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Digital marketing Mastery

  • Mode: Live Classes
  • |
  • Live Projects: 10+
  • |
  • Duration: 8 Months

Digital Marketing Certification Program

No prior knowledge needed | Updated Industry oriented syllabus

Become an ace of digital marketing with hands- on expertise and knowledge in the entire digital marketing landscape both organic and paid marketing.

Launch brands into success with the power of organic marketing and paid marketing and become the top 1% of marketers.

  • 200+

    Hours of Live Sessions

  • 12

    Hours daily doubt solving

  • Sat & Sun

    3PM to 5PM IST

  • live sessions from industry experts
  • Master organic marketing & Growth hacks | SEO, SEM and Social Media Marketing
  • Learn Paid marketing across 5 platforms | Meta, Google, LinkedIN, Quora & Twitter
  • Become a certified Digital Marketer
  • Guaranteed internship opportunities for top 5% students

Course benefits

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Course features

Live sessions

120+ hours of interactive live and recorded sessions from basics to advance

Domain specific live projects

Choose projects from Industry relevant domain

1 to 1 doubt solving

12 hours daily 1 to 1 live support with experts

Dashboard Access With Recordings

Get all your recorded conttent, Assignments and study material even post course completion based on your plan

Placement assistance

Get resume support & Mock interview preparation to showcase the skills you have earned

Career support

No need to hunt for jobs, focus on building you skills while we do the rest

Course benefits

Skills to Master

Ace these industry skills to become job ready

google ads

google adsense

google analytics

google search console

google tag manager

meta ads

graphic designing

social media growth

wordpress cms

blogging

google ads

google adsense

google analytics

google search console

google tag manager

meta ads

graphic designing

social media growth

wordpress cms

blogging

Explore your Roadmap to Success

Master Ms Excel and Adv.Excel | master libraries for data manipulation

Learn fundamentals of ETL (Extract, Transform & Load) process | Master SQL and no SQL

Learn Python Programming | Unleash the power with one of the most powerful and easiest programming language

Leverage Statistics | Analyze data like a pro, make accurate predictions and find meaningful insights from data

Master Data visualization | Create stunning and interactive dashboards with PoweBI & Tableau

Placement assistance | Get mock interviews and resume support

Job Ready Curriculum

  • Course induction
  • course overview and dashboard description
  • Introduction of data industry
  • lab introduction
  • support system introduction
  • community introduction
  • What is Data Analytics?
  • What is the importance of Data Analytics?
  • How Data Analytics is helping businesses?
  • Data Analytics Life Cycle
  • What is Database?
  • Why to use Databases?
  • What is RDBMS?
  • Operations in Databases
  • ER Diagrams
  • Concepts of Keys - Primary Key, Foreign Keys, Composite Keys, Candidate Keys
  • Joining Datasets in Databases - Inner, Left, Right, Full Outer
  • ACID Properties
  • Transactions and Transaction Control
  • Indexing
  • Why SQL?
  • Application of SQL
  • Characteristics of SQL
  • MySQL Installation Guide
  • Connection & Setup
  • DDL, DML, DCL in SQL
  • Data Types in SQL
  • Binary Data Types
  • Approximate Numeric Data Types
  • Exact Numeric Data Types
  • Character String Data Types
  • Date and Time Data Types
  • CREATE DATABASE
  • DROP DATABASE
  • CREATE table
  • CREATE table with PRIMARY KEY
  • CREATE table with FOREIGN KEY
  • DELETE table
  • TRUNCATE table
  • TEMP table
  • RENAME table
  • DROP table
  • COPY table
  • ALTER table
  • INSERT query
  • UPDATE query
  • DELETE query
  • SELECT statement
  • SELECT DISTINCT
  • SELECT COUNT
  • SELECT TOP
  • SELECT LAST
  • SQL WHERE clause
  • SQL ORDER BY clause
  • SQL DESC statement
  • SQL USE statement
  • SQL COMMIT statement
  • SQL ROLLBACK statement
  • Addition (+)
  • Multiplication (*)
  • Division (/)
  • Modulus (%)
  • EXISTS
  • IN, NOT IN
  • ANY, ALL
  • NULL, NOT NULL
  • LIKE
  • BETWEEN
  • Equal to (=)
  • Greater than (>)
  • Less than (<)
  • Not equal to (!=)
  • COALESCE Clause
  • IF Clause
  • SQL CASE-WHEN Clause
  • INNER JOIN
  • LEFT JOIN
  • RIGHT JOIN
  • FULL JOIN
  • SELF JOIN
  • CARTESIAN JOIN
  • Subquery in FROM clause
  • Subquery in SELECT clause
  • Subquery in WHERE clause
  • Correlated subqueries
  • Filter query results using query on different table
  • Conditional aggregation
  • SUM(), MIN(), MAX(), AVG(), COUNT()
  • SQL GROUP BY clause
  • SQL HAVING clause
  • Window Function Syntax
  • OVER Clause
  • PARTITION BY
  • ORDER BY
  • ROW_NUMBER()
  • RANK()
  • DENSE_RANK()
  • LEAD()
  • LAG()
  • FIRST_VALUE()
  • LAST_VALUE()
  • NTILE()
  • RANGE BETWEEN
  • ROWS BETWEEN
  • NON-Recursive CTE
  • Recursive CTE
  • View
  • Stored Procedures
  • Functions
  • Normalization - 1NF, 2NF, 3NF, etc.
  • What is Power BI? Overview and Use Cases
  • Components of Power BI (Desktop, Service, Mobile)
  • Power BI Architecture
  • Installing Power BI Desktop
  • Exploring the Power BI Interface
  • Overview of Data Sources Supported by Power BI
  • Connecting to Different Data Sources (Excel, SQL Server, Web, etc.)
  • Importing Data vs. Direct Query
  • Best Practices for Data Connectivity
  • Introduction to Power Query Editor
  • Data Cleaning and Shaping: Removing Duplicates, Filtering, Sorting, and Merging
  • Advanced Data Transformation: Unpivoting Columns, Grouping, and Aggregating Data
  • Creating Custom Columns and Measures
  • Understanding the M Language for Data Manipulation
  • Understanding the Importance of Data Modeling
  • Creating Relationships between Tables
  • Using Primary Keys and Foreign Keys
  • Normalizing Data
  • Introduction to Star and Snowflake Schemas
  • What is DAX (Data Analysis Expressions)?
  • Basic DAX Functions: SUM, AVERAGE, COUNT, DISTINCT, etc.
  • Calculated Columns vs. Measures
  • Understanding Row Context and Filter Context
  • Using DAX to Create Calculated Fields
  • Time Intelligence Functions: DATEADD, DATESYTD, SAMEPERIODLASTYEAR, etc.
  • Advanced DAX Functions: CALCULATE, ALL, FILTER, etc.
  • Working with Variables in DAX
  • Troubleshooting and Optimizing DAX Queries
  • Types of Visualizations in Power BI (Bar, Line, Pie, Map, etc.)
  • Customizing Visuals: Formatting, Colors, Labels, Tooltips
  • Using Filters, Slicers, and Drill-through
  • Creating Interactive Dashboards
  • Custom Visuals: What They Are and How to Use Them
  • Working with Bookmarks and Selections
  • Using Conditional Formatting and Data Bars
  • Advanced Mapping Techniques (ArcGIS, Filled Maps, Shape Maps)
  • Introduction to Power BI Report Themes
  • Publishing Reports to Power BI Service
  • Creating and Sharing Dashboards
  • Managing Data Refresh in Power BI Service
  • Understanding Workspaces, Apps, and Datasets
  • Setting Up Row-Level Security (RLS)
  • Introduction to Power BI Mobile: Features and Navigation
  • Designing Reports for Mobile View
  • Introduction to Power BI Embedded
  • Embedding Power BI Reports in Websites and Apps
  • Security and Licensing Considerations
  • Introduction to R and Python Integration in Power BI
  • Using R Scripts for Data Manipulation and Visualization
  • Advanced Analytics with Python in Power BI
  • Incorporating AI Visuals (Key Influencers, Decomposition Tree)
  • Predictive Analysis and What-If Scenarios
  • Optimizing Data Models for Performance
  • Reducing File Size and Improving Load Times
  • Best Practices for Report Design and Development
  • Monitoring and Auditing Power BI Usage
  • Troubleshooting Common Issues
  • Understanding Power BI Admin Roles
  • Managing Permissions and Security
  • Data Governance and Compliance in Power BI
  • Setting Up Data Gateways
  • Power BI Tenant Settings and Configuration
  • Project Overview: Business Case and Data Source
  • Planning and Designing the Dashboard
  • Implementing the Dashboard with Visualizations
  • Applying Advanced DAX and Data Modeling Techniques
  • Publishing and Sharing the Final Dashboard
  • Recap of Key Concepts and Techniques Covered
  • Real-World Use Cases and Success Stories
  • Open Q&A Session
  • Additional Resources and Next Steps
  • Course Feedback and Evaluation
  • What is Tableau? Overview and Use Cases
  • Tableau Product Suite (Desktop, Online, Server, Public, Prep)
  • Installing and Setting Up Tableau Desktop
  • Exploring the Tableau Interface (Menus, Shelves, and Cards)
  • Understanding Tableau Terminology (Dimensions, Measures, Discrete, Continuous)
  • Overview of Data Sources Supported by Tableau
  • Connecting to Different Data Sources (Excel, CSV, SQL Server, etc.)
  • Managing Data Extracts and Live Connections
  • Creating Data Extracts for Performance Optimization
  • Joining and Blending Data from Multiple Sources
  • Introduction to Data Preparation in Tableau
  • Using Tableau Prep for Data Cleaning and Shaping
  • Filtering, Sorting, and Grouping Data
  • Using Calculated Fields and Table Calculations
  • Understanding Aggregation, Granularity, and Level of Detail (LOD) Expressions
  • Creating Basic Charts: Bar, Line, Pie, Scatter Plot, Heat Map
  • Formatting Visualizations: Colors, Labels, Tooltips, and Annotations
  • Using Filters, Parameters, and Sets
  • Creating Interactive Dashboards
  • Introduction to Geographical Mapping and Maps in Tableau
  • Creating Advanced Chart Types: Gantt, Bullet, Waterfall, Histogram, etc.
  • Using Dual-Axis and Combined Charts
  • Working with Advanced Mapping Techniques
  • Using Advanced Table Calculations (Window Functions, Running Total, etc.)
  • Creating Custom Territories and Geocoding
  • Principles of Effective Dashboard Design
  • Using Layout Containers and Floating Objects
  • Optimizing Dashboards for Different Devices (Desktop, Mobile, Tablet)
  • Adding Interactivity: Filters, Actions, Parameters, and Highlighters
  • Using Dashboard Extensions
  • Introduction to Data Storytelling
  • Creating Stories in Tableau
  • Using Visual Elements to Enhance Stories
  • Case Studies and Best Practices in Data Storytelling
  • Introduction to Tableau Server and Tableau Online
  • Publishing Workbooks and Dashboards
  • Creating and Managing Data Sources on Tableau Server
  • Setting Permissions and Access Controls
  • Scheduling Refreshes and Subscriptions
  • Overview of Tableau Prep Builder
  • Connecting to and Preparing Data with Tableau Prep
  • Cleaning and Shaping Data (Filtering, Pivoting, Splitting, Merging)
  • Creating Data Flows and Outputting Clean Data
  • Automating Data Preparation with Tableau Prep
  • Using R and Python Integration in Tableau
  • Creating Advanced Statistical Visualizations (Regression, Forecasting)
  • Using Clustering and Trend Lines
  • Leveraging Tableau's Built-In AI Features (Explain Data, Ask Data)
  • Visualizing Predictive Models in Tableau
  • Optimizing Workbook Performance
  • Understanding Performance Recorder and Analyzing Slow Dashboards
  • Reducing Load Time with Extracts and Aggregations
  • Best Practices for Workbook and Dashboard Performance
  • Troubleshooting Common Performance Issues
  • Understanding Tableau Site Roles and User Management
  • Managing Content: Projects, Workbooks, Data Sources
  • Monitoring Tableau Server Usage and Performance
  • Ensuring Data Security and Compliance
  • Backup, Restore, and Upgrading Tableau Server
  • Project Overview and Requirements Gathering
  • Data Preparation and Transformation
  • Designing and Implementing the Dashboard
  • Applying Advanced Analytics and Visual Techniques
  • Publishing, Sharing, and Collaborating on the Dashboard
  • Analyzing Real-World Use Cases and Best Practices in Tableau
  • Exploring Industry-Specific Dashboards (Finance, Healthcare, Marketing, etc.)
  • Discussing Tableau Community and Resources for Continuous Learning
  • Future Trends in Data Visualization with Tableau
  • Recap of Key Concepts and Techniques Covered
  • Real-World Use Cases and Success Stories
  • Open Q&A Session
  • Additional Resources and Next Steps
  • Course Feedback and Evaluation
  • Python Installation
  • Python Basics
  • Input/Output
  • Data Types
  • Variables
  • Operators
  • List, Tuple, Dictionary, Set
  • List Comprehension
  • Dictionary Comprehension
  • For Loop; While Loop, Nested Loops
  • Control Flow
  • Functions
  • Lambda Functions
  • Map(), Filter(), Reduce()
  • Regex
  • Object Oriented Concepts
  • Exception Handling
  • Introduction
  • Different Types of Statistics
  • Population vs Sample
  • Mean, Median, and Mode
  • Variance, Standard Deviation
  • Sample Variance: Why n-1?
  • Standard Deviation
  • Variables
  • Random Variables
  • Percentiles & Quartiles
  • 5 Number Summary
  • Histograms
  • Gaussian - Normal Distribution
  • Standard Normal Distribution
  • Application of Z-Score
  • Permutation
  • Combination
  • Basics of Probability
  • Addition Rule in Probability
  • Multiplication Rule in Probability
  • Log-Normal Distribution
  • Central Limit Theorem
  • Statistics - Left Skewed and Right Skewed Distribution and Relation with Mean, Median, and Mode
  • Covariance
  • Pearson and Spearman Rank Correlation
  • What is P-Value?
  • What are Confidence Intervals?
  • How to Perform Hypothesis Testing - Confidence Interval, Z-Test Statistics, Derive Conclusion
  • Hypothesis Testing Part 2
  • Hypothesis Testing Part 3
  • Finalizing Statistics
  • Launching Excel
  • Microsoft Excel Startup Screen
  • Introduction to the Excel Interface
  • Customizing the Excel Quick Access Toolbar
  • More on the Excel Interface
  • Understanding the Structure of an Excel Workbook
  • Saving an Excel Document
  • Opening an Existing Excel Document
  • Common Excel Shortcut Keys
  • Entering Text to Create Spreadsheet Titles
  • Working with Numeric Data in Excel
  • Entering Date Values in Excel
  • Working with Cell References
  • Creating Basic Formulas in Excel
  • Relative Versus Absolute Cell References in Formulas
  • Understanding the Order of Operation
  • The Structure of an Excel Function
  • Working with the SUM() Function
  • Working with the MIN() and MAX() Functions
  • Working with the AVERAGE() Function
  • Working with the COUNT() Function
  • Adjacent Cells Error in Excel Calculations
  • Using the AutoSum Command
  • Excel's AutoSum Shortcut Key
  • Using the AutoFill Command to Copy Formulas
  • Moving and Copying Data in an Excel Worksheet
  • Inserting and Deleting Rows and Columns
  • Changing the Width and Height of Cells
  • Hiding and Unhiding Excel Rows and Columns
  • Renaming an Excel Worksheet
  • Deleting an Excel Worksheet
  • Moving and Copying an Excel Worksheet
  • Working with Font Formatting Commands
  • Changing the Background Color of a Cell
  • Adding Borders to Cells
  • Excel Cell Borders Continued
  • Formatting Data as Currency Values
  • Formatting Percentages
  • Using Excel's Format Painter
  • Creating Styles to Format Data
  • Merging and Centering Cells
  • Using Conditional Formatting
  • Editing Excel Conditional Formatting
  • Project Introduction
  • Dataset
  • Problem Statement
  • Solution Building
  • Conclusion
  • Project Introduction
  • Dataset
  • Problem Statement
  • Solution Building
  • Conclusion
  • Project Introduction
  • Dataset
  • Problem Statement
  • Solution Building
  • Conclusion
  • Project Introduction
  • Dataset
  • Problem Statement
  • Solution Building
  • Conclusion
  • Project Introduction
  • Dataset
  • Problem Statement
  • Solution Building
  • Conclusion

Invest In Your Success

Job Guarantee

₹1,25,000 /Excl GST

  • 100% job Guarantee or get 1,00,000 refunded instantly
  • Live sessions
  • 1 to 1 Live Doubt Support
  • Course completion certificate
  • Personalized Resume Support
  • Personalized Mock Interviews
  • Lifetime dashboard access
  • One complimentary upgrade to the next batch
Premium

Placement Assistance

₹25,000 /Excl GST

  • Live sessions
  • community doubt support
  • Course completion certificate
  • Resume Support
  • Mock Interviews
  • 1 year dashboard access
  • 1 to 1 Live Doubt Support
Gold

Job roles post course completion

Data Analyst
Business Analyst
Business intelligence analyst
Sales Analyst/ HR analyst/ Ops Analyst, Financial Analyst

Success Stories

⁠ Priya Sharma

Finally found a course that doesnot just teach theory! Was stuck in a boring data entry job, spending 50k on random courses. The SQL module is gold - learned stuff I actually use daily now at my new startup job.

⁠Arjun Mehta

Was super skeptical at first tbh (tried 2 other institutes before 😅). DSA felt like a mountain, especially coming from a tier-3 college. But Amit sir explanation of recursion actually made it click!

⁠Rajesh Krishnamurthy

Writing this after 6 months in my new role. Mechanical grad who couldnot write a single line of code. Was scared about DSA (those leetcode hards 😱). But the building blocks approach really worked. Started from arrays, now solving graph problems! Big thanks to Sneha for the extra doubt sessions.

⁠Karthik Iyer

Real talk: This is one of those "become DSA expert in 30 days" scams. It intense, requires 3-4 hours daily commitment.

⁠Vikram Singhania

My analytics team actually noticed the difference in my reports after just the first month! The R programming part was tough (lots of late nights), but the teaching assistants were patient. Special shoutout to Rohit bhaiya for debugging help. No more googling "how to analyze this data" 😂

⁠Neha Patel

Honest review after completing BA course: Pros - Actually teaches real analytics, not just basic Excel. The projects use actual company data. Thursday doubt sessions are super useful. Cons - Batch was too large (40+ students), some 1:1 mentor sessions got delayed. Still, landed a good role, so paisa vasool!

⁠Aryan Malhotra

Been coding for 2 years but DSA concepts were always confusing. The visualization tools helped a lot (especially for trees and graphs) 💪 .

⁠Kavita Sundaram

Analytics course alumna here! Warning: First two weeks feel very basic, but then it gets intense. The statistics part finally makes sense (hated it in college).

⁠Amit Kapoor

Straightforward review: DSA foundation is solid. But prep for long hours (especially if working). The dynamic programming module was tough, but the flowcharts helped. Some classes got extended beyond schedule. Good: They added extra doubt sessions before placement season.

⁠Rohit Verma

6 months, 400+ DSA problems, 8 interviews, and finally cracked a product based company! The structured roadmap works. Those 2 AM debugging sessions with batchmates are memorable 😅 Worth every rupee and every sleepless night. Just follow the schedule and trust your mentors.

⁠Divya R

Clear Explanations I used to get frustrated with DSA concepts, but the clear explanations at iSkills helped me break things down. Now, I approach problems with a much clearer mindset.

⁠Anuj G

Great Practice Problems The practice problems provided are both challenging and rewarding. They really test your understanding of DSA and help you prepare for real-world coding challenges.

⁠Priya Singh

Learning Made Simple iSkills takes the complex world of coding and analytics and makes it simple. The content is easy to digest, and the flexibility to learn at your own pace is a bonus.

⁠Kavita Sundaram

Smooth Transition I transitioned from preparing for CAT to diving into analytics, and iSkills made it seamless. The lessons are structured in a way that builds up your knowledge gradually but effectively.

⁠Rajesh Krishnamurthy

Perfect for Working ProfessionalsBalancing work and learning was tough, but iSkills made it easy with their flexible pre-recorded sessions. I could learn at my own pace without feeling rushed. Great learning experience overall.

⁠Arjun Mehta

Thoroughly ImpressedThe way the course covers everything from Python basics to statistical models is amazing. The pace is perfect for beginners and those with some experience. I feel more confident with analytics now.

⁠Amit Kapoor

Confidence BoostThe DSA course at iSkills is phenomenal. I was always struggling with data structures, but this course made me not only understand them but also apply them confidently in coding challenges.

⁠Aryan Malhotra

Mastering Analytics This course isn’t just about memorizing concepts. Its about applying them in real business scenarios. iSkills taught me how to think analytically, and that’s the real value.

⁠Vikram Singhania

Solid FoundationI started with zero knowledge of analytics, and now I feel ready to apply Python and statistical methods in my work. The course was designed so well that I could track my progress easily.

⁠Karthik Iyer

Clear, Concise, and FunThe best part? The course doesn’t just give you theory, but also explains why certain techniques are used in real business problems. Statistics that seemed difficult before are now super easy to understand.

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