Become a Job-Ready Data Analyst in 4 Months!

Complete Data Analytics Bootcamp : Excel, SQL, Python, Tableau, Power BI

Gain practical, in-demand data analytics skills through guided training, real-world projects, and structured career preparation designed to help you confidently transition into a data analyst role.

4.1 (20 reviews) 1,003 students enrolled 4 Months

Last updated: 19 April 2026 Available in: English, French, Pidgin (West African)

Data Analytics

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Data Analytics Training for Teams

Citichoice Institute partners with organizations to deliver tailored data analytics training that improves decision-making, operational efficiency, and data literacy across teams, with customized curriculum. Flexible delivery. Measurable outcomes.

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Your Destination

Launch Your Career as a Data Analyst

The Complete Data Analytics Bootcamp at Citichoice Institute is designed to take you from beginner to confident data analyst through structured learning, hands-on projects, and guided career preparation.

You will work with industry-standard tools such as Excel, SQL, Python, Tableau, and Power BI while solving real business problems that reflect how data analytics is used in professional environments.

Our approach emphasizes practical application over theory, ensuring you graduate with the skills, confidence, and portfolio needed to pursue data analyst roles across multiple industries.

What's included on this platform

Live Classes

Real-time instruction via Microsoft Teams with Q&A

Course Groups

Private peer communities with expert moderation

Social Feed

Progress sharing and achievement recognition

Direct Messages

Instructor & peer messaging with quick support

Project Library

Realistic datasets and personalised feedback

Certificates

Verified, employer-recognised digital badges

Start Dates & Learning Options Flexible Learning Options

Available Now

Self-Paced

Enrolling Now

Start immediately on enrolment

Start immediately and learn at your own pace. No deadlines or fixed schedule.

What You'll Gain 3 Benefits

Expert Instructors

Learn from industry professionals with 8+ years of experience at companies like Google and McKinsey.

Hands-On Projects

Build a job-ready portfolio with 5+ graded projects using real retail and finance datasets.

Job Placement

Resume workshops, 1:1 mock interviews, and access to our exclusive 500+ partner job board.

Tools You'll Master 4 Tools

Microsoft Excel

Develop advanced proficiency in Excel for analytical work, including complex formulas, Pivot Tables, data modeling, and Power Query for cleaning and transforming datasets. You’ll learn how to structure raw data, perform analysis efficiently, and build professional dashboards and reports used in real business environments.

PostgreSQL

Learn how to design and interact with relational databases using SQL. You’ll write complex queries involving JOINs, aggregations, subqueries, and filters to extract insights directly from structured data sources, mirroring how analysts work with production databases in real organizations.

Python 3.x

Use Python as a powerful analytical tool for working with real-world data. You’ll leverage Pandas and NumPy to clean and manipulate datasets, perform exploratory data analysis, and automate workflows, while using visualization libraries to uncover trends and patterns efficiently.

Tableau & Power BI

Master industry-leading business intelligence tools to transform analysis into insight. You’ll connect to multiple data sources, create calculated fields and measures, and design interactive dashboards that clearly communicate findings to decision-makers using data storytelling best practices.

Course Curriculum 33 Modules · 204 Lessons · 16 Tests · 32 Assignments · 12 Projects

8–12 hrs/week / week 4 Months Lifetime Access

Section Welcome & Orientation

1 Welcome to the Data Analytics Bootcamp

Navigating the Citichoice Learning Platform 18:06

Course Structure and Certification Path Overview 09:41

Meet Your Instructor and Mentor 05:48

Understanding the Modern Data Analyst Role 25:35

Setting Realistic Goals and a Study Schedule 13:18

How to Maximize Your Learning and Career Outcomes

Introduce Yourself and Your Goals on the Community Forum

2 Setting Up Your Professional Analytics Environment

Installing Microsoft Excel and the Analysis ToolPak 18:41

Setting Up a Local SQL Environment with PostgreSQL and DBeaver 22:43

Installing Python and Anaconda for Data Science 15:26

Configuring Jupyter Notebooks for Your First Python Scripts

Installing Tableau Public for Data Visualization

Setting Up a Microsoft Power BI Desktop Account

Creating a GitHub Account for Version Control and Portfolio Hosting

Joining the Course Slack Channel for Real-Time Collaboration

Verify Your Software Installations with a Configuration Checklist

Development Environment Setup Check

Section Data Analytics Fundamentals

3 The Data Analytics Lifecycle and Methodologies

Understanding the Ask: Defining Business Questions

The CRISP-DM Framework: From Business Understanding to Deployment

Data Collection: Sources, Types, and Methods

Data Cleaning: Identifying and Handling Dirty Data

Exploratory Data Analysis (EDA): Uncovering Initial Insights

Data Modeling and Interpretation

Data Storytelling: Communicating Findings to Stakeholders

Ethical Considerations in Data Analytics

Apply the CRISP-DM Framework to a Sample Business Case

Core Concepts of the Data Lifecycle

4 Core Statistical Concepts for Data Analysts

Differentiating Between Descriptive and Inferential Statistics

Measures of Central Tendency: Mean, Median, and Mode

Measures of Dispersion: Variance, Standard Deviation, and Range

Understanding Distributions: Normal, Skewed, and Bimodal

Introduction to Probability and Its Role in Analytics

Correlation vs. Causation: Avoiding Common Pitfalls

Introduction to Hypothesis Testing and A/B Testing Concepts

Identifying and Handling Outliers in a Dataset

Calculate Key Descriptive Statistics for a Provided Dataset

Foundational Statistics Knowledge Check

Section Stage 1 – Mastering Data Analysis with Microsoft Excel

5 Excel Basics for Data Analysis

Navigating the Excel Interface: Ribbons, Sheets, and Cells

Mastering Data Entry and Formatting Techniques

Understanding Cell References: Relative, Absolute, and Mixed

Sorting and Filtering Data to Find Information Quickly

Using Tables for Structured Data Management

Introduction to Conditional Formatting for Visual Analysis

Essential Keyboard Shortcuts to Boost Productivity

Format and Structure a Raw Sales Dataset Using Excel Tables

6 Formulas and Functions for Data Manipulation

Core Mathematical and Statistical Functions (SUM, AVERAGE, COUNT)

Logical Functions: IF, AND, OR, nested IF statements

Lookup Functions: VLOOKUP, HLOOKUP, and the Modern XLOOKUP

Advanced Lookups with INDEX and MATCH

Text Functions for Cleaning and Manipulation (LEFT, RIGHT, CONCAT, TRIM)

Date and Time Functions for Time-Series Analysis

Combining Functions for Complex Problem Solving

Introduction to Array Formulas

Enrich Customer Data Using VLOOKUP and XLOOKUP

Clean and Standardize Product Names with Text Functions

Excel Formulas and Functions Mastery

7 Advanced Data Analysis with PivotTables and Slicers

Creating Your First PivotTable from a Data Range

Understanding the PivotTable Fields: Rows, Columns, Values, and Filters

Grouping Data by Dates, Numbers, and Custom Categories

Calculated Fields and Items for Custom Metrics

Summarizing Values with Different Calculation Types

Using Slicers and Timelines for Interactive Filtering

Creating PivotCharts to Visualize PivotTable Data

Building a Basic Dashboard with Multiple PivotTables

Analyze Sales Performance Across Regions and Product Categories using PivotTables

PivotTable Concepts and Application

Project: Interactive Regional Sales Performance Dashboard in Excel

8 Data Cleaning and Modeling with Power Query

Introduction to Power Query (Get & Transform Data)

Connecting to Various Data Sources (CSV, Excel, Web)

The Power Query Editor Interface and Applied Steps

Transforming Data: Splitting Columns, Changing Data Types, and Filtering

Merging and Appending Queries to Combine Datasets

Pivoting and Unpivoting Data for Proper Structuring

Introduction to the M Language for Custom Transformations

Loading Transformed Data into Excel or the Data Model

Clean and Combine Multiple Monthly Sales Files with Power Query

9 Excel Stage Assessment

Project: Comprehensive Financial Analysis and Dashboard for a Retail Company (CCS Excel Certification)

Section Stage 2 – Querying Databases with SQL

10 Introduction to Relational Databases and SQL

What is a Database? Files vs. Databases

Understanding Relational Database Concepts: Tables, Rows, and Columns

Primary Keys, Foreign Keys, and Establishing Relationships

Introduction to SQL: The Language of Data

Exploring a Database Schema with an ERD (Entity Relationship Diagram)

Different Types of SQL: DQL, DML, DDL

Writing Your First Query with the SELECT Statement

Map out the relationships in a sample database schema

Relational Database Fundamentals

11 Foundational SQL: Filtering, Sorting, and Aggregating Data

Filtering Data with the WHERE Clause

Using Comparison, Logical, and Special Operators (BETWEEN, IN, LIKE)

Handling NULL values in your queries

Sorting Results with ORDER BY

Limiting Results with LIMIT and FETCH

Creating Calculated Columns with Aliases

Introduction to Aggregate Functions: COUNT, SUM, AVG, MIN, MAX

Grouping Data with GROUP BY

Filtering Grouped Data with HAVING

Query a Customer Database to Find High-Value Customers

Calculate Average Product Prices by Category

SQL Filtering and Aggregation

12 Intermediate SQL: Joining Tables and Subqueries

The Theory Behind Joins: Understanding Set Theory

Mastering INNER JOIN to Combine Related Tables

Using LEFT JOIN and RIGHT JOIN for Asymmetrical Data

Understanding FULL OUTER JOIN

Joining Multiple Tables in a Single Query

Using Table Aliases for Cleaner and More Readable Queries

Introduction to Subqueries (Queries within Queries)

Combining Result Sets with UNION and UNION ALL

Join Customer and Order Tables to Analyze Purchasing Behavior

SQL Joins and Subqueries Knowledge Check

Project: Multi-Table E-commerce Database Querying Project

13 Advanced SQL: Window Functions, CTEs, and Data Transformation

Introduction to Window Functions for Complex Analysis

Using ROW_NUMBER, RANK, and DENSE_RANK

Calculating Running Totals and Moving Averages 34:37

Organizing Queries with Common Table Expressions (CTEs)

Conditional Logic with CASE Statements

Data Type Conversion with CAST and CONVERT

String Manipulation Functions in SQL

Date and Time Manipulation in SQL

Rank Products by Sales Within Each Category Using Window Functions

Refactor a Complex Subquery into a Readable CTE

Advanced SQL Techniques

14 SQL Stage Assessment

Project: In-Depth Analysis of a Digital Music Store Database (CCS SQL Certification)

Section Stage 3 – Data Analysis and Automation with Python

15 Python Fundamentals for Data Analysis

Introduction to Python and its Role in Data Analytics

Working with Variables and Basic Data Types (int, float, string)

Core Data Structures: Lists, Tuples, and Dictionaries

Controlling Program Flow with Loops and Conditional Statements

Writing Reusable Code with Functions

Introduction to Python Libraries for Data Analysis

Reading and Writing Files (CSV, Excel) in Python

Write a Python Script to Process a Simple CSV File

Python Programming Basics

16 Data Manipulation with Pandas

Introduction to the Pandas Library: Series and DataFrames

Importing Data into a DataFrame from Various Sources

Exploring a DataFrame: .head(), .info(), .describe()

Selecting Data: Slicing, Dicing, and Indexing with loc and iloc

Filtering DataFrames Based on Conditions

Adding and Removing Columns

Handling Missing Data: Dropping vs. Imputing

Basic Data Cleaning and Type Conversion

Load, Inspect, and Clean a Real-World Dataset with Pandas

Pandas DataFrame Manipulation

17 Advanced Pandas: Grouping, Merging, and Analyzing Data

Grouping Data with groupby() and Performing Aggregations

Combining DataFrames: Merging, Joining, and Concatenating

Working with Multi-Index DataFrames

Pivoting and Melting DataFrames for Tidy Data

Applying Custom Functions to DataFrames

Working with Time Series Data in Pandas

Method Chaining for Clean and Efficient Code

Analyze Sales Data by Grouping and Aggregating with Pandas

Merge Customer Demographics with Transaction Data

Advanced Pandas Operations

Project: Exploratory Data Analysis of a Movie Ratings Dataset with Python

18 Data Visualization with Matplotlib and Seaborn

The Principles of Effective Data Visualization

Introduction to Matplotlib: Creating Your First Plot

Customizing Plots: Titles, Labels, Colors, and Styles

Creating Common Chart Types: Line, Bar, and Scatter Plots

Visualizing Distributions: Histograms and Box Plots

Introduction to Seaborn for Statistical Visualization

Creating Advanced Plots with Seaborn: Heatmaps and Pairplots

Building Subplots to Compare Visualizations

Create a Series of Visualizations to Explore a Housing Dataset

19 Python Stage Assessment

Project: Automated Sales Data Cleaning and Reporting Script in Python (CCS Python Certification)

Section Stage 4 – Visual Storytelling with Tableau

20 Getting Started with Tableau Desktop

Understanding the Tableau Product Suite

Navigating the Tableau Workspace: Data Pane, Shelves, and Cards

Connecting to Data: Live vs. Extract

Understanding Dimensions vs. Measures

Discrete vs. Continuous Fields and Their Impact

Building Your First Visualization with Show Me

Sorting, Filtering, and Grouping Data in the View

Connect to the Superstore Dataset and Build a Basic Sales Bar Chart

Tableau Interface and Core Concepts

21 Creating Impactful Visualizations and Calculations

Creating Bar Charts, Line Charts, and Pie Charts

Visualizing Geographic Data with Maps

Using Scatter Plots to Show Relationships

Visualizing Density with Heat Maps and Tree Maps

Introduction to Calculated Fields

Working with String, Date, and Logical Functions

Creating Table Calculations for Relative Analysis

Using Parameters for User-Driven Analysis

Build a Map Visualization of Sales by State

Create a Calculated Field for Profit Ratio and Visualize It

Tableau Visualizations and Calculations

22 Building Interactive Dashboards and Stories

The Principles of Effective Dashboard Design

Combining Multiple Worksheets into a Dashboard

Dashboard Layouts: Tiled vs. Floating

Adding Interactivity with Filters, Highlighters, and URL Actions

Using a Sheet as a Filter for Other Sheets

Designing for Different Devices (Desktop, Tablet, Mobile)

Introduction to Story Points for Narrative-Driven Analysis

Formatting Your Dashboard for a Professional Look and Feel

Combine Multiple Worksheets into a Cohesive Sales Dashboard

Project: Executive KPI Dashboard for a Global Retailer in Tableau

23 Tableau Stage Assessment

Project: Interactive Story-Driven Analysis of Global Superstore Data (CCS Tableau Certification)

Section Stage 5 – Business Intelligence with Power BI

24 Introduction to the Power BI Ecosystem

Understanding the Components: Power BI Desktop, Service, and Mobile

Navigating the Power BI Desktop Interface

The BI Workflow: Get Data, Model Data, Visualize Data, Share Insights

Connecting to Data Sources in Power BI

Import vs. DirectQuery vs. Live Connection

Exploring the Report, Data, and Model Views

Connect to and Load a Sales Dataset into Power BI Desktop

Power BI Fundamentals

25 Data Transformation with Power Query Editor

Launching and Navigating the Power Query Editor

Applying Basic Transformations: Removing Columns, Filtering Rows

Changing Data Types and Handling Errors

Splitting Columns and Merging Columns

Appending and Merging Queries from Multiple Sources

Grouping and Aggregating Data

Pivoting and Unpivoting Columns

Understanding the Importance of Query Folding

Clean and Transform a Multi-File Dataset using Power Query

26 Data Modeling and DAX Fundamentals

Introduction to Data Modeling in Power BI

Creating Relationships Between Tables

Understanding Cardinality and Cross-Filter Direction

Introduction to DAX (Data Analysis Expressions)

Creating Calculated Columns vs. Measures

Writing Your First DAX Measures (SUM, AVERAGE, COUNTROWS)

Introduction to the CALCULATE Function

Creating a Date Table for Time Intelligence

Build a Star Schema Data Model and Create Basic DAX Measures

Power BI Data Modeling and DAX Concepts

27 Creating Reports and Visualizations in Power BI

Building Your First Power BI Report Page

Working with Common Visuals: Bar, Column, Line, and Area Charts

Using Slicers for Interactive Filtering

Visualizing Data with Maps, Tables, and Matrices

Formatting Visuals for Clarity and Impact

Using Conditional Formatting in Tables and Matrices

Configuring Drill-Through and Cross-Filtering Interactions

Using Bookmarks to Create Report Navigation

Design a Multi-Page Sales Report with Interactive Visuals

Project: Human Resources Analytics Dashboard in Power BI

28 Power BI Stage Assessment

Project: End-to-End Business Intelligence Solution for a Manufacturing Firm (CCS Power BI Certification)

Section Bringing It All Together

29 A Day in the Life of a Data Analyst: The Full Workflow

Receiving a Business Request and Asking Clarifying Questions

Extracting Data from a Database using SQL

Performing In-Depth Cleaning and EDA with Python

Using Excel for Quick Ad-Hoc Analysis and Sanity Checks

Building an Interactive Dashboard in Tableau or Power BI

Synthesizing Findings and Preparing a Presentation

Communicating Insights to a Non-Technical Audience

Document the Full Workflow for a Given Business Problem

Section Building Your Professional Portfolio

30 What Makes a Great Data Analytics Portfolio?

Why a Portfolio is More Important Than Your Resume

Selecting Projects that Showcase a Range of Skills

The Art of Writing a Compelling Project README

Structuring Your Project: From Business Question to Conclusion

Using GitHub to Host Your Code and Portfolio

Presenting Your Dashboards on Tableau Public or Power BI Service

Creating a Personal Portfolio Website (Optional but Recommended)

Create a Professional README for One of Your Completed Projects

Section Career Launch & Job Preparation

31 Crafting a Data-Driven Resume and Professional Brand

Tailoring Your Resume for Data Analyst Roles

Using Action Verbs and Quantifiable Achievements

Optimizing Your LinkedIn Profile for Recruiters

Writing a Compelling Cover Letter

Networking Strategies for the Data Industry

Submit Your Resume for Peer and Instructor Review

32 Mastering Technical and Behavioral Interviews

Common SQL and Python Technical Interview Questions

Navigating Take-Home Case Studies and Assignments

Preparing for Live Technical Screens

Using the STAR Method for Behavioral Questions

How to Talk About Your Portfolio Projects

Asking Insightful Questions to Your Interviewers

Mock Interview Practice Session

Record a Video of Yourself Answering a Common Behavioral Question

33 Final Capstone Project (CCA Certification)

Capstone Project Kickoff: Choosing Your Dataset and Defining a Problem

Project Planning and Milestone Setting

Final Project Workshop and Q&A

Project: End-to-End Data Analytics Capstone Project (CCA Certification)

Material Includes

Downloadable Datasets for Practice

Real-World Case Studies

Python Jupyter Notebooks

SQL Practice Database Files

Tableau and Power BI Project Files

Step-by-Step Video Tutorials

Quizzes and Assignments

Course Completion Certificate

Requirements

No prior experience required — beginners welcome

Access to a computer with internet connection

Installation of Excel, Python, Tableau, and Power BI (guides included)

Commitment to practice regularly for best results

A curiosity for numbers and problem-solving mindset

This Course Is For

Aspiring Data Analysts or Data Scientists

Business professionals who want to make data-driven decisions

Students or graduates seeking a career in analytics

Marketing, finance, or operations professionals looking to upskill

Entrepreneurs and small business owners who want to leverage data

Anyone interested in learning data analytics from scratch

$79.99

From — 3 plan options

Real Projects You'll Build 2 Projects

Sales Dashboard

Create an interactive dashboard to visualize sales performance across global regions.

Excel Data Visualization

Customer Segmentation

Use machine learning to segment customers based on purchasing behavior and demographics.

Career Paths After This Programme 5 Roles

Data Analyst

$60,000 – $90,000 / year

Analyze structured data to identify trends, generate reports, and support business decision-making using tools like Excel, SQL, and visualization platforms.

Excel SQL Data Cleaning Data Visualization Critical Thinking

Business Intelligence Analyst

$70,000 – $100,000 / year

Transform data into dashboards and visual reports that help organizations track performance and make strategic decisions.

Power BI Tableau SQL Data Modeling Dashboard Design

Junior Data Scientist

$80,000 – $120,000 / year

Use Python and statistical methods to analyze data, build predictive models, and uncover deeper insights from complex datasets.

Python Pandas Machine Learning Statistics Data Analysis

Reporting Analyst

$55,000 – $85,000 / year

Prepare regular reports, track key metrics, and ensure data accuracy for business operations and performance monitoring.

Excel SQL Data Reporting Data Validation Attention to Detail

Data Analyst Intern / Entry-Level Analyst

$40,000 – $65,000 / year

Support data teams by cleaning data, running basic analysis, and assisting in report creation while gaining hands-on experience.

Excel SQL Basics Data Entry Data Cleaning Communication

Your Instructors

Lead Instructor

Justice Akorede

Lead Data Analytics Instructor

4.1 Rating

45 Reviews

3,526 Students

39 Courses

Justice is an experienced data analytics professional who has been teaching data skills since 2018, with expertise in Excel, SQL, Python, Power BI, and Tableau. He has also helped private companies build and implement data systems that improve reporting, streamline operations, and support data-driven decision-making, bringing real-world insight into his teaching.

Guest Instructors

Guest Instructor

Aviyah Akorede

Chief Distraction Officer & Junior Co-Instructor

Our youngest faculty member, Aviyah provides expert background vocals and surprise cameos across all live virtual cohorts and self-paced lessons. She specializes in spontaneous keyboard adjustments and morale-boosting distractions.

Student Success Stories 3 Stories

David had no prior coding experience but committed to learning Python and Power BI during the program. After completing his dashboard projects, he landed a role focused on business reporting and insights.

David O

Business Intelligence Analyst

Sarah transitioned from a retail manager role into data analytics within 4 months. She used her SQL and Excel project experience to confidently pass interviews and secure her first analyst position.

Sarah K.

Junior Data Analyst

Michael improved his Excel and SQL skills through the course and transitioned into a reporting role where he now manages company data and generates weekly insights.

Michael E.

Reporting Analyst

Student Reviews 20 Reviews

4.1

Course Rating

95%

Fanta Dembele

1 month ago

5.0

I love this course

Saliou Gueye

1 month ago

4.0