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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.
Data Analytics Online Training
- 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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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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