Security (Power BI Pro)

Export Data from a Visualization

Export to PowerPoint and Sharing Options Summary

Refreshing Datasets

Understanding Data Refresh

Personal Gateway (Power BI Pro and 64-bit Windows)

Replacing a Dataset and Troubleshooting Refreshing

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Tableau is an end-to-end data analytics platform that allows you to prep, analyze, collaborate, and share your data insights. Tableau excels in self-service visual analysis, allowing people to ask new questions of governed big data and easily share those insights across the organization.

Introduction to Data Preparation using Tableau Prep

Data Visualization

Business Intelligence tools

Introduction to Tableau

Tableau Architecture

Tableau Server Architecture

VizQL Fundamentals

Introduction to Tableau Prep

Tableau Prep Builder User Interface

Data Preparation techniques using Tableau Prep Builder tool

Data Connection with Tableau Desktop

Features of Tableau Desktop

Connect to data from File and Database

Types of Connections

Joins and Unions

Data Blending

Tableau Desktop User Interface

Basic Visual Analytics

Visual Analytics

Basic Charts Bar Chart, Line Chart, and Pie Chart

Hierarchies

Data Granularity

Highlighting

Sorting

Filtering

Grouping

Sets

Calculations in Tableau

Types of Calculations

Built-in Functions (Number, String, Date, Logical and Aggregate)

Operators and Syntax Conventions

Table Calculations

Level of Detail (LOD) Calculations

Using R within Tableau for Calculations

Advanced Visual Analytics

Parameters

Tool tips

Trend lines

Reference lines

Forecasting

Clustering

Level of Detail (LOD) Expressions in Tableau

Count Customer by Order

Profit per Business Day

Comparative Sales

Profit Vs Target

Finding the second order date

Cohort Analysis

Geographic Visualizations in Tableau

Introduction to Geographic Visualizations

Manually assigning Geographical Locations

Types of Maps

Spatial Files

Custom Geocoding

Polygon Maps

Web Map Services

Background Images

Advanced charts in Tableau

Box and Whisker’s Plot

Bullet Chart

Bar in Bar Chart

Gantt Chart

Waterfall Chart

Pareto Chart

Control Chart

Funnel Chart

Bump Chart

Step and Jump Lines

Word Cloud

Donut Chart

Dashboards and Stories

Introduction to Dashboards

The Dashboard Interface

Dashboard Objects

Building a Dashboard

Dashboard Layouts and Formatting

Interactive Dashboards with actions

Designing Dashboards for devices

Story Points

Get Industry Ready

Tableau Tips and Tricks

Choosing the right type of Chart

Format Style

Data Visualization best practices

Exploring Tableau Online

Publishing Workbooks to Tableau Online

Interacting with Content on Tableau Online

Data Management through Tableau Catalog

AI-Powered features in Tableau Online (Ask Data and Explain Data)

Understand Scheduling

Managing Permissions on Tableau Online

Data Security with Filters in Tableau Online

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AWS allows you to easily move data between the data lake and purpose-built data services. For example, AWS Glue is a serverless data integration service that makes it easy to prepare data for analytics, machine learning, and application development.

Introduction to Cloud Computing

In this module, you will learn what Cloud Computing is and what are the different models of Cloud Computing along with the key differentiators of different models. We will also introduce you to virtual world of AWS along with AWS key vocabulary, services and concepts.

A Short history
Client Server Computing Concepts
Challenges with Distributed Computing
Introduction to Cloud Computing
Why Cloud Computing
Benefits of Cloud Computing

Amazon EC2 and Amazon EBS

In this module, you will learn about the introduction to compute offering from AWS called EC2. We will cover different instance types and Amazon AMIs. A demo on launching an AWS EC2 instance, connect with an instance and host ing a website on AWS EC2 instance. We will also cover EBS storage Architecture (AWS persistent storage) and the concepts of AMI and snapshots.

Amazon EC2
EC2 Pricing
EC2 Type
Installation of Web server and manage like (Apache/ Nginx)
Demo of AMI Creation
Exercise
Hands on both Linux and Windows

Amazon Storage Services S3 (Simple Storage Services)

In this module, you will learn how AWS provides various kinds of scalable storage services. In this module, we will cover different storage services like S3, Glacier, Versioning, and learn how to host a static website on AWS.

Versioning
Static website
Policy
Permission
Cross region Replication
AWS-CLI
Life cycle
Classes of Storage
AWS CloudFront
Real scenario Practical
Hands-on all above

Cloud Watch & SNS

In this module, you will learn how to monitoring AWS resources and setting up alerts and notifications for AWS resources and AWS usage billing with AWS CloudWatch and SNS.

Amazon Cloud Watch
SNS - Simple Notification Services
Cloud Watch with Agent

Scaling and Load Distribution in AWS

In this module, you will learn about 'Scaling' and 'Load distribution techniques' in AWS. This module also includes a demo of Load distribution & Scaling your resources horizontally based on time or activity.

Amazon Auto Scaling
Auto scaling policy with real scenario based
Type of Load Balancer
Hands on with scenario based

AWS VPC

In this module, you will learn introduction to Amazon Virtual Private Cloud. We will cover how you can make public and private subnet with AWS VPC. A demo on creating VPC. We will also cover overview of AWS Route 53.

Amazon VPC with subnets
Gateways
Route Tables
Subnet
Cross region Peering

Identity and Access Management Techniques (IAM)

In this module, you will learn how to achieve distribution of access control with AWS using IAM.

Amazon IAM

add users to groups,
manage passwords,
log in with IAM-created users.

User

Group

Role

Policy

Amazon Relational Database Service (RDS)

In this module, you will learn how to manage relational database service of AWS called RDS.

Amazon RDS
Type of RDS
RDS Failover
RDS Subnet
RDS Migration
Dynamo DB (No SQL DB)
Redshift Cluster
SQL workbench
JDBC / ODBC

Multiple AWS Services and Managing the Resources' Lifecycle

In this module, you will get an overview of multiple AWS services. We will talk about how do you manage life cycle of AWS resources and follow the DevOps model in AWS. We will also talk about notification and email service of AWS along with Content Distribution Service in this module.

Cloud Trail,

AWS Architecture and Design

In this module, you will cover various architecture and design aspects of AWS. We will also cover the cost planning and optimization techniques along with AWS security best practices, High Availability (HA) and Disaster Recovery (DR) in AWS.

AWS High Availability Design
AWS Best Practices (Cost +Security)
AWS Calculator & Consolidated Billing

Migrating to Cloud & AWS

Router S3 DNS

Public DNS

Private DNS

Routing policy

Records

Work with third party DNS as well

Cloud Formation

Stack

Templet

Json / Ymal

Installation of Linux
Configuration
Manage
Installation of app on Linux (apache / Nginx etc)
AWS cli configuration on Linux
Complete hands-on on Linux.
Scenario based lab and practical
Each topic and services will be cover with lab and theory.

Elastic Beanstalk

EFS / NFS (hands-on practice)

Hands-on practice on various Topics

Linux

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Data Scientists know how to train Predictive Models. So, by enabling them to work together, Microsoft Azure Data Science ensures high-quality models at scale in production. With MLOps incorporated as a part of the Microsoft Azure Data Science platform, Data Scientists can create a discrete pipeline for each model

Describe Cloud Concepts

Identify the benefits and considerations of using cloud services Cloud Computing Basics.

Identify the benefits of cloud computing, such as High Availability,Scalability, Elasticity,
Agility, and Disaster Recovery
Identify the differences between Capital Expenditure (Cap Ex) and Operational.
Expenditure (Op Ex)
Describe the consumption-based model

Describe the differences between categories of cloud services

Describe the shared responsibility model
Describe Infrastructure-as-a-Service (IaaS),
Describe Platform-as-a-Service (PaaS)
Describe server less computing
Describe Software-as-a-Service (SaaS)
Identify a service type based on a use case

Describe the differences between types of cloud computing

Define cloud computing
Describe Public cloud
Describe Private cloud
Describe Hybrid cloud
Compare and contrast the three types of cloud computing Describe Core Azure Services

Manage Azure identities and governance (15-20%)

Manage Azure AD objects

create users and groups
manage user and group properties
manage device settings
perform bulk user updates
manage guest accounts
configure Azure AD Join
configure self-service password reset
NOTE Azure AD Connect; PIM

Manage role-based access control (RBAC)

create a custom role
provide access to Azure resources by assigning roles
subscriptions
resource groups
resources (VM, disk, etc.)
interpret access assignments
manage multiple directories

Manage subscriptions and governance

configure Azure policies
configure resource locks
apply tags
create and manage resource groups
move resources
remove RGs
manage subscriptions
configure Cost Management
configure management groups

Implement and Manage Storage (10-15%)

Manage storage accounts

configure network access to storage accounts
create and configure storage accounts
generate shared access signature
manage access keys
implement Azure storage replication
configure Azure AD Authentication for a storage account

Manage data in Azure Storage

export from Azure job
import into Azure job
install and use Azure Storage Explorer
copy data by using AZ Copy

Configure Azure files and Azure blob storage

create an Azure file share
create and configure Azure File Sync service
configure Azure blob storage
configure storage tiers for Azure blobs

Deploy and Manage Azure Compute Resources (25-30%)

Configure VMs for high availability and scalability

configure high availability
deploy and configure scale sets

Create and configure VMs

configure Azure Disk Encryption
move VMs from one resource group to another
manage VM sizes
add data discs
configure networking
redeploy VMs

Create and configure Web Apps

create and configure App Service
create and configure App Service Plans

Configure and Manage Virtual Networking (30-35%)

Implement and manage virtual networking

create and configure VNET peering
configure private and public IP addresses, network routes, network interface,
subnets, and virtual network

Configure name resolution

configure Azure DNS
configure custom DNS settings
configure a private or public DNS zone

Secure access to virtual networks

create security rules
associate an NSG to a subnet or network interface
evaluate effective security rules
deploy and configure Azure Firewall
deploy and configure Azure Bastion Service
NOT Implement Application Security Groups; DDoS

Configure load balancing

configure Application Gateway
configure an internal load balancer
configure load balancing rules
configure a public load balancer
troubleshoot load balancing
NOT Traffic Manager and Front Door and Private Link

Monitor and Back up Azure Resources (10-15%)

Implement backup and recovery

configure and review backup reports
perform backup and restore operations by using Azure Backup Service
create a Recovery Services Vault
use soft deletes to recover Azure VMs

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The training offers complete career transitioning projects based on the current needs of the organization. These projects are guided by experts and help you to add more value to your profile. You will learn to initiate data science projects based on a high-level perspective helping you to understand and articulate the innovative solutions for topical real-time data science projects.

Here is the project list you will going to work on

Managing credit card Risks

Bank Loan default classification

YouTube Viewers prediction

Super store Analytics (E-commerce)

Buying and selling cars prediction (like OLX process)

Advanced House price prediction

Analytics on HR decisions

Survival of the fittest

Twitter Analysis

Flight price prediction

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Mock Interviews

Prepare & Practice for real-life job interviews by joining the Mock Interviews drive at Croma Campus and learn to perform with confidence with our expert team.Not sure of Interview environments? Don’t worry, our team will familiarize you and help you in giving your best shot even under heavy pressures.Our Mock Interviews are conducted by trailblazing industry-experts having years of experience and they will surely help you to improve your chances of getting hired in real.How Croma Campus Placement Process Works?

Projects

Validate your skills and knowledge by working on industry-based projects that includes significant real-time use cases.Gain hands-on expertize in Top IT skills and become industry-ready after completing our project works and assessments.Our projects are perfectly aligned with the modules given in the curriculum and they are picked up based on latest industry standards.Add some meaningful project works in your resume, get noticed by top industries and start earning huge salary lumps right away.

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FAQ's

The main reason to choose the Online Data Science Course in India from the Croma Campus is so that you can grow your skills under the guidance of the corporate trainers that help you too gain the essential skills and knowledge to meet the demands of the organization with perfect solutions.

The Data Science Online Training in India from Croma Campus will help you to learn from the practical and theoretical formats and will also help you to learn from the real time-based projects that will upgrade your profile needed by the fortune organizations.

The Online Data Science Course in India with Certification can be done with Croma campus offering various ways to learn. You can choose any service from:

Instructor Training
Online Training
Corporate Training
Self-paced Training
1 on 1 Training

It takes around 5 to 6 months to learn the course from the Online Data Science Course in India. Also, it depends upon the learner. On an average this time is perfect to learn the course.

To start learning Data Science Online Certification Course in India you can contact to: Email: Info@cromacampus.com Contact no.: +91-9711526942 / +91-120-4155255

The duration of a Data Science course typically ranges from a few weeks for introductory programs to 2-3 years for in-depth degree courses.

Data Science course fees in India vary, starting from around INR 10,000 for basic courses to over INR 2,00,000 for comprehensive and specialized programs.

Topics in a Data Science online course usually include statistics, machine learning, Python programming, data visualization, and big data analysis.

A Data Scientist is a professional skilled in extracting insights and knowledge from data, using techniques in statistics, machine learning, and data analysis.

Yes, this Data Science course can be suitable for freshers, as many courses are designed to accommodate beginners with no prior experience.

A Data Science Management course focuses on combining data science skills with management principles, targeting professionals who oversee data-driven projects and teams.

To enroll in the best Data Science courses, research top-rated programs online or through academic institutions, and follow their application or registration process.

Yes, a Data Scientist job is often categorized under IT, as it involves working with technology and data systems, though it also encompasses statistical and analytical skills.

Eligibility for a Data Science online course varies, but generally, anyone with a keen interest in data and basic analytical skills can enroll. Some advanced courses might require prior knowledge in statistics or programming.

Croma Campus is the Best Data Science Training Institute in India from last two decade.

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