5 Months
Duration
Physical/Online
Classes
Weekday/Weekend
Tue & Thur (Saturdays)
10am - 1pm (WAT)
Time
Data Analytics Projects We Will Build
In addition to the Data Analytics projects below, you will also be given resources to build others on your own.
Analysis of store sales data with Excel
Learn how to help business to extract valuable insights from their data to aid decision making using Microsoft Excel.
Uber data analytics using Python
Create a time series to understand how business is performing overtime and draw a meaningful conclusion.
A top-notch dashboard design with PowerBI
Tell a story through visual that communicate key performance indicator (KPI) to difference audiences.
Solve business problem with SQL
Learn how to use SQL to help businesses find key information in their database
Hotel data analysis with Python
Identify key customers and what drives business growth and revenue with python
Data manipulation with Excel
Use various Excel functions to clean and transform data for analysis
** The projects can be changed anytime
Training Benefits
Here are some of the benefits of learning at Tech365
- Beginner-friendly
- Project-based training
- Excellent support
- Job alerts
- Career guidance
- Certificate of completion
- Flexible payment plan
- Conducive learning environment
- Unlimited internet access
- Remote internship with top organizations
Data Engineering Course Outline
Introduction to Data Engineering
Participants will learn what data engineering is and how to create pipeline, data lakes and other key concepts of data engineering
What is Data Engineering?
The role of a Data Engineer in the data lifecycle
Key tools and technologies used in Data Engineering
Overview of Big Data and its importance
- Data Governance and Security
- Importance of data quality and integrity
- Implementing data validation and error handling
- Data compliance and regulations (GDPR, CCPA)
- Data security best practices:
- Encryption
- Access control policies
Data Pipelines and Automation
Setting up batch and real-time pipelines
Workflow orchestration with Apache Airflow or Prefect
Monitoring and logging data pipelines
Data Modeling
Understanding OLTP vs. OLAP systems
Data modeling concepts: Star schema and Snowflake schema
Entity-Relationship (ER) modeling
Data normalization and denormalization
Data Storage Solutions
Relational databases (MySQL, PostgreSQL)
NoSQL databases (MongoDB, Cassandra, DynamoDB)
Data lakes and data warehouses
Introduction to platforms: Snowflake, BigQuery, Redshift, Databricks
Data Integration and APIs
Integrating multiple data sources
Working with APIs for data extraction
REST and GraphQL
Introduction to real-time data ingestion tools
Kafka, Kinesis, and RabbitMQ
ETL (Extract, Transform, Load) Processes
Designing and implementing ETL pipelines
Popular ETL tools: Apache NiFi, Apache Airflow, Informatica
Hands-on with Python for custom ETL scripts
Best practices for ETL processes
Big Data Frameworks
Introduction to Big Data and its challenges
Hadoop Ecosystem:
HDFS (Hadoop Distributed File System)
MapReduce
Apache Spark:
Core concepts: RDDs, DataFrames, and Datasets
Spark SQL and PySpark for data processing
SQL For Database Manipulation
Learn how to clean, query, manipulate, and aggregate data using SQL.
Introduction to SQL
- What is SQL?
- Create Table Statement
- ALTER TABLE Statements
- Rename Columns of a Table
- Modify Column DataType
- Drop Columns from Table
- Rename Tables
- Drop Tables
- What is Null Value?
DML Statements
- Insert Statement
- Update Statement
- Delete Statement
- Truncate Statement
- Merge Statement
SQL Functions
- AVG()
- COUNT()
- MAX()
- MIN()
- SUM()
Select Queries
- Select Query
- WHERE Clause
- GROUP BY Clause
- HAVING Clause
- ORDER BY Clause
SQL Joins
- Inner Join
- Left Join
- Right Join
SQL Operators
- BETWEEN
- IN
- LIKE
- INTERSECT
- MINUS
- UNION
- DISTINCT
- ANY
- ALL
Project: Use the knowledge gained to analyze data for a business.
Power BI For Business Intelligence
Understand how to create reports and dashboards to communicate your findings to stakeholders.
Data Visualization and report
- Introduction
- Exploring the “Report”
- Adding Simple Objects to the Power BI Report Canvas
- Inserting Basic Charts & Visuals in Power BI
- Conditional Formatting
- Report Formatting Options
- Report Filtering Options
- Exploring Data with Matrix Visuals
- Filtering with Date Slicers
- Inserting Text Cards
- Visualizing Geospatial Data with Maps
- Visualizing Data with Treemaps
- Artificial Intelligence in PowerBI
Project: Designing of sales dashboard
Python Programming
We will focus on programming paradigms using Python. With this foundation, participants will be able to understand how to write code to solve problems and perform automation tasks using Python.
- Overview of python
- Installation and setup
- Python Syntax
- Variables
- Reserved words
- Input and output
- Comments in python
Data Types
- Strings
- Lists
- Dictionary
- Tuple
- Sets
- Numbers
- Boolean
- None
Operators
- Arithmetic operators
- Comparison operators
- Logical operators
- Assignment operator
- Membership operator
- Identity operator
Control Flow
- If statement
- elif
Loops
- While loops
- For loops
- For in Loops
Functions
- In-built functions
- User-defined function
- Function parameter
- Lambda
- Map, filler, reduce
- *args and **kwargs
Modules
- Modular programming
- Python package
File Handling
- Reading, Writing and appending files
- Handling HTML and CSV Data
- Manipulating Data with Python
NumPy For Array Manipulation
Understand how to work with multi-dimensional arrays and integrate numpy with other libraries like Pandas and Matplotlib
Introduction to Numpy
- What is NumPy (Numerical Python)?
- Understanding dimensions
Numpy Basics
- Array Creation
- Array attributes
Random number generation
- Random sampling
- Random seeds
Array operation
- Indexing numpy array
- Slicing in NumPy
- Arithmetic operations
- Broadcasting
- Mathematical Functions in Numpy
- Sorting in numpy
- Using Axis
Pandas For Data Analysis
hands-on data analytics using Pandas. We will work on different data to understand how to clean, analyze and draw conclusions.
Introduction to Pandas
- Importing pandas libary
- Creating a Pandas Series
- Creating a Pandas DataFrame
Pandas Methods
- Describe and info methods
- Head and tail method
- Apply method
- Unique and nunique method
- Sorting values
- Counting values
- Replacing values
- Deleting values
- Largest and lowest values
Manipulating data
- Dealing with Rows and Columns in Pandas
- Extracting rows
- Indexing and Selecting Data
- Conditional filtering
- Handling missing data
Grouping and merging data
- Grouping data
- Merging data
- Joining data
- Concatenating data
- Working with date and time
- Working with text data
Reading files
- Read CSV files
- Reading excel file
- Reading HTML tables from websites
Project: Applying the knowledge gained to analyze hotel data and Uber.
Matplotlib For Visualization
Plot data on charge, style and customize the chart with matplotlib.
Introduction
- Overview of Matplotlib
- Importing matplotlib
- Plotting your first graph
Adjusting graph
- Figure Class
- Axes Class
- Setting Limits and Tick labels
- Multiple Plots
- Adding a Legend?
Creating Different Types of Plots
- Line Graph
- Bar chart
- Histograms
- Scatter Plot
- Pie Chart
- Customizing Plots
Task: Grade student results and plot a graph to visualize it.
Seaborn For Visualization
Use different types of charts to perform univariate and multivariate analyses.
Introduction
- Importing seaborn
- Using Seaborn with Matplotlib
- Plotting graph using seaborn
Customizing Seaborn Plots
- Changing Figure Aesthetic
- Changing the figure Size
- Scaling the plots
- Color Palette
- Multiple plots with Seaborn
Creating Different Types of Plots
Relational Plots
- Scatter Plot
- Line Plot
Categorical Plots
- Bar Plot
- Count Plot
- Box Plot
Distribution Plots
- Histogram
- Distplot
- Pairplot
- Rugplot
Matrix Plots
- Heatmap
Key takeaway: Understand how to create and style plots using Seaborn
Github and Data Analytics
Portfolio building and collaboration
- What is GitHub?
- How to set up a GitHub account
- Uploading your project to the GitHub repository
- Downloading and forking code from GitHub
- Documenting your work
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AI and Data Analytics
The role of AI in data analytics
- Why AI is the future
- How to solve data analytics tasks by leveraging AI
- How to stay relevant despite the advancement in AI
- Career tips and positioning for job opportunities
Student Feedback
Training Fees
Data Engineering
Covers all the Data engineering modules 6 months (weekends)
N1,500,000
$1,500
Frequently Asked Questions (FAQs)
As more companies are embracing artificial intelligence, the need for data analyst who will clean and process the data is on the increase. The demand for data analysts is growing rapidly across industries like finance, healthcare, e-commerce, marketing, and more as companies are increasingly relying on data-driven insights to make strategic decisions.
No prior programming experience is required. Our training approach is beginner friendly, and we work on several real world projects to help our student have deeper understanding of each module. However, you need to know how to operate the computer efficiently.
We have weekend class for those who cannot attend the weekday class. 10am to 1pm (Nigerian Time)
You can pay N200,000 or N300,000 at the beginning of each month to attend data analytics and science respectively. N500,000 monthly for artificial intelligence.
Yes, it is recommended you come with your own laptop as it will help you to practice whatever you are learning. However, we provide laptop that can be used only within our premises if need be.
Depending on your budget, Core i7, 16 GB ram with SSD is best.
However, core i3 or i5 with 4gb or 8gb ram HDD will also work fine.
It can be HP, DELL, Mac or any other brand.
Yes. In fact, this is our strength. We are committed to supporting our students to ensure they succeed. You will be added to our WhatsApp group to connect with others and also ask questions.
We don’t guarantee job after the training. However, some of our students are connected to remote internships with companies like Accenture, PwC, KPMG etc. We share job alerts from time to time on available vacancies and tips that can help our students get job faster. We also offer CV review, LinkedIn profile optimization and letter of recommendation to diligent students.
All our training are physical at our Ikeja office. However, those who are outside Nigeria or living far can join online. Who join our classes from Canada, The UK, USA, UAE, Finland etc.
Yes. Tech365 was established to bridge the practical knowledge that people are lacking to secure a job. Hence, we focus on making the class beginner friendly with lots of projects to help the participants master the skill.
We issue a certificate of completion after the training. If you are interested in certification, you can pay to take certification exam by international bodies online.
You can chat with our representation using the WhatsApp chat button on our website or call the phone number at the top or bottom of this page. You can also click the register button on this page to show your interest and one of our team will reach out to you.
We don’t compete based on price. Our fees is based on the value we offer. If you want a place that will give you in-depth knowledge and value for your money, you are in the right place. Our past students are glad to learn from us as our training is hands-on and designed to make you job-ready. You can check what over 80 of our students had to say about our training on our Google review page.
Our Students Work at
