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Data Analysis

Master Data Science with our comprehensive training course! Learn from industry experts, enhance your coding skills, and build robust applications. Perfect for beginners and experienced developers aiming to excel.

  • Duration: 3 months
  • Delivery Type: Delivery Type: Online/2hrs/3 classes a week
  • Weekdays: To be communicated
  • Time: 9am - 11am (WAT) or 1am - 3am
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PRACTICAL AND JOB-READY DATA ANALYSIS MODULES

Modules Covered

Introduction To Data Analysis/Analytics

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Excel For Data Analysis

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Python For Data Analysis

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SQL For Data Analysis

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Pandas For Data Analysis

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Manipulating Arrays With Numpy

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Power BI For Business Intelligence

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Data Visualization With Matplotlib/Seaborn

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Introduction to Data Analysis

Introduction To data analysis

  • Definition and Importance of Data Analytics
  • Types of Data Analytics
  • Data Analytics Lifecycle
  • Phases of Data Analytics
  • Data Visualization, and Interpretation
  • Key Roles in Data Analytics
  • Tools and Technologies in Data Analytics

Data Collection and Data Sources

  • Data Collection Methods
  • Types of Data
  • Data Formats
  • Data Sources
  • Introduction to APIs for Data Retrieval
  • Using Web Services and Public Data Sets

Data Cleaning and Preparation

  • Importance of Data Cleaning
  • Understanding Data Quality
  • Common Data Issues
  • Data Cleaning Techniques
  • Data Preparation
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Excel For Data Analysis

Introduction to Excel

  • Understanding Workbooks and Worksheets
  • Understanding Cells, Rows, Columns, and Ranges
  • Basic Formulas and Functions
  • Formatting Data
  • Printing and Page Setup

Intermediate Excel

  • Advanced Formulas and Functions
  • Logical Functions (IF, AND, OR, NOT)
  • Lookup and Reference Functions(VLOOKUP, HLOOKUP, MATCH, INDEX)
  • Text Functions (LEFT, RIGHT, MID, CONCATENATE)
  • Date and Time Functions

Data Validation and Protection

  • Data Validation Rules
  • Protecting Worksheets and Workbooks
  • Data Entry Forms

Working with Tables

  • Creating and Formatting Tables
  • Sorting and Filtering Data
  • Table References and Structured Data

Charts and Graphs*

  • Creating Different Types of Charts (Bar, Line, Pie, etc.)
  • Customizing Chart Elements
  • Using Sparklines
  • Creating Combo Charts

Advanced Excel

  • PivotTables and PivotCharts
  • Slicers and Timelines
  • Calculated Fields and Items
  • Dynamic Array Functions
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Python For Data Analysis

Introduction to Python for Data Science

  • Importance of Python in data science
  • Overview of Python's role and applications in data science
  • Installing Python
  • Setting up Jupyter Notebook
  • Introduction to Anaconda

Python Basics

  • Variables and data types
  • Basic operators
  • Conditional statements (if, elif, else)
  • Loops (for, while)

Functions and Modules

  • Defining and calling functions
  • Function arguments and return values
  • Importing and using standard libraries

Python Data Structures

  • Tuples and Sets
  • Creating and using tuples
  • Set operations

Dictionary

  • Dictionary
  • Creating and manipulating dictionaries
  • Creating and manipulating dictionaries
  • Dictionary comprehensions

File Handling

  • Reading and Writing Files
  • Working with text files
  • Reading and writing CSV files
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Databases

Overview of Databases

  • What is a database?
  • Types of databases
  • Introduction to relational databases
  • Database Management Systems (DBMS)

SQL Basics

  • What is SQL?
  • History of SQL
  • SQL standards and dialects
  • Overview of SQL syntax and commands

SQL Query

  • Data Retrieval
  • Basic SELECT statements
  • Filtering data with WHERE clause
  • Sorting data with ORDER BY clause

Data Filtering

  • Using logical operators (AND, OR, NOT)
  • Pattern matching with LIKE
  • Range filtering with BETWEEN
  • NULL values handling

Advanced SQL Queries

  • Joining Tables
  • Understanding joins (INNER, LEFT, RIGHT, FULL)
  • Cross joins and self joins
  • Using aliases for readability
  • Introduction to subqueries
  • Correlated vs. non-correlated subqueries
  • Using subqueries in SELECT, FROM, WHERE, and HAVING clauses

Data Manipulation

  • INSERT INTO statements
  • Bulk insertions
  • Inserting data from queries
  • UPDATE statements
  • Conditional updates
  • Updating multiple tables
  • DELETE statements
  • TRUNCATE statements
  • Handling referential integrity
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Pandas For Data Analysis

Introduction to Pandas

  • What is Pandas?
  • Importance in data analysis
  • Installation and setup
  • Importing Pandas
  • Series and DataFrames: The core data structures
  • Creating Series and DataFrames
  • Basic attributes and methods

Data Manipulation with Pandas

  • Indexing and Selecting Data
  • Selecting data by label and position
  • Boolean indexing
  • Setting and resetting the index
  • Handling missing data
  • Removing duplicates
  • Data type conversions
  • Applying functions
  • Using apply(), map(), applymap()
  • Grouping data with groupby()

Working with DataFrames

  • Merging and Joining DataFrames
  • Concatenation
  • Merging and joining
  • Reshaping DataFrames
  • Pivot tables
  • Melt and stack/unstack functions
  • Time Series Analysis
  • Working with dates and times
  • Time-based indexing and resampling
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Manipulating Arrays With Numpy

Introduction to Numpy

  • What is NumPy?
  • Overview of NumPy
  • Importance of NumPy in data science
  • Installation and setup

Array Creation and Manipulation

  • Creating Arrays
  • Creating arrays from lists
  • Using arange, linspace, and random module
  • Creating multidimensional arrays
  • Array Indexing and Slicing
  • Basic indexing and slicing

NumPy for Data Analysis

  • Working with Data
  • Loading data with NumPy
  • Saving and exporting data
  • Handling Missing Data
  • Identifying missing data
  • Filling and dropping missing values
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Power BI For Business Intelligence

Introduction to Power BI

  • Overview of Power BI
  • Importance of Business Intelligence
  • Power BI vs. Other BI Tools
  • Installing Power BI Desktop
  • Power BI Service
  • Power BI Mobile Apps

Data Sources and Transformation

  • Connecting to Data Sources
  • Data Transformation with Power Query
  • Data Modeling in Power BI

Creating Reports and Visualizations

  • Designing Interactive Reports
  • Creating Custom Visuals
  • Using Power BI Templates

Power BI and DAX (Data Analysis Expressions)

  • Introduction to DAX
  • Basic DAX Functions
  • Advanced DAX Functions

Publishing and Sharing

  • Publishing Reports to Power BI Service
  • Sharing Dashboards and Reports
  • Collaborating with Teams

Power BI Administration

  • Managing Power BI Workspaces
  • Security and Permissions
  • Monitoring Usage and Performance
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Data Visualization With Matplotlib/Seaborn

Introduction to Data Visualization

  • Importance of Data Visualization in Analytics
  • Overview of Popular Visualization Libraries

Getting Started with Matplotlib

  • Installing Matplotlib
  • Basic Plotting with Matplotlib
  • Customizing Plots (Titles, Labels, and Legends)
  • Working with Multiple Figures and Axes
  • Saving and Exporting Plots

Advanced Matplotlib Techniques

  • Creating Subplots
  • Plotting Different Types of Data (Bar, Scatter, Histogram, etc.)
  • Styling Plots (Colors, Lines, and Markers)
  • Annotations and Text on Plots
  • 3D Plotting with Matplotlib

Introduction to Seaborn

  • Installing Seaborn
  • Seaborn vs. Matplotlib: Key Differences
  • Basic Seaborn Plots
  • Customizing Seaborn Plots

Advanced Seaborn Techniques

  • Creating Complex Visualizations with Seaborn
  • Plotting with Categorical Data
  • Using Seaborn with Pandas DataFrames
  • Pair Plots and Joint Plots
  • Heatmaps and Cluster Maps

Integrating Matplotlib and Seaborn in Data Analytics

  • Case Studies and Practical Examples
  • Project: Visualizing a Real-world Dataset
  • Best Practices for Effective Data Visualization
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Course Fees

Data Analysis

Cover every aspect of data analysis modules

N350, 000

400USD

3 Months

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Data Science

Data analysis modules + Machine learning + Deployment

N500, 000

550USD

4 Months

Get Started

Artificial Intelligence

Data Analysis modules + Data Science modules + Articial Intelligence

N1000, 000

1,200USD

5 Months

Get Started

Frequently Asked Question

Q1. Why should I take your training?

Q2. How can I start?

Q3.Is the training practical and suitable for beginners?

Q4. Can I participate in the training programs remotely?

Q5. Is it necessary to bring my own laptop for the training?

Q6. Can working-class people take the training?

Q7. Is there installmental payment?

Q8. Will I receive a certificate after the training?

Q9. Do you provide support after the training?

Q10. Can I expect immediate job placement upon completing the training?