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Master data visualization, SQL, Python, and machine learning with our Data Analytics Course in Dehradun for career success.
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4.5/5Course Duration
80 Hrs.Live Project
5 ProjectCertification Pass
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Live Online /Self-Paced/ClassroomWatch Live Classes
Speciality
200+
Professionals Trained
3+
Batches every month
20+
Countries & Counting
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Corporate Served- From learning basic Excel operations to advanced Python programming, SQL queries, data visualization tools like Tableau and Power BI, and even learning basics of machine learning, Data Analytics Course In Dehradun covers everything. This is different to short crash courses that this course promises will educate you on how data flows in a business, how to clean and prepare it, and how to take raw data and turn it into insights that can inform managers and businesses when they need to make decisions. Dehradun is developing into an education hub very quickly, and learning data analytics there means availing the advantages of modern learning, practical training, and placement support.
Data Analytics Course In Dehradun
- The main objective of this course is to help the students have a clear idea of how data is collected, processed, analyzed, and used to guide strategies in organizations. A Data Analytics Course Online makes you study the concepts and gain hands-on experience by handling live projects. The idea is to prepare you for the industry so that you can apply data analytics in real-life situations with ease, if you are a fresher or an experienced specialist.
- The prime objectives are:
To teach you Python and R programming for analytics tasks.
To gain strong skills in SQL for data management.
To be familiar with statistics and probability for sound decision-making.
To become proficient with tools like Tableau, Power BI, and Excel.
To explore machine learning basics like regression, clustering, and classification.
To utilize capstone projects that tie analytics to business situations.
- Every course is different, but this Dehradun data analytics course is special because it combines the extent of technical coverage with a focus on real-world projects. Data Analytics Classes In Dehradun features have been developed based on a research study of the requirements of Indian and global industries. The intent is to provide students with tools that can be applied in real-world contexts instead of book-based information.
- The most important features are:
100% hands-on training on technology like Python, SQL, Excel, Tableau, and Power BI.
Trainers with over 10 years of industry experience and teaching.
Data Analytics Coaching In Dehradun curriculum aligned with top certifications like Google Data Analytics and Microsoft Data Analyst.
Hands-on assignments after every module for reinforcing learning.
Resume preparation tips, interview prep, and placement.
Weekday and weekend classes with flexible timings to suit students and working professionals.
Lifetime access to course materials and video sessions.
- One of the most popular questions among students is whether or not they can join this course. The positive aspect of the Data Analytics Coaching in Dehradun is that it doesn't require very advanced programming skills beforehand. Anyone possessing basic knowledge of computers and logical reasoning can enroll. Still, it makes the learning process quicker if one has a technical background.
- Eligibility requirements are:
Computer Science, IT, Statistics, Mathematics, and Economics graduates.
Finance, HR, IT, or marketing professionals who want to shift their career towards analytics.
Beginners with some basic Excel skills but strong logical reasoning skills.
Engineers, management graduates, or even non-technical graduates who want to start an analytics career.
- The size of data analytics is huge, and for this reason, this course remains in demand. Every company, irrespective of the industry, wants to take data-driven decisions rather than assumption-driven decisions. This creates a constant need for skilled analysts. By pursuing Data Analytics Training In Dehradun, students not only open up opportunities for local companies but also global opportunities.
- Data Analytics Scope points:
Steep rise in demand for analytics experts in India.
Data Analytics is used in IT, finance, healthcare, e-commerce, telecom, and government projects.
Professionals with data analytics training can move to higher-paying roles at a fast rate.
Coverage includes career roles like Data Analyst, Business Analyst, BI Developer, and junior Data Scientist.
Knowledge gained through this course can be implemented in various industries.
- The highlight of this course is the well-structured modules. The course has been divided into different phases so that students move step by step from basic to advanced concepts. Each module includes a mix of theory, practical classes, and real datasets.
- Modules covered are:
Introduction to Data Analytics: Understanding what data analytics is and why it is used.
Excel for Data Analytics: Master pivot tables, advanced formulas, and data cleaning.
SQL: Database querying, joins, subqueries, and advanced database management.
Python Programming: Beginner to advanced concepts using Pandas, NumPy, and Matplotlib.
Data Visualization: Creating dashboards in Power BI and Tableau.
Statistics for Data Analytics: Probability, regression, hypothesis testing, and distributions.
Machine Learning Fundamentals: Linear regression, classification models, and clustering techniques.
Capstone Project: Real-world problem where students apply everything they've learned during the course.
- Certifications help authenticate your knowledge, and Data Analytics Training In Dehradun prepares you for some of the most popular industry-recognized certifications. A certification adds a touch to your resume and distinguishes you from professionals and freshers.
- Once you complete Data Analyst Classes In Dehradun, the freshers will be able to get decent salary packages because companies realize how valuable data analytics is. Salaries also increase depending on the industry and the certifications you hold.
- Salaries anticipated are:
Average salary of freshers: 4 LPA to 6 LPA.
Certified freshers with project experience: 6 LPA to 8 LPA.
If we consider metro cities such as Noida, Bangalore, and Pune, salaries increase to 7 LPA to 10 LPA.
Salary grows very fast with 2 to 3 years of experience, and it typically doubles.
- Data Analysis Course In Dehradun is not merely searching for your first job, but also long-term growth. A beginner can start off as a Data Analyst and move to a Data Scientist role in a short period of a couple of years if he keeps learning. The course lays a strong foundation for different career advancements.
- Career growth path:
Data Analyst Senior Data Analyst Data Scientist.
Business Analyst Analytics Manager Data Architect.
BI Analyst BI Developer Data Engineering Specialist.
Experts can also become consultants, freelancers, or trainers.
- Data Analyst Course In Dehradun is becoming very popular with students in Dehradun because the demand for analytics is growing, and here in Dehradun, the fee is much lower compared to metro cities. The city is a suitable place to study and establish a career.
- The popularity reasons are:
Low costs compared to other major metro cities.
Real-life case studies training of international class.
Growing need for data professionals in nearby NCR and metro cities.
Getting a chance to work on real-time datasets during the course.
Placement assistance post-course.
- Through this course, you become qualified for different job titles. Every title has certain responsibilities. Data Analysis Training In Dehradun qualifies you for these titles by imparting the required combination of technical and business skills.
- Roles and Responsibilities are:
Data Analyst: Cleaning, preparing, and analyzing data for insights.
Business Analyst: Interpreting business requirements into data solutions.
BI Analyst: Creating reports, dashboards, and visualizations for management.
Junior Data Scientist: Using machine learning methodologies to predict results.
SQL Developer: Developing intricate queries and database administration.
- Data Analytics skills are needed across all industries. I mean that upon completion of Data Analysis Training Institute in Dehradun, you're not restricted to IT alone. You could move to finance, healthcare, marketing, or government sectors too.
- Choosing the right institute to learn your course is as important as the Data Analytics Training Institute in Dehradun that you choose. We are committed to providing you with hands-on, project-oriented training to prepare you for the industry. We have carefully handpicked trainers and experienced professionals who have worked on big-bang analytics projects.
- Why choose us:
IT and Software Development.
Banking and Financial Services.
E-commerce and Retail.
Healthcare and Pharmaceuticals.
Telecom and Media.
Supply Chain and Logistics
10+ years experienced trainers with an industry background.
Live project work while studying.
Placement assistance with MNC tie-ups.
Global certification guidance.
One-on-one mentoring and regular doubt sessions.
Reasonable course charges in comparison to metro cities.
- You May Also Read:
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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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