Data Analytics for Digital Marketers (Cohort 1)
logo
Courses Digital Products Login
Contact us
Data Analytics for Digital Marketers (Cohort 1) cover

Data Analytics for Digital Marketers (Cohort 1)

Instructor: Umar Tazkeer / Shiva Sir

Language: English and Hindi

Validity Period: Lifetime

$350 65% OFF

$120

Note : Live Classes starting from 20th January, 2025

Our Course Specialities : 

✅ Complete Practical Course/ Live Sessions 

✅ 1.5 Months Course - Monday - Wednesday - Friday (8:00pm - 10:30pm)

✅ Get Certified by our Agency UT Digital Media 

✅ Tasks and Assignments during each Topic based on Practical Projects and Scenarios. 

✅ Recordings will be available.

✅ Limited Seats

✅ Trainers : Umar Sir & Shiva Sir (an IITian & Senior Data Consultant at Alliance Tek Solutions - Full-time)

✅ There is be private group / Community with batchmates. (Access will be there Post Course)

✅ Who can attend : Performance Marketers, Media Buyers


Session 1: Introduction to Data Analytics in Digital Marketing (2.5 hours)  
  Concepts:
  Introduction to Data Analytics: Understanding the importance of data in digital marketing.
  Overview of Tools: Excel, SQL, Power BI, and Looker Studio.
  Excel Basics:
  Excel interface and navigation.
  Data entry and formatting.
  Basic formulas (SUM, AVERAGE).
  Overview of digital marketing metrics.
  Outcome:
  Understanding of the full Domain Data Science, Machine Learning, AI, Gen AI, Data Analytics, Business Analytics
  Understanding of the Tools working in the Real world - Excel, Python, SQL, Tableau, Power BI, Looker Studio
  Basic Understanding of working on Excel - Interface, Navigation, Basic Operations
Session 2: Excel Functions for Data Analytics (2.5 hours)  
  Concepts:
   
  Excel Functions:
  Essential Mathematical Functions
  SUM, AVERAGE AND MAX FUNCTION
  MIN, ROUND AND SUMIF FUNCTION
  COUNT, COUNTA AND COUNTIF FUNCTION
  Essential Text Functions
  Implementing LEFT, RIGHT AND CONCATENATE FUNCTION
  Implementing UPPER, LOWER AND MID FUNCTION
  Implementing PROPER, TRIM AND REPT FUNCTION
  Essential Date & Time Functions
  Implementing TIME and DATE Functions
  Conditional Formatting:
  Formatting data based on conditions.
  Digital Marketing Use Cases:
  ROI, CPC, CTR, conversion tracking.
  Analysis of campaign performance.
  Outcome:
  Understanding of functions required to perform arithmetic operations.
  Hand-On operations on the real Data
  Data Cleaning using text data
  Creating KPIs using Excel functions
Session 3: Pivot Tables and Case Study (2.5 hours)  
   
  Concepts:
   
  Excel Concepts:
  Essential Logical Functions
  AND, OR FUNCTION
  IF & VLOOK FUNCTION
  Essential DataValidation & Filter Functions
  Data Validation and Filter
  Pivot Tables
  Case Study:
  Inventory Data (E-Commerce).
  Segmenting customers and analyzing conversions.
  Outcome:
  Unerstaning of Advance Excel concepts like VlookUp or Pivot Table
  Creating detailed Reports, MIS, Dashboards using Advance Excel
   
Session 4 Capstone Project for Excel: Inventory Management for a Shopify Store Owner  
   
  Scenario: Optimizing Inventory Management for a Shopify Store
  Objective:
  You are tasked with helping a Shopify store owner optimize their inventory management process using Excel.
The owner has data on product sales, inventory levels, suppliers, and order history.
Your goal is to use Excel to analyze sales trends, forecast future demand, and create visualizations that help the owner manage stock levels more efficiently.
   
  Steps:
   
  Data Cleaning:
  Raw Data:
  The data includes product details (SKU, category, supplier), sales data (date, quantity sold, sales price), and inventory levels (current stock, reorder point).
  Cleaning Process:
  Remove duplicate entries.
  Fill in missing product details (e.g., supplier or category).
  Format dates and quantities to ensure consistency.
  Sales Trend Analysis:
  Pivot Tables:
  Create pivot tables to analyze sales by product category, month, and supplier. This will help identify which products are selling the most and when demand is highest.
  Forecasting Demand:
  Use Excel's built-in forecasting tools to predict future demand based on historical sales data.
  Calculate the average sales per month and use this to estimate future sales for each product category.
  Identify products with seasonal sales trends and adjust forecasts accordingly.
  Inventory Management:
  Reorder Point Analysis:
  Calculate the reorder point for each product using sales velocity (average sales per day) and lead time (time it takes for suppliers to deliver products).
  Identify products that are close to or below their reorder points.
  Create a dynamic inventory management sheet that updates as sales occur.
  Supplier Analysis:
  Track the performance of each supplier, including delivery times and the accuracy of order fulfillment.
  Visualization and Reporting:
  Sales Dashboards:
  Build dashboards to visualize inventory levels, reorder points, and sales performance.
  Inventory Alerts:
  Create conditional formatting to highlight products that need to be reordered soon or that have overstock issues.
  Trend Graphs:
  Visualize sales trends over time, highlighting peak periods and slow-moving products.
  Capstone Presentation:
  Present your findings on inventory performance, highlighting products that require better inventory management or more frequent reordering.
  Offer actionable insights on improving inventory management based on sales trends and forecasted demand.
  Suggest ways to optimize stock levels to prevent overstocking or stockouts, reducing holding costs and improving cash flow.
   
  Outcome:
  Students will learn how to clean and analyze sales and inventory data in Excel, create pivot tables and forecasts, and visualize key metrics for better inventory management. This will help Shopify store owners optimize their inventory processes and make data-driven decisions about stock levels and supplier relationships.
   
   
Session 5: Introduction to SQL (2.5 hours)  
  Concepts:
   
  SQL Basics:
  What is RDBMS and Schema
  Primary/Foreign Key
  Basic Data Types
  SELECT, FROM, WHERE, ORDER BY queries.
  Null, And, OR, NOT Keywords
  Filtering Data:
  Querying customer data based on marketing criteria.
  Marketing Use Cases:
  Extracting specific performance data from SQL databases.
  Outcome:
  Setting up the database and querying from the same.
  Fetching necessary data from the database
  Filtering the results using Where Clause along with And, OR, NOT Keywords
   
   
   
Session 6: Advanced SQL: Joins and Subqueries (2.5 hours)  
   
  Concepts:
   
  Joins:
  In, Between, and LIKE function
  Row function Single, Multiple
  INNER JOIN, LEFT JOIN for combining datasets.
  Subqueries:
  Writing subqueries for more complex data analysis.
  Cross-Channel Analysis:
  Analyzing customer behavior across channels using SQL.
  Outcome:
  Ineractiong with multiple tables while working on Huge Database.
  Combining the results from multiple tables using Joins
  Summarising the data using Row Functions
   
Session 7: Data Aggregation: GROUP BY and Digital Marketing Metrics (2.5 hours)  
   
  Concepts:
   
  GROUP BY and HAVING:
  Window Functions
  Rank vs Dense Rank
  Row Number, Avg, Count
  Case When
  Group by & Having Clause
  Where VS Having Clause
  Aggregation Functions:
  SUM, AVG, COUNT for campaign performance analysis.
  Digital Marketing Metrics:
  Calculating CTR, CPC, CAC, and ROI using SQL.
  Outcome:
  Analysing huge data records as summarisation using aggregation functions.
  Utilize aggregation functions (SUM, AVG, COUNT) for campaign performance analysis.
  Calculate key digital marketing metrics like CTR, CPC, CAC, and ROI using SQL.
  Interpret and analyze results of SQL queries to draw meaningful insights.
   
Session 8: SQL Final Practice, 50 Questions (2.5 hours)  
   
  Concepts:
   
  Hands-On SQL:
  Implementing the SQL queries coming from different concepts.
  Audience Segmentation:
  Segmenting customers based on demographics and behavior using SQL.
  SQL Use Cases:
  Segmenting high-value customers for targeted campaigns.
   
   
Session 9 Capstone Project for SQL: Performance Marketing Campaign Analysis for Pepsi  
   
  Scenario: Multi-Channel Advertising Performance Analysis for Pepsi
  Objective:
 

Pepsi has launched multiple marketing campaigns across channels including TV, digital (social media, search ads), and outdoor advertising. Your task is to analyze the performance of these campaigns using SQL, identifying which channels are driving the most conversions, which audience segments are responding best, and how Pepsi can optimize its advertising spend across these channels.
   
  Steps:
   
  Capstone Analysis:
  Channel Performance Analysis:
  Use SQL to compare the performance of different advertising channels (TV, digital, outdoor) based on impressions, clicks, and conversions.
  Aggregate the data by channel and calculate metrics like conversion rate and cost-per-conversion.
  Ad Spend Efficiency:
  Analyze how efficiently Pepsi is using its advertising budget by calculating the return on ad spend (ROAS) and cost-per-acquisition (CPA) for each channel.
  Audience Segmentation:
  Segment the audience based on demographics (age, gender, location) and analyze which segments are responding best to Pepsi's ads across different channels.
  Identify high-performing segments and recommend tailored ad strategies.
  Time-Based Analysis:
  Query the data to identify the best-performing times or days for Pepsi’s ads, focusing on when engagement rates and conversions are highest.
  Presentation:
  Present findings on the performance of Pepsi’s marketing campaigns, highlighting which channels are the most effective in terms of conversions, ROAS, and CPA.
  Provide recommendations for optimizing Pepsi's marketing budget by redistributing spend towards more cost-effective channels or high-performing audience segments.
  Identify key insights on how Pepsi can adjust their ad schedule for better performance based on time-based trends.
   
  Outcome:
  Students will use SQL to analyze complex advertising data, segment audiences, and identify actionable insights that can optimize Pepsi’s marketing strategies. This project demonstrates the power of SQL in marketing analytics, particularly for managing large datasets and uncovering patterns in performance.
   
Session 10: Looker Studio - Building an Interactive Dashboard (2.5 hours)  
   
  Concepts:
   
  Dashboard Design:
  Connecting data sources, adding filters, and designing dashboards.
  Customization of the Charts
  Formatting of the charts
  Understanding the use cases of every chart
  Interactive Elements:
  Visualizing real-time campaign performance using Looker Studio.
  Campaign Monitoring:
  Building dashboards for tracking KPIs like conversions and CTR.
   
  Outcome:
  Design and build interactive dashboards in Looker Studio by connecting data sources and adding filters.
  Customize charts and visualizations for effective data presentation and insights.
  Monitor campaign performance in real-time by building dashboards that track key KPIs like conversions and CTR.
Session 11: Introduction to Power BI (2.5 hours)  
   
  Concepts:
   
  Power BI Basics:
  Connecting Power BI to Excel and SQL data sources.
  Data Modelling in Power Query
  Power Query and basic transformations,
  Merge and split columns,
  Power Query: Add columns and data types
  Data Transformation:
  Cleaning and modeling data in Power BI.
  Visualizing Campaign Data:
  Creating simple reports and visualizations of marketing metrics.
  Outcome:
  Connect to various data sources like Excel and SQL and perform basic data transformations using Power Query.
  Clean, model, and prepare data for analysis within the Power BI environment.
  Create basic reports and visualizations of marketing metrics using Power BI's interactive features.
Session 12: Creating Basic Reports in Power BI (2.5 hours)  
   
  Concepts:
   
  Report Building:
  Building reports with bar charts, line charts, pie charts
  Visual Creation and Format Pane
  Types of visuals in Power BI, choosing the correct visual
  DAX - Calculated column and measures
  Create simple report and AI visuals in Power BI
  Customer Journey Analysis:
  Tracking customer behavior across multiple channels.
  Case Study:
  Creating reports to monitor key marketing KPIs.
  Outcomes:
  Build comprehensive reports using a variety of visualizations like bar charts, line charts, and pie charts in Power BI.
  Master DAX for creating calculated columns and measures to enhance data analysis.
  Analyze customer journeys and monitor key marketing KPIs through the creation of insightful reports in Power BI.
   
Session 13: Power BI Dashboards and Advanced Visualizations (2.5 hours)  
   
  Concepts:
   
  Building Dashboards:
  Filter Pane
  Hirerachy, Drill up and drill down
  Sync slicers, themes, and performance analyzers
  Conditional formatting and Bookmarks
  Drillthrough, Field Parameter
  Row-level Security
  Power BI Service and Dashboard Creation
  Advanced Visualizations:
  Using heatmaps, time series, and scatter plots for campaign analysis.
  Performance Tracking:
  Building dashboards to track conversion rates and ROI.
  Outcome:
  Design and build interactive dashboards in Power BI using advanced features like filters, slicers, hierarchies, and drill-through functionality.
  Utilize a range of advanced visualizations such as heatmaps, time series, and scatter plots to effectively analyze campaign performance.
  Develop dashboards for comprehensive performance tracking of key metrics like conversion rates and ROI within the Power BI environment.
   
Session 14 Capstone Project for Power BI: Website Traffic Dashboard  
   
  Scenario: Building a Website Traffic Dashboard for a Content-Based Website
  Objective:
  You are tasked with creating a Power BI dashboard to visualize website traffic for a content-based website (e.g., a blog or news site).
The data includes user sessions, page views, bounce rates, traffic sources (organic, social media, referrals), and engagement metrics.
The goal is to help the website owner understand their traffic patterns, improve user engagement, and optimize content for better performance.
   
  Steps:
   
  Data Integration:
  Import Data:
  Import data from Google Analytics or similar web analytics tools. The data includes session data, page views, traffic sources, bounce rates, and average session duration.
  Data Cleaning and Transformation:
  Clean the data within Power BI to ensure it is ready for analysis. This may include normalizing page URLs, handling missing values, and standardizing traffic source names.
  Dashboard Design:
  Traffic Overview:
  Create a dashboard page that provides an overview of website traffic, including metrics such as total sessions, unique visitors, page views, and bounce rates.
  Source Analysis:
  Visualize traffic by source (e.g., organic search, social media, referrals) to understand where users are coming from and which channels are most effective at driving traffic.
  Engagement Metrics:
  Track user engagement metrics such as average session duration, pages per session, and bounce rate to identify how engaged users are with the content.
  Top Pages:
  Highlight the top-performing pages based on page views, time on page, and engagement, helping the website owner understand which content resonates most with users.
  Key Metrics to Track:
  Sessions and Page Views: Measure overall website traffic and identify trends over time.
  Bounce Rate and Session Duration: Analyze how well the site retains users and which pages or sections have high bounce rates.
  Traffic Source Performance: Identify which traffic sources are driving the most engaged visitors and conversions (e.g., newsletter sign-ups, content shares).
  Presentation of Insights:
  Present the Power BI dashboard to the website owner, showcasing insights into traffic patterns and user engagement.
  Provide recommendations on how to optimize content based on the insights from the dashboard, such as improving underperforming pages or focusing more on high-performing traffic sources.
  Highlight areas of improvement, such as pages with high bounce rates, and suggest content or UX optimizations to reduce them.
   
  Outcome:
  Students will build an interactive dashboard that visualizes key website traffic metrics, helping website owners understand how their audience interacts with their content. This project allows students to practice data integration, transformation, and visualization using Power BI, with a focus on web analytics and user engagement.
   
Session 15: Introduction to Final Capstone Project (2.5 hours)  
   
  Concepts:
   
  End-to-End Analysis:
  Combining Excel, SQL, Power BI, and Looker Studio for comprehensive reporting.
  Project Guidelines:
  Structuring the capstone project, setting objectives, and expectations.
  Final Campaign Data:
  Preparing for the end-to-end analysis of digital marketing data.
   
Session 16: Final Presentation of Capstone Projects (2.5 hours)  
   
  Concepts:
   
  Capstone Presentation:
  Presenting the final analysis of a marketing campaign.
  Key Insights:
  Highlighting data-driven insights and recommendations.
  Q&A:
  Answering questions on the analysis and methodologies used.
   
Session 17: Gen AI for Data Analytics in Digital Marketing (2.5 hours)  
   
  Concepts:
   
  Introduction to Gen AI:
  Understanding how AI is transforming digital marketing.
  AI Use Cases:
  Predictive analytics, customer behavior analysis, personalized campaigns.
  AI-Driven Tools:
  Overview of tools used for AI-powered marketing analytics.
   
Session 18: LinkedIn Optimization, Portfolio Building (2.5 hours)  
   
  Concepts:
   
  LinkedIn Optimization:
  Best practices for optimizing LinkedIn profiles for digital marketing roles.
  Portfolio Building:
  Creating a professional portfolio with digital marketing data analytics projects.
  Job Search Strategies:
  Leveraging LinkedIn and other platforms to find roles in digital marketing analytics.

Reviews
Other Courses
Launch your GraphyLaunch your Graphy
100K+ creators trust Graphy to teach online
Umar Tazkeer 2025 Privacy policy Terms of use Contact us Refund policy