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Learn how to extract data for analysis. Join now and learn under an expert data analyst.
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- Data has proven to be one of the key drivers of businesses to make data-driven decisions with the progressing time. A data analyst is an individual who collects, organizes, and examines data to find trends or patterns so as to fix a problem or streamline a process. If you want to design a successful career as a data professional, the Data Analytics Course in Delhi will equip you with the necessary skills. Data Analysis Course in Delhi is all about learning the process of collecting, analyzing, and graphing data with standard tools and procedures. With more demand for data analysts emerging, this training will render you job-ready for careers where you are transforming raw data into precious insights.
Data Analytics Training in Delhi
- The primary task of a data analyst is to work on data in order to allow organizations to get to know their markets, operations, or customers better. They refine and arrange data in order to get it in the correct and precise form, process it through statistical processes, and represent findings as charts, graphs, or dashboards.
Understand real-world applications of data analytics.
Interpret and communicate data insights properly.
Solve business issues using analytics.
Use data-driven decision-making.
Learn to organize, clean, and analyze data professionally.
- Data Analytics Course in Delhi is done to get you to work on real data projects by training you in every facet of analytics that you must learn. It starts to acquaint you with the basics of statistics and data science. Next, it educates you on practical tools like Excel and SQL in data handling and querying data. You will code in Python or R to visualize and report results using visualization tools. The goal is to have you with the ability to work with difficult datasets and provide output that informs business choices.
- Top Skills You Master in This Course
- You will gain analytical and technical skills that employers require. You will be taught to prepare and clean data, work with databases, write SQL code, and conduct data set analysis using Python or R. You will learn how to make good visualizations using Tableau or Power BI. You also learn statistical analysis methods, machine learning basics, and how to use the above for predicting trends. All these combined will make you a strong data analyst.
Master Excel data manipulation.
Know SQL for maximum data extraction.
Code in Python or R to run complex analysis.
Create data visualization that beautifully communicates findings.
Be introduced to machine learning basics for predictive analytics.
Practice using real company data sets to have a hands-on learning experience.
Get ready to work in data analytics jobs.
Data preprocessing and data cleaning methods.
SQL queries to extract effective data.
Statistical training to analyze datasets.
Automation using programming and higher-level analysis.
Interactive dashboard and report building.
Forecasting using basic machine learning models.
Communication and collaboration skills for a business setting.
- Data Analyst Training in Delhi covers all the tools it takes to be a successful data analyst. You will start with Microsoft Excel for simple data calculations, then SQL for querying the database. Python or R will be taught for advanced analysis, and libraries such as Pandas and NumPy. For visualization purposes, you will use Tableau and Power BI to make dashboards. Big data and cloud platform brief will also be discussed to prepare for big size data projects.
Excel for data input, formula, and pivot tables.
SQL for database data manipulation.
Python/R for programming and data analysis.
Tableau and Power BI for visualization.
Brief on big data concepts.
Overview of cloud platforms for processing and storing data.
Applications of libraries that are unique to data science.
- This Data Analysis Course in Delhi is perfect for all those enthusiastic about analytics and data. It is perfect for data freshers who are looking to enter the data field, working professionals wanting to upskill, or career shift towards analytics. No higher math or coding is needed as the course starts from the beginning and has structured learning. Even from non-professional backgrounds, people can be proficient in data analysis if they are regular and under the guidance of instructors.
Fresh graduates wishing to pursue data careers.
Career professionals wishing to upskill.
Career transitioners transitioning into data analysis.
Business staff wishing to make sense of data.
Commerce, science, or arts students.
Individuals who have a passion for data and technology.
Individuals who desire to gain programming and statistics basics.
- Learning data analytics enables vast opportunities. As businesses collect massive amounts of data, they need individuals who can properly study the data. Data Analytics Course Online provides you with applied skills to apply on numerous projects, ranging from market analysis and customer behavior analysis to operations optimization and financial forecasting.
Enhance career prospects across different disciplines.
Develop analytical capabilities useful in everyday business problems.
Master data-driven decision-making.
Enhance self-confidence in presenting challenging data graphically.
Prepare for certification and better job interviews.
Master industry software and technology.
Enhance career development and career progression.
- After completing the Data Analytics Training in Delhi, there are a number of roles you can perform that involve data analysis and reporting. Hiring managers at e-commerce, finance, healthcare, telecommunication, and startup firms look for data-intensive professionals.
Entry-level professional job roles: Reporting Analyst, Data Analyst.
Mid-level professional job roles: Business Intelligence Analyst, Data Scientist.
Risk analysis, sales forecasting, marketing analytics specialty fields.
Jobs in banking, health, retail, IT sectors.
Opportunity to work as analytics managers or team leaders.
Contract and freelance project assignments are also available.
International employment opportunities in data intensive business.
- The Data Analysis course in Delhi is divided into different learning modules with step by step progressive complexity. Early modules are more about mastering fundamentals of data, Excel, and SQL. After you've mastered these basics, then you move to programming with Python or R and mastering the art of interpreting data.
- Certifications and Preparation
- All Data Analyst Training in Delhi courses make you expert enough in highly demanded certifications like Microsoft Certified: Data Analyst Associate or Google Data Analytics Professional Certificate. These certifications demonstrate your skills to top employers globally. Data Analytics Training in Delhi helps you with mock tests, practice assignments, and exam tips for your success. Certification boosts your CV and can lead to better job opportunities and better salary.
- Freshers Salary Expectations
- Fresher's who complete Data Analyst Course in Delhi are expected to start their career with the average salary range of 3 to 6 lakhs annually. Salary is certificate, experience, and skill-based. With experience and next-generation skill sets, data analysts can earn 7 to 15 lakhs or even more. The steady increase in data jobs in Delhi provides good opportunities for graduates to boost their income as well as profession. Freelance and advisory professionals with data skills have alternative sources of income. Initial salary range: 3-6 lakhs/year.
Basics of Data and Statistics.
Microsoft Excel for general and intermediate purposes.
SQL querying and database administration.
Python or R programming basics.
Data visualization using Tableau/Power BI.
Introduction to machine learning and big data.
Final project with end-to-end data analysis.
High-level analysts earn 7 to 15+ lakhs.
Off-campus part-time job for additional income.
Salaries enhance with experience and certifications on projects.
High performer bonuses and promotions.
Demand in the industry continues to grow.
Placement facilities by training institutes.
- Delhi has some of the top data analytics training institutes that provide professional training, current curriculum, and career-focused teaching. Most of the Data Analyst Course in Delhi provide offline and online batches as per your choice and style of learning. You get to learn with real data sets and use commonly used analytics tools. Trainers with real industry knowledge.
Latest and comprehensive curriculum.
Convenient timings with online classes.
Hands-on experience with real data.
Welcome letter and interview assistance.
Welcome letter and placement assistance.
Delhi business and alumni community.
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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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