Become an AI-Powered Certified Data Analyst with SQL & Tableau

As students of Data analysis you will be introduced to data analysis softwares and techniques.

What you’ll learn:

1. How to identify and remove duplicate records from a dataset using SQL.

2. How to find and handle missing values, such as blank or null fields, by filling, replacing, or excluding them.

3. How to ensure consistency in data formats, like standardizing date entries, numbers, and text.

4. How to correct data entry errors and resolve inconsistencies (e.g., fixing spelling errors, inconsistent labels).

5. How to split or merge data columns as needed to restructure information for easier analysis and visualization.

TEXT FUNCTOWhat you’ll learn:

1. Introduction to text-based operations in SQL, such as trimming spaces, extracting parts of text, replacing characters, and formatting text.

2. Practice applying these techniques to clean and prepare text data from sample datasets before using them in Tableau.

Basic Functions

What you’ll learn:

1. Introduction to basic calculations and summary techniques in SQL, such as finding totals, averages, minimum and maximum values.

2. Practice performing these calculations on sample datasets and create visual summaries in Tableau for analysis.

Project Objective:

As a Data Analyst at Netflix, your primary responsibility is to clean and prepare the content library dataset using SQL for data transformation and Tableau for visualization and reporting. The goal is to enhance the performance of the content recommendation system by ensuring the data is accurate, complete, and consistent.

Key Tasks:

1. Use SQL queries to detect missing values across various columns to identify data quality issues.

2. Apply appropriate SQL-based strategies to handle missing data:

   • Impute missing values using functions like, averages, or subqueries.

   • Replace with default values where appropriate.

   • Remove rows or columns using where clauses if data is insufficient.

3. Identify and remove duplicate records using SQL ROW_NUMBER() or DISTINCT to ensure the dataset contains only unique entries.

4. Check and standardize data formats using SQL functions:

   • Use CAST() or CONVERT() to fix date formats.

   • Normalize ratings and other inconsistent formats using conditional logic (statements).

5. Standardize categorical values like genres and countries:

   • Clean inconsistencies using SQL string functions or mappings.

   • Create lookup tables where necessary to enforce consistency.

6. Validate numeric fields such as duration and user ratings:

   • Use tools to check for valid ranges and flag anomalies.

7. Cross-check temporal data for logic:

   • Use SQL logic to ensure values 

   •  follow chronological order.

8. Use Tableau to visualize:

   • Missing data distributions,

   • Duplicates and anomalies,

   • Outliers in numeric fields,

   • Trends and inconsistencies in date-related information.

What you’ll learn 

• IF function: Basic usage and syntax.
• Creating more complex conditional logic by nesting IF functions.
• Introduction to the IFS function as an alternative to nested IFs.

Advanced Logical Functions

ACTIVITIES:

• Understanding and applying the SUMIF, COUNTIF, AVERAGEIF functions.
• Use SUMIF with multiple criteria (by combining with SUMIFS).
• Use COUNTIF with multiple criteria (by combining with COUNTIFS).
• Use AVERAGEIF with multiple criteria (by combining with AVERAGEIFS).

1. Background:
   A global retail company aims to enhance its marketing campaigns by boosting sales and improving customer engagement. The marketing team has collected campaign-related sales data over the past year, including customer details, transaction history, and campaign-specific information. Your job is to analyze this data using SQL for querying and Tableau for visualization to uncover insights and improve future marketing strategies.

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   Your Responsibilities:

   • Use SQL conditional logic to segment customers based on criteria such as age ranges, purchase volume, and loyalty membership status, and visualize the segments in Tableau.

   • Identify high-value customers by analyzing those who make large or frequent purchases or show high engagement with past campaigns.

   • Evaluate the performance of each marketing campaign by calculating:

     • Total sales

     • Average transaction amount

     • Number of transactions per campaign

   • Apply logical filters in SQL to compare campaign performance across regions, discount usage, and sales channels, and present findings in Tableau dashboards.

   • Use conditional logic to identify sales trends, such as:

     • Peak shopping periods

     • Top-performing product categories

     • Regional buying preferences

   • Analyze the behavior of loyalty program members versus non-members, focusing on differences in purchasing habits and campaign response rates.

   • Based on your SQL analysis and Tableau reports, propose data-driven strategies to improve future campaigns—such as focusing on profitable segments, refining discount offers, or prioritizing the most successful regions and channels.

What you’ll learn

• Introduction to lookup concepts in SQL for retrieving related data from multiple tables.

• Practice performing basic table joins using simple datasets.

• Learn how to use nested queries to retrieve related values for more advanced analysis.

• Explore alternative lookup approaches, such as using subqueries and table relationships.

• Understand how to combine multiple conditions when retrieving data across tables.

• Learn the benefits of using joins and subqueries compared to basic lookups when working with large and complex datasets.

Background:
A retail company wants to improve its sales reporting by combining data from multiple sources to gain deeper insights into performance. Sales transactions, customer profiles, and product information are stored in separate datasets. Your role is to use SQL for data integration and Tableau for visualization and reporting to carry out a detailed sales analysis.

Your Responsibilities:

• Use SQL joins to combine data from the Sales Transactions, Customers, and Products tables into a single, unified dataset.

• Build a complete sales report that includes customer names, product names, product categories, regions, and loyalty program membership for each transaction.

• Categorize and group sales data by product category to identify high-performing product lines.

• Segment the data by customer region and loyalty program status to analyze purchasing trends across different customer types.

• Analyze regional sales contributions and compare buying behavior between loyalty program members and non-members.

• Connect the sales data to the product supplier information to understand supplier performance and dependency.

• Calculate key sales metrics such as:

  • Average order value

  • Total units sold

  • Revenue per customer

• Based on your findings, recommend strategies to:

  • Target high-value customers

  • Optimize inventory management

  • Strengthen supplier relationships

• What you’ll learn

  • Plan and organize the layout and structure of dashboards for clear data storytelling.

  • Use interactive elements such as filters and dynamic visuals to explore data.

  • Display important performance indicators and key metrics effectively within the dashboard.

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  PRESENTATION

  • Collaborate in teams to prepare and deliver presentations based on your analysis.

  • Practice presenting your findings with clarity and confidence.

  • Collect feedback from peers and instructors, and refine your work based on suggestions.

• Background:
  A top bike retailer wants to strengthen its business strategy by gaining deeper insights from its sales data. The company aims to uncover sales patterns, understand customer behavior, and improve inventory planning. Your task is to use SQL to analyze the data and Tableau to build an interactive dashboard that provides actionable insights for decision-making.

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  Your Responsibilities:

  • Analyze sales trends over time to identify high-performing months, peak seasons, and fluctuations in demand.

  • Assess how different bike categories perform based on units sold and revenue generated.

  • Segment the customer base by age, gender, and location to better understand buying preferences and behavior.

  • Design and build an interactive Tableau dashboard that clearly communicates your findings.

  • Display important KPIs, such as:

    • Total sales

    • Average order value

    • Best-selling bike types

    • Customer demographics

    • Sales by region

  • Add interactive filters to the dashboard so users can explore the data by time range, region, or customer segments.

  • Use a mix of visual formats—including bar charts, line graphs, pie charts, and heat maps—to present insights in a visually compelling way.

  • Provide data-driven recommendations on which bike types to prioritize in marketing and inventory, based on customer demand and sales performance.

ACTIVITIES

• Identify and work with different data sources such as CSV files, Excel sheets, and databases.

• Import and load data into a SQL environment for querying and into Tableau for visualization.

Data Cleaning and Transformation

ACTIVITIES

• Use SQL queries to clean and prepare raw data (e.g., removing nulls, fixing inconsistencies, formatting columns).

• Apply correct data types to columns (e.g., date, numeric, text) to ensure consistency in analysis.

Data Modeling

ACTIVITIES

• Establish relationships between tables in your database using appropriate join keys.

• Identify and define primary keys and foreign keys to support relational analysis in SQL and Tableau.

Background:
Choprite, a multi-location retail chain, has collected a large amount of sales data over the past year. However, the data is unstructured, with inconsistencies and missing values. Management wants to use this data to evaluate sales performance, uncover trends, and support better decision-making. Your task is to clean, transform, and analyze the data using SQL and Tableau to produce actionable insights.

Your Responsibilities:

• Detect and handle missing values in the dataset by using appropriate methods such as filling with averages or removing records with too much missing information.

• Identify and eliminate duplicate transactions to ensure the dataset accurately reflects unique sales records.

• Verify sales accuracy by checking that the total sale amount equals the quantity sold multiplied by the unit price, and correct any mismatches.

• Group similar products into broader categories where necessary, and generate sales summaries by category to aid in product-level insights.

• Analyze overall sales trends over time, including identifying:

  • Peak sales periods

  • Seasonal buying patterns

  • Performance across different store locations

• Examine customer purchasing behavior to:

  • Identify high-value customers

  • Understand how discounts influence sales activity

• Based on your analysis, recommend product categories or individual items that should be prioritized in marketing and inventory decisions, based on demand and revenue contribution.

ACTIVITIES

• Create basic visualizations such as bar charts, line graphs, pie charts, etc., in Tableau.

• Use visual analysis to explore data distributions and uncover trends and patterns.

Calculated Fields and Metrics

ACTIVITIES

• Create summary statistics (e.g., totals, averages, counts) using calculated fields in Tableau.

• Build custom calculations and formulas for more advanced metrics.

• Use calculated fields to create conditional logic and derived values within your dashboards.

Dashboard Creation

ACTIVITIES

• Design and build interactive dashboards in Tableau.

• Add essential visual components like charts, tables, and KPIs to your dashboard layout.

• Enable user interaction through filters, slicers, and drop-down controls for dynamic exploration.

Background:
A retail analytics firm has collected a detailed dataset from a major online retailer to explore consumer shopping behavior. The goal is to uncover patterns in how customers shop, identify trending product interests, and generate strategic insights to improve marketing, product selection, and overall customer experience. Your task is to use SQL for analysis and Tableau for visualization to deliver meaningful insights and recommendations.

Your Responsibilities:

• Use SQL to analyze customer demographics and segment shoppers by age, gender, income level, and region. Determine the most valuable segments based on spending habits and purchase frequency.

• Identify the most popular product categories for each customer segment and highlight any emerging preferences or changes in demand.

• Examine purchase timing patterns, including days of the week, times of day, and seasonal fluctuations, to understand when customers are most active.

• Analyze device usage (mobile, desktop, tablet) and determine if purchase behavior varies by device type, including order size, frequency, and product type.

• Evaluate payment method preferences across customer groups. Identify which methods are preferred by different demographics or during specific times of year.

• Identify frequently co-purchased products using transactional data. Use these patterns to reveal cross-selling or bundling opportunities.

• Analyze product relationships by segment to find out which types of products tend to be purchased by similar customer groups, indicating product affinities.

• Based on your findings, provide data-driven marketing recommendations, including targeting strategies by segment, device type, and purchase timing.

• Design Tableau dashboards and visualizations that clearly communicate key shopping trends, allowing real-time strategic decisions by the business team.

• • We will help you prepare for technical and HR interviews for Data Analysis roles, focusing on SQL and Tableau skills.
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In this course, you will execute between 15 to 20 mini-projects and 3 Capstone Projects to prepare you for job opportunities in data. We know exactly what you need to get a job as a data analyst and we will equip you with all the skills and confidence that you need to land any job opportunity in data analysis.

In this segment of the training, We will help you prepare for technical and HR interviews for Data Analysis Roles. Also, we will provide you with training in CV writing skills and LinkedIn Optimization. Everything you need to grab any job opportunity.

• Get guidance from a top data analyst in an “Ask-Me-Anything” session.

• • A top freelancer will share a blueprint for earning a remote income with your data analysis skills.
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• Receive a guide on applying for the Skilled Worker Visa and Tech Nation Visa.

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