Why Choose Croma Campus for Data Analytics Online Training in India?

Croma campus is one of the best institute for training of IT professional jobs. It is one of the most prestigious and certified organizations that has been associated with the top most MNCs. Croma campus is situated in Noida which is really famous for its innovative and technical teaching methods. So, if you want to get linked with Data Analytics then do a Collab with Croma Campus.

How Data Analytics Online Course Help to Get a Good Job?

Data Analytics is a process of examining datasets to know about the information they contain. There are a lot of works and jobs under Data Analytics. The first thing you need to do is to always keep your profile updated on LinkedIn because they are directly associated with Data Analytics. So, if you get an Data Analytics certificate you can work as Data Analyst.

How to Do Certification in Data Analytics?

Data Analytics is nowadays becoming a very important certification that can lead you to get a good job. To get any Data Analyst job you need to get a certification in Data Analytics. There is a list of things after which you can get a certificate that contains in-depth training, many simultaneously exams, live demos, and other industrial projects that can make you a perfect Data Analyst. After all this training, you can get an Data Analytics certification.

What Kind of Projects Are Included in Data Analytics Online Training?

Croma Campus India program sizes a powerful training tool that can be applied in classrooms as well as in manufacturing. We offer a wide range of agendas for Live Project Data Analytics Training in India under the leadership of the best industrial experts. We are always awarded for the past 10 years as the Best Data Analytics Online Training in India.

How to Contact for Data Analytics Online Course?

The ways to connect Croma Campus Phone Number: - +91-120-4155255, +91-9711526942 Email: - info@cromacampus.com Address: - G-21, Sector-03, Noida (201301)

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Data Analytics is the process of reviewing data with a view to uncovering patterns, trends, and insightful actions leading to making decisions. Data Analytics Course Online aims at providing learners with theoretical foundations and technical skills in becoming professional data analysts. The best part is that you will be enrolling in a Data Analyst Placement Course.Means job ready after the course completion.
In 2025, and more so than ever, there is a need for data analytics because companies, governments, and even people are inundated with huge volumes of data every single day. Customer purchasing behavior or internet surfing activities are just a few examples; anything that is done generates useful data. Without analysis, the data is nothing but numbers and nothing else.
Data analysis supports the organizations in converting this unprocessed data into distinct insights, enabling better decisions, cost reductions, and new possibilities.
From scrubbing data to visualization, you can turn raw numbers into business-savvy insights. Data Analysis Online Course emphasizes the significance of handling massive amounts of structured and unstructured data and getting you at ease with the top tools and approaches adopted by industries today.

How to use data and determine useful patterns and trends.

The Data Analytics Online Course teaches you both fundamentals and hands-on skills.

Learn how to clean and sort data, and present it in easy-to-understand simple charts.

Develop experience with both small and large sets of data.

Get trained in the best tools and techniques used by companies today in Data Analysis Online Training.

The main goal of this Data Analytics Course Online is industry-readying the students with data fundamentals, tools, and better techniques training.
You must be able to manage big data, extract useful inferences, and deploy statistical or machine learning models to solve real-world problems at the end of Data Analytics Online Training.
Not only technical proficiency is this objective set to share but also your problem-solving ability and analytical mind, in addition to your ability to convey, such that you can articulate insights in a pragmatic, business-savvy way to stakeholders.
Goals of the Data Analyst Placement Course are:

Implementation of data collection, cleaning, and preparation concepts

Usage of visualization techniques for the delivery of data-driven narratives

Usage of statistical models for hypothesis testing in business

Application of machine learning algorithms in predictive modeling

Development of SQL queries for real-time data management

Development of dashboards using BI tools for real-time reporting

Tools contribute substantially to making data analysis possible. You are familiarized with tools used in industry and employers specifically look for these skills in Data Analysis Online Course. Not only familiarized, you will be trained hands-on on these tools with actual datasets.

Microsoft Excel (formulas, pivot tables, lookups, VBA): Excel is not merely for creating tables-it's a powerhouse function to calculate, sort, and consolidate data at lightning speed. With advanced formulas, pivot tables, and lookups, you can easily retrieve answers from massive sheets.

SQL (querying, joins, stored procedures, subqueries): SQL is almost a database query language. It assists you to ask questions to huge data storage systems, such as "display all Delhi sales last month." Joins, subqueries, and procedures enable you to get tricky information in seconds.

Python (NumPy, Pandas, Matplotlib, Scikit-learn): Python is a good beginning language employed in the majority of data analytics tasks. Libraries such as NumPy and Pandas are used in data cleaning and preparation, Matplotlib for plotting charts, and Scikit-learn for predictive model building.

R Programming (statistical testing, regression, data cleaning): R is primarily for data science and statistics. It allows one to test hypotheses, perform regression (discover the correlation between data), and sanitize dirty data. It's essentially a calculator, but specifically made for data scientists.

Power BI (DAX, data modeling, interactive dashboards: Power BI is a Microsoft tool that turns business data into clear information. Power BI generates interactive dashboards and visualization reports. Through DAX formulas and data modeling, you can link various sources of information and make sense of them all.

Tableau (stories, dashboards, filters, calculated fields: Tableau is a data visualization platform that makes data pretty, interactive dashboards, and charts. You can filter, make calculations, and even "tell data stories" to reveal insights in plain words.

Google Analytics (conversions, behavior analysis, web traffic): Google Analytics is employed to monitor what occurs on websites. It provides feedback on the number of visitors who arrive, where they arrive from, what they click, and whether or not they buy something (convert). This assists businesses with making their websites and advertisements better.

Hadoop & Spark (processing big data): Hadoop and Spark are similar to giant engines to run the enormous amount of data that do not fit in regular computers. They execute data in many machines simultaneously, which is why they are very significant for big companies possessing huge data.

Trained by experienced trainers, Data Analytics Course Online builds foundational and advanced technical abilities sought by companies. These are far beyond Excel fundamentals and include coding, database administration, and high-level analytics of our best Institute for Data analysis Online Course.

Data cleaning and preprocessing techniques: Prior to analysis, data is often dirty with errors, redundancy, or missing information. Cleaning and preprocessing is akin to washing and sorting raw ingredients prior to preparing the food-it prepares the data for hard-hitting insights.

SQL coding and database tuning: SQL assists in communication with the database, but coding it efficiently and database tuning accelerates data fetching. It is comparable to alphabetizing a library so that you can quickly grab the right book rather than digging through it.

Exploratory Data Analysis (EDA): EDA is the initial deep examination of data. It refers to verifying patterns, trends, and unusual values in order to learn what is happening. Consider it as "getting acquainted with your data" prior to making decisions.

Data visualization using BI tools: Visualization transforms dull numbers into beautiful charts and dashboards. BI software such as Tableau or Power BI enables one to easily communicate detailed data to anyone-even non-technical persons.

Statistical hypothesis testing: It's used to test whether your hypotheses regarding data are correct. For instance, "Do men shop online more than women" Hypothesis testing confirms or rejects such notions with real data.

Machine learning predictive modeling: All about getting computers to predict the future based on the past. For instance, predicting churn of customers or stock price. It's like providing the system with a "crystal ball" fueled by math.

Python/Pandas data wrangling: Data wrangling is cleaning, joining, or reshaping data into the correct format. Using Python's Pandas library, data analysts can organize dirty data and make it clean to analyze.

Big Data processing using Spark/Hadoop: When data is too large to process with regular tools, Hadoop and Spark come into play. They slice the massive data into manageable pieces, process the data within many machines, and gather results-similar to a team process to tackle big issues.

Fourier transformations for industry-special data: This is a mathematical method employed in telecommunication, engineering, or audio processing. It is useful in breaking down complicated signals (such as sound waves or sensor outputs) into simpler signals for analysis.

Product and marketing analytics A/B testing strategies: A/B testing tests two versions of an ad or a product (Version A and Version B) to determine which one is best. It is similar to testing two recipes among a group of individuals to determine which one they like best.

Data Analytics Training consists of well-defined modules that lead students from the basic to advanced level in a systematic way. You will begin with learning the principles of data, categories of data, and data collection.
Proceeding with Best Institute for Data Analyst Course, you will learn SQL, Python, and data visualization tools, then machine learning, predictive analytics, big data management, and industry applications. All modules are intended to transfer solid technical and hands-on expertise in the Data Analyst Online Course.

Introduction to Data Analytics: Business analytics concepts, business relevance

Analysts & SQL & Excel: SQL queries, joins, pivots, functions, automation

Probability & Statistics: Hypothesis testing, regression, sampling

Data Analytics & Python: Data wrangling, visualization, machine learning libraries

R Programming: Statistical modeling, simulation-based statistics

Business Intelligence Tools: Tableau & Power BI dashboards, reporting

Big Data Analytics: Hadoop, Spark introduction, handling humongous datasets

Advanced Analytics: A/B testing, predictive analytics, case studies

Capstone Project: Live project reflection of industry problem-solving

After successful completion of Data Analytics Course with Placement at end,students are provided with marked certificates, hence making them more employable.

Data Analyst Course India Completion Certificate

Microsoft Power BI Certification (optional with module)

Tableau Desktop Specialist Certification support

Google Analytics Individual Qualification (GAIQ) guide

Python for Data Science certification support

SQL Advanced Queries Certificate

Capstone Project Industry Certification

Once you finish Data Analytics Training, there are numerous career profiles based on what you would like to specialize in machine learning, visualization, or business intelligence.

Data Analyst

Business Intelligence Analyst

SQL Data Analyst

Marketing Data Analyst

Financial Data Analyst

Machine Learning Associate

Junior Data Scientist

MIS Reporting Analyst

Web Data Analyst

Risk Analyst

After the completion of the Data Anlytics Course with Placement,you can earn a good package.Pay of data analytics experts differs with regard to work, place of work, and work experience. Certified experts earn more than beginners who are not certified, though.

Entry Level (0 to 2 years): Job is Data Analyst or MIS Analyst with a mean pay of 4.5 to 6.5 LPA.

Mid Level (2 to 5 years): Job like BI Analyst or Web Analyst gets about 7 to 12 LPA.

Experienced (5+ years): Senior Data Analyst or Data Scientist may anticipate 15 to 22 LPA.

Advanced Expertise: Analytics Manager or AI/ML Specialist may provide 25+ LPA.

Career development in data analytics is among the fastest for other career paths in technology. Students normally advance to technical and decision-making ability-based strategic functions from data working. Data scientists, managers, or consultants develop after three years of experience as analysts. The harmonious mix of technical competencies and business skills enables people to change to leadership jobs.

Junior Data Analyst - Data Analyst - Senior Data Analyst - Business Intelligence Specialist - Data Scientist - Analytics Manager - Chief Data Officer

Accessibility is one of the best reasons why data analytics online courses are trending. IT, business managers or even non-technical managers do not want to quit their existing jobs but yet are eager to shift to analytics. Online learning allows students to study in any location and at any time and through live projects build technical portfolios.
They are employer-sponsored certificates as skills learned are based on the needs of the real world. Another reality is that the jobs out there are far more than the candidates. Therefore, e-learning is the golden ticket for career transition into a high-earning classes.

Every profession in data analytics has deliverables. Assignments typically consist of data preparation, reporting, analysis, and recommendations. Along the process, the following titles can be accepted by professionals after Data Analytics Certification.

Capturing ordered and unordered collections of data from various sources

Data cleaning, transformation, and validation for accuracy

Composing SQL queries to analyze data and manage databases

Creating interactive dashboards and reports using BI tools

Applying statistical models to identify patterns and make predictions

Facilitating business decisions through data analysis

Reporting back findings with technical stakeholders and non-technical managers

Interfacing with big data software for mass-scale analysis

A/B testing of sales/marketing metrics

After doing Data Analyst Online Course, you will have a lot of job opportunities in your life.Data analytics professional job roles are not confined to the IT sector. Every industry where data is being created needs individuals who can analyze and act upon it. After completing Data Analytics Online Training following are the professional job roles in other industries.

Information Technology & Software Services

Finance & Banking

Healthcare and Life Sciences

E-commerce and Retail

Telecommunications

Manufacturing & Supply Chain

Media and Entertainment

Consulting and Market Research Companies

Logistics and Transportation

Energy and Utilities

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Why should you learn Data Analytics Course?

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Helping out businesses to adapt to the new trends.

Data scientists have a tremendous impact on businesses.

It aims to capture out with the development technology.

Opens out the door for various opportunities in corporate.

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CURRICULUM & PROJECTS

Data Analytics Training Program

Introduction

What is Data Analytic

Common Terms in Data Analytics

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 Analytics tools & their popularity.

Data Analytics Methodology & problem-solving framework.

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Understanding Concepts of Excel

Creation of Excel Sheet Data

Range Name, Format Painter

Conditional Formatting, Wrap Text, Merge & Centre

Sort, Filter, Advance Filter

Different type of Chart Creations

Auditing, (Trace Precedents, Trace Dependents)Print Area

Data Validations, Consolidate, Subtotal

What if Analysis (Data Table, Goal Seek, Scenario)

Solver, Freeze Panes

Various Simple Functions in Excel(Sum, Average, Max, Min)

Real Life Assignment work

Ms Excel Advance

Advance Data Sorting

Multi-level sorting

Restoring data to original order after performing sorting

Sort by icons

Sort by colours

Lookup Functions

Subtotal, Multi-Level Subtotal

Grouping Features

Consolidation With Several Worksheets

Filter

Printing of Raw & Column Heading on Each Page

Workbook Protection and Worksheet Protection

Specified Range Protection in Worksheet

Excel Data Analysis

Data Table

Pivot Table

Generating MIS Report In Excel

Text Functions

Lookup & Reference Function

Logical Functions & Date and Time Functions

Database Functions

Statistical Functions

Financial Functions

Functions for Calculation Depreciation

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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

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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

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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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

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

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

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 folders details using OS

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Data Analysis and Visualization using Pandas.

Statistics

Pandas

Data Analysis and Visualization using NumPy and MatPlotLib

NumPy

MatPlotLib

Introduction to Data Visualization with Seaborn

Introduction to Seaborn

Visualizing Two Quantitative Variables

Visualizing a Categorical and a Quantitative Variable

Customizing Seaborn Plots

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Capstone Project

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