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Uncover the methods to extract data for analytical purposes. Join us now and study under a skilled data analyst.
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- In today's world, being able to analyse data is essential for making smart business decisions. Our data analysis course in Mumbai is designed to give you the skills and knowledge you need to succeed in this field.
- This course covers a wide range of topics, from basic data handling to advanced analysis techniques, ensuring you are prepared to handle real-world data challenges. The course content is balanced with both theory and hands-on practice. You'll learn how to collect, clean, and prepare data, as well as dive into advanced topics like statistical analysis, data visualization, and predictive modelling.
- By the end of the course, you'll be skilled in using popular tools like Excel, SQL, Python, and R to analyse data and create useful insights.
- Prerequisites for Data Analyst Course in Mumbai
- Who Should Enroll for Data Analytics Classes in Mumbai
Open to Everyone: No specific background needed.
Basic Understanding: Some knowledge of statistics, math, and programming is helpful.
Familiarity with Tools: Experience with Excel, SQL, Python, or R is a plus but not required.
Interest in Learning: A strong desire to learn about data and analytics is important.
Beginners: Anyone new to data analysis.
Career Changers: People looking to switch to a career in data analysis.
Current Data Professionals: Data analysts, data scientists, and business analysts wanting to improve their skills.
IT Professionals: IT experts looking to add data analysis to their skillset.
Business Managers: Managers who want to use data analytics for better decision-making.
Students and Graduates: Recent graduates or students wanting to gain practical data analysis skills.
Data Analytics Course in Mumbai
- Our data analytics training in Mumbai aims to provide the essential skills for effective data analysis. The goals include:
Comprehensive Knowledge: Understand the basics and advanced concepts of data analysis.
Hands-On Experience: Work on real-world projects to apply what you learn.
Tool Mastery: Get proficient with tools like Excel, SQL, Python, and R.
Statistical Analysis: Learn how to perform statistical analysis to find trends and patterns in data.
Data Visualization: Learn to create visual representations of data to communicate findings effectively.
Predictive Modeling: Understand how to use data to predict future trends.
Problem-Solving: Use data to identify and solve business problems.
Communication Skills: Improve your ability to present data findings clearly to stakeholders.
- Completing a data analyst course in Mumbai can boost your earning potential. Here's what you can expect based on experience:
Entry-Level Data Analysts: Around Rs 4,00,000 to Rs 6,00,000 annually.
Mid-Level Data Analysts: Rs 7,00,000 to Rs 12,00,000 annually with a few years of experience.
Senior Data Analysts: Rs 13,00,000 to Rs 20,00,000 annually for experienced professionals.
Global Opportunities: Higher salaries in global markets like the US or UK, often exceeding $70,000 to $100,000 per year.
- Completing a data analysis course with the best data analytics institute in Mumbai opens up many career opportunities, including:
Diverse Roles: Move into roles like data scientist, business analyst, or data engineer.
High Demand: Industries like IT, finance, healthcare, and retail need data analysts.
Professional Development: Further certifications and training can lead to higher positions.
Leadership Roles: Experienced data analysts can advance to management positions.
Entrepreneurial Opportunities: Some data analysts start their own consulting businesses.
- The popularity of data analytics courses in Mumbai is due to several reasons:
Industry Demand: Businesses need skilled data analysts to make informed decisions.
Career Opportunities: Many job openings in various industries like IT, finance, healthcare, and retail.
Technological Advancements: New tools and technologies make data analysis easier.
Global Recognition: Certifications from Mumbai are recognized worldwide.
Professional Community: Access to a network of professionals and experts in the field.
- Our Data analytics classes in Mumbai helps you to become an established data analyst and Data analysts play a key role in organizations by:
Collecting Data: Gathering data from various sources.
Cleaning Data: Ensuring data is accurate and consistent.
Analysing Data: Using statistical methods to find trends and patterns.
Visualizing Data: Creating charts and graphs to present findings.
Reporting: Preparing reports and presentations for stakeholders.
Using Tools: Working with tools like Excel, SQL, Python, and R.
Collaborating: Working with different teams to meet their data needs.
Solving Problems: Using data to address business issues.
- Data analysts are in demand in various industries, including:
IT: Analyzing large volumes of data to improve decision-making.
Finance and Banking: Optimizing processes, managing risks, and ensuring compliance.
Healthcare: Improving patient care and managing healthcare data.
Retail and E-commerce: Enhancing customer experience and optimizing inventory.
Consulting: Providing data-driven solutions to clients.
- After completing the data analysis course in Mumbai, you'll receive a certificate that proves your skills and knowledge. This certification can enhance your credibility and career prospects
- By enrolling in our data analysis training in Mumbai, you'll acquire the skills needed to excel in this dynamic field, opening up many career opportunities and advancing your professional journey.
In-Depth Knowledge: Gain a strong understanding of data analysis principles.
Practical Experience: Work on real-world projects.
Career Advancement: Essential for securing data analyst roles and advancing your career.
Attractive Salaries: Access to high-paying job opportunities.
Updated Skills: Stay current with industry trends and best practices.
- You May Also Read:
Why Should You Learn Data Analytics?
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CURRICULUM & PROJECTS
Data Analytics Training Program
- 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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- Installation and Working with Python
- Understanding Python variables
- Python basic Operators
- Understanding the Python blocks.
- Python Comments, Multiline Comments.
- Python Indentation
- Understating the concepts of Operators
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- Categorical Data
- Numerical Data
- Mean
- Median
- Mode
- Outliers
- Range
- Interquartile range
- Correlation
- Standard Deviation
- Variance
- Box plot
- Descriptive Statistics
- Sample vs Population Statistics
- Random variables
- Probability distribution functions
- Expected value
- Normal distribution
- Gaussian distribution
- Z-score
- Spread and Dispersion
- Correlation and Co-variance
- Data Preparation
- Feature Engineering
- Feature Scaling
- Datasets
- Dimensionality Reduction
- Anomaly Detection
- Parameter Estimation
- Data and Knowledge
- Selected Applications in Data Mining
- Need for structured exploratory data
- EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
- Identify missing data
- Identify outliers data
- Imbalanced Data Techniques
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- Statistics
- Pandas
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- 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 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 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
- 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
- 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
- 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
- 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
- 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
- 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
- Cursor declaration and Life cycle
- STATIC
- DYNAMIC
- SCROLL Cursors
- FORWARD_ONLY and LOCAL Cursors
- KEYSET Cursors with Complex SPs
- ACID Properties and Scope
- EXPLICIT Transaction types
- IMPLICIT Transactions and options
- AUTOCOMMIT Transaction and usage
- AI Tools:
- Copilot in Azure Data Studio or GitHub for SQL suggestions
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- 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
- 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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- 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
- Extracting data from various sources
- Workspaces in Power BI
- 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
- 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
- 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
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- Data Visualization
- Business Intelligence tools
- Introduction to Tableau
- Tableau Architecture
- Tableau Server Architecture
- VizQL Fundamentals
- Introduction to Tableau Prep
- Tableau Prep Builder User Interface
- Data Preparation techniques using Tableau Prep Builder tool
- Features of Tableau Desktop
- Connect to data from File and Database
- Types of Connections
- Joins and Unions
- Data Blending
- Tableau Desktop User Interface
- Visual Analytics
- Basic Charts Bar Chart, Line Chart, and Pie Chart
- Hierarchies
- Data Granularity
- Highlighting
- Sorting
- Filtering
- Grouping
- Sets
- Types of Calculations
- Built-in Functions (Number, String, Date, Logical and Aggregate)
- Operators and Syntax Conventions
- Table Calculations
- Level of Detail (LOD) Calculations
- Using R within Tableau for Calculations
- Parameters
- Tool tips
- Trend lines
- Reference lines
- Forecasting
- Clustering
- Count Customer by Order
- Profit per Business Day
- Comparative Sales
- Profit Vs Target
- Finding the second order date
- Cohort Analysis
- Introduction to Geographic Visualizations
- Manually assigning Geographical Locations
- Types of Maps
- Spatial Files
- Custom Geocoding
- Polygon Maps
- Web Map Services
- Background Images
- Box and Whiskers Plot
- Bullet Chart
- Bar in Bar Chart
- Gantt Chart
- Waterfall Chart
- Pareto Chart
- Control Chart
- Funnel Chart
- Bump Chart
- Step and Jump Lines
- Word Cloud
- Donut Chart
- Introduction to Dashboards
- The Dashboard Interface
- Dashboard Objects
- Building a Dashboard
- Dashboard Layouts and Formatting
- Interactive Dashboards with actions
- Designing Dashboards for devices
- Story Points
- Tableau Tips and Tricks
- Choosing the right type of Chart
- Format Style
- Data Visualization best practices
- Publishing Workbooks to Tableau Online
- Interacting with Content on Tableau Online
- Data Management through Tableau Catalog
- AI-Powered features in Tableau Online (Ask Data and Explain Data)
- Understand Scheduling
- Managing Permissions on Tableau Online
- Data Security with Filters in Tableau Online
- Ask Data: Natural Language Data Exploration
- Explain Data: Automatic statistical insights
- Tableau GPT (Einstein Copilot - Salesforce)
- AI forecasting in visualizations
- Integration with Python (TabPy) and R
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