Data Analysis For Data Science

Course Overview

In this course, you will learn how to use Business intelligence (BI) software and services to transform data into actionable insights that inform an organization’s business decisions.

Data analysis is the process of extracting information from data. … Data analysis can involve data mining, descriptive and predictive analysis, statistical analysis, business analytics and big data analytics.

Courses Included are:

Microsoft Excel

Microsoft Power BI

Tableau & SQL

Python for Data Science  

R Programming

Microsoft Excel

Chapter 01 – Working with Functions
Chapter 02 – Working with Lists
Chapter 03 – Analyzing Data
Chapter 04 – Visualizing Data with Charts
Chapter 05 – Using PivotTables and PivotCharts
Chapter 06 – Working with Graphical Objects
Chapter 07 – Using Array Formulas

Chapter 01 – Working with Multiple Worksheets and Workbooks
Chapter 02 – Sharing and Protecting Workbooks
Chapter 03 – Automating Workbook Functionality
Chapter 04 – Using Lookup Functions and Formula Auditing
Chapter 05 – Forecasting Data
Chapter 06 – Creating Sparklines and Mapping Data
Chapter 07 – Importing and Exporting Data
Chapter 08 – Internationalizing Workbooks
Chapter 09 – Working with Power Pivot
Chapter 10 – Advanced Customization Options
Chapter 11 – Working with Forms and Controls

Microsoft Power BI

This module explores the landscape of the Power BI portfolio and describes several use cases for Power BI. The course then identifies and describes the role and responsibilities of a Data Analysts.

The Power BI Portfolio
Identifying Tasks of the Data Analyst
Lab : Getting Started
Getting Started

This module explores identifying and connecting to different data sources. The student will also learn the basics on how to identify and optimize query performance issues. They will also learn how to perform proper data profiling in preparation for the subsequent step of cleaning and shaping the data prior to loading the data.
Data Sources
Storage Modes
Query Performance
Data Profiling
Lab : Preparing Data in Power BI Desktop
Prepare Data

This module teaches the fundamental concepts of designing a data model for proper performance and scalability. It instills in the student a list of items to think about prior to building the model.
Combining Queries
Data cleaning and transformation
Advanced capabilities
Configuring data loading and resolving errors
Lab : Loading Data in Power BI Desktop
Loading Data

This module teaches the fundamental concepts of designing a data model for proper performance and scalability. It instills in the student a list of items to think about prior to building the model.
Data modeling basics
Measures and Dimensions
Model Performance
Lab : Data Modeling in Power BI Desktop
Create Model Relationships
Configure Tables
Review the model interface
Create Quick Measures

In this module the student will apply the steps learned in the previous module and build a data model while learning and implementing additional items to create the foundation of the model. The student will be introduced to initial security concepts and the Q&A feature in this module.
Common data modeling techniques
Adding columns to support the data model
Row-level security
Q&A considerations
Lab : Advanced Data Modeling
Create a man-to-many relationship
Enforce row-level security

This module first introduces the student to DAX and some of the critical functions and operators necessary to enhance a data model, including the concepts of Measures, and calculated columns and tables, and Time Intelligence.
Introduction to DAX
Creating tables and columns
The CALCULATE expression
Time-Intelligence functions
Lab : Using DAX in Power BI – Part 1
Create Calculated tables
Create Measures
Lab : Using DAX in Power BI – Part 2
Work with Filter content
Work with Time-Intelligence
Publish the Power BI Desktop file

In this module the student is introduced to steps, processes, and concepts necessary to optimize a data model for enterprise-level performance.
Fine-tune the data model
Identifying performance issues

This module introduces the student to the fundamental concepts and principles of building a report, including selecting the correct visuals, designing a page layout, and applying basic but critical functionality including slicing and filtering. This important topic of designing for accessibility is also covered.
Selecting a visualization
Configuring visualizations
Formatting pages
Enhancing the report
Lab : Designing a report in Power BI Desktop – Part 1
Create a report
Sign in to the Power BI Service

This module helps the student think beyond the basics of report building and discusses topics for enhancing the report for usability and performance. The student will leave this module with knowing that a report is not something to just look at, but is a living canvas that tells a story, and should be designed as such.
Bookmarks and navigation
Designing cohesive pages and interactions
Improving reports
Lab : Designing a report in Power BI Desktop – Part 2
Configure Sync Slicers
Configure Drill-through
Add Conditional Formatting
Add Bookmarks and Buttons
Explore the Report

In this module the student learns about dashboards and the many features and functionality they contain. The student learns how to take the report they built in the previous module and pin it to a dashboard, then enhance to dashboard for additional usability and insights.
Dashboard design
Real-time dashboards
Dashboard enhancements
Lab : Creating a Power BI Dashboard
Create a Dashboard
Refresh the dataset
Review the dashboard

This module helps the student apply additional features to enhance the report for analytical insights in the data, equipping the student with the steps to use the report for actual data analysis. This module will also arm the student with additional steps and concepts to apply and perform advanced analytics on the report for even deeper and meaningful data insights.
Basic analysis
Grouping, binning, and clustering
Analysis over time
Advanced analysis
Lab : Data Analysis in Power BI Desktop
Create a report
Create a Scatter chart
Create a Forecast
Work with a Decomposition Tree
Work with Key Influencers

In this module the student will learn the concepts of managing Power BI assets, including datasets and workspaces, as well as how to apply role-level security to a dataset. This module teaches the student how to create and manage workspaces, as well as how to share content, including reports and dashboard, and how to distribute an App.
Dataset management
Enhancing datasets
Configure row-level security
Create and Manage workspaces
Enhancing datasets and reports in the workspace
Sharing and distributing content
Lab : Publishing and Sharing Power BI Content
Configure dataset security
Share a Dashboard
Publish an APP

This module will teach the student about paginated reports. The student will learn what they are how they fit into the Power BI spectrum, and then look at how to build and publish a report.
Introduction to Paginated Reports
Data sources and datasets
Adding visual elements
Enhancing and publishing reports
Lab : Creating a Paginated report
Getting Started
Develop the report


The course is divided into 4 weeks. Over these 4 weeks, you will learn how to use Tableau to implement all types of visualizations and to help you find, and communicate, answers to business questions, as well as work with the Tableau functions that all data analysts should be familiar with. You will also learn how to use a different data set to work out analyses and how to use functions to include new calculations to your data.

1. Introduction

2. Get started (Tableau Family of Products, Installation)

3. Connecting to Web Services

4. Connecting data

5. Preparing data and data types

6. Combining Data in Tableau

1. Filter data

2. Sorting, Grouping, Hierarchies and Sets data

3. Line Graphs and Box Plots

4. Mapping

5. Working With Dates

1. Parameters

2. Filtering for Top and Top N

3. Row-level Calculations

4. Blending and Aggregation-level Calculations

5. Table Calculations and Parameters

1. Combination charts

2. Dynamical visualization

3. Dashboards and Story Points

4. Creating and Formatting Story Points

5. Saving and Publishing to the Web

6. Data Science with Tableau


  • Introduction to SQL
  • Basic SQL
  • SQL Joins
  • SQL Aggregations
  • Advanced SQL
  • Queries

Python for Data Science

History of Computers
Understanding Hardware
Writing First Program (“Hello World”)
Variables & Data Types
Strings, Integers, Integers, Floats, Boolean, etc.
Assigning Variables

Define motivation behind control flow
If, If-Else, Elif, Switch Statements
Complex Data Types
Initializing Lists
Printing Lists
List functions such as length, append, pop, etc.
Introduction to Dictionaries & their structures
Define the motivation behind using a loop
For, While, Do-While, For Each loops
Error Handling
Course Syllabus | Python for Data Science Bootcamp 1
Identify when to use a function
Syntax & Implementation
Arguments & Return values.

Data Sources
Storage Modes
Query Performance
Data Profiling
Lab : Preparing Data in Power BI Desktop
Prepare Data

Introduction to O.O.P paradigm
Introduction to Objects, Classes, Instances
Inheritance, Abstraction, and Sets

File Input
User Input
List Comprehension

Introduction to Data Science
Review Python Fundamentals
Understanding the data science discipline
Data set reading
Filtering, Cleaning, Manipulating Data
Excel vs Python
Data Visualization
Matplotlib Package
Understanding motivations between different graphs
Machine Learning
Sci-Kit Learn package
Understand motivation and definition of machine learning
Statistical simulation
Data Visualizations
Shell Workshop
Purpose &
Create a Git Repo
Review a Repo’s
Add commits to a
Tagging, Branching,
and Merging
Undoing Changes
Working with
Working on Another
Staying in Sync with
a Remote Repository

Data Visualization using R Programming

  • Introduction to R Programming
  • History and overview of R
  • Install and configuration of R programming environment
  • Basic language elements and data structures
  • R+Knitr+Markdown+GitHub
  • Data input/output
  • Data storage formats
  • Subsetting objects
  • Vectorization
  • Control structures
  • Functions
  • Scoping Rules
  • Loop functions
  • Graphics and visualization
  • Grammar of data manipulation (dplyr and related tools)

₦ 270,000

  • Days: Weekdays & Weekends
  • Time: Morning & Evening
  • Start Date: 30th September, 2023
  • Duration: 24 weeks
The training was detailed and very easy to comprehend. Our Instructor was apt. He took time to explain and answered all our questions. I am really impressed with the teachings. I will not hesitate to recommend your training center to friends.
Christian Onyezewe
Flexible class … Interactive class Understand and easygoing teacher Well informed course outline and recording.
Louisa Tare Louis
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