Training Details
16 weeks
10am to 1pm (Morning)
Weekdays (Mon, Wed, Fri) Weekends (Sat)
1st Floor, Nikky Africana Plaza, 70c Allen Avenue, Obasa Close, Opp UBA, Ikeja, Lagos
N500,000
2348029704730
Data Science Training In Lagos Nigeria
Our data science training is beginner-friendly, practical and project-based. It is designed to help participants gain mastery of different technologies and tools used to make predictions and classifications using machine learning. It is tailored to help you gain real-world experience that can get you a job and grow your career as a data scientist.
Who should attend
- Those who are switching career to tech industry.
- Those planning to travel abroad and seeking hands-on skill that can make them relevant.
- Those in managerial position seeking to have knowledge of how data is cleaned, analyzed and visualization.
- Those who studied computer science, mathematics, statistics and other related courses seeking to acquire skill with real-world project to be able to get a job and be relevant to organizations.
Technologies covered
Learning outcomes
Real-World Project
Work on different data such as sales data, customer data, student record, covid-19, customer data, olympic and more
Data science role
Ability to work as a data scientist, data analyst, and engage in data cleaning, prediction, classification and visualization
Course Curriculum
Introduction to data Science
Introduction and onboarding
- Introduction to data science
- Data analytics vs data science
- Application of data science in real-world
- What is data?
- Why data is important
- Python for data science
- Understanding data processing
- Installation of Jupyter notebook
Introduction to Statistics
- Measures of Central Tendency
- Measure of variability
- Statistics with Python
- Measuring Variance
- Normal Distribution
- Binomial Distribution
- Poisson Discrete Distribution
- Bernoulli Distribution
- P-value
- Correlation
Python Programming
Introduction to python
- What is Python?
- Installing Python
- Setting up VSCode for Python
- Python fundamentals
- Python Syntax
- Variables
- Input and output
- comments
Data types
- Strings
- String properties
- Indexing and slicing
- String methods
- Numbers
- Boolean
Python lists
- Introduction to Lists
- Sorting a List
- Slicing a List:
- Unpacking a List
- Iterating over a List
- Finding Index of an Element: index()
Tuple
- Indexing tuple elements
- Unpacking Tuples
- Type Conversion
Python dictionary
- Introduction to dictionary
- Nested dictionary
- Dictionary methods
- Indexing dictionary items
- Updating dictionary items
- Dictionary Comprehension
Python Set
- Set Comprehension
- Union of Sets
- Intersection of Sets
- Difference between Sets
- Symmetric Difference of Sets
- Subset
- Superset
- Disjoint Sets
Operators
- Arithmetic Operators
- Assignment operators
- Comparison Operators
- Membership
- Control flow
- if…elif
- type Conversion
Loops
- for Loop
- while Loop
- For in loop
Python Functions
- Inbuilt function
- User-defined function
- Default Parameters
- Keyword Arguments
- Lambda Expressions
- Function Docstrings
- The *args Parameters
- The **kwargs Parameters
- Transform List Elements: map()
- Filtering List Elements: filter()
- Reducing List Elements: reduce()
- List Comprehensions
Working with Files
- Reading a Text File
- Writing to a Text File
- Creating a Text File
Modules
- Benefits of using modules
- Python packages
- Using the OS module
Personal tasks: Create a function that can process data
Key takeaway: Understand python programming and be able to translate concepts into pseudo-code and program which can be applied to analyze data
Numpy for array manipulation
Introduction
- What is Numpy?
- Array Creation
- Indexing numpy array
- Slicing in NumPy
- Data Type Objects
- Iterating Over Array
- Mathematical Function
- String Operations
- Sorting, Searching and Counting
Random sampling
- randint() function
- random_sample()
- function
- ranf() function
Statistical Function
- Minimum and maximum value
- Percentile
- Median
- Standard deviation
- Variance
- Average
Key takeaway: Understand how to work with multi-dimensional array and integrate numpy with other libraries like Pandas and Matplotlib
Pandas for data analytics
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: Analyzing covid19 data
Key takeaway: Ability to clean and analyze different types of data
Matplotlib for visualization
Introduction
- Overview of Matplotlib
- Importing matplotlib
- Plotting your first graph
Adjusting graph
- Figure Class
- Axes Class
- Setting Limits and Tick labels
- Multiple Plots
- What is a Legend?
Creating Different Types of Plots
- Line Graph
- Bar chart
- Histograms
- Scatter Plot
- Pie Chart
- 3D Plots
- Customizing Plots
Project: Grade student results and plot a graph to visualize it.
Takeaway: Being able to plot and style different types of graph
Seaborn for visualization
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
Regression Plots
- lmplot
- Regplot
Matrix Plots
- Heatmap
Key takeaway: Understand how to create and style plots using seaborn
Machine Learning
Introduction
- Getting Started with Machine Learning
- An Introduction to Machine Learning
- What is Machine Learning?
- Introduction to Data in Machine Learning
- Demystifying Machine Learning
- Applications of Machine Learning
- Best Python libraries for Machine Learning
- Difference between Machine learning and Artificial Intelligence
Data and Its Processing:
- Introduction to Data in Machine Learning
- Understanding Data Processing
- Data Cleaning
- Label Encoding of datasets
- One Hot Encoding of datasets
Supervised learning :
- Getting started with Classification
- Basic Concept of Classification
- Types of Regression Techniques
- Classification vs Regression
- Types of Learning – Supervised Learning
- Gradient Descent
Linear Regression
- Introduction to Linear Regression
- Application of Linear Regression using sklearn
Logistic Regression
- Understanding Logistic Regression
- Why Logistic Regression in Classification?
- Logistic Regression using Python
- Naive Bayes Classifiers
Support Vector
- Support Vector Machines(SVMs) in Python
- Using SVM to perform classification
Decision tree algorithm
- Decision Tree Introduction with example
- Decision Tree Regression using sklearn
Random Forest algorithm
- Random Forest Regression
- Application of Random Forest classifier
Unsupervised learning
- Supervised vs Unsupervised learning
- Clustering in Machine Learning
- Different Types of Clustering Algorithms
- K means Clustering – Introduction
- Elbow Method for optimal value of k in KMeans
- K-means++ Algorithm
- Analysis of test data using K-Means Clustering
Key takeaway: Understanding of how machine learning is used to make predictions and classifications.
Bonuses
Excel for data analysis
- Introduction
- Reading CSV, tabbed and fixed column data
- Data manipulation
Formula basics
- What is a cell reference?
- What is a formula?
- What is a function?
- How to enter a formula with cell references
- How to use addition in a formula
- How to use subtraction in a formula
- How to use multiplication in a formula
- How to use division in a formula
- How to use exponents in a formula
- The order of operations
Formatting
- How to use number formatting in Excel
- How to use currency formatting in Excel
- How to use percentage formatting in Excel
- How to use fraction formatting in Excel
Excel chart
- How to create a basic chart
- How to move and resize a chart in Excel
- How to create a standalone chart
- How to edit and add to chart data
- How to add a title and legend to a chart
Working with data and lists
- How to freeze rows
- How to quickly sort using one column in Excel
- How to sort using more than one column
- How to filter a list
- How to filter with multiple criteria
References
- Relative references
- Absolute reference
- Creating a reference to another sheet
Conditional formatting in Excel
- Using the if function
- How to create a formula with nested IFs
Working with Text
- How to join values with an ampersand
- How to join cell values with concatenating
- How to clean text with trim
- How to count characters
- Changing case in Excel
- How to extract text
Dates and time
- How to work with date in excel
- Formatting date
- Calculating the number of days
- Calculating years and months
Statistics
- How to use the COUNT function
- Using the COUNTIF function
- Using the SUM function
- How to use SUMIF
- How to use the AVERAGE function
- How to calculate the minimum and maximum value
- How to rank value with the RANK function
Lookups
- How to use VLOOKUP
- How to use VLOOKUP for approximate matches
- VLOOKUP vs Nested IFs
- Using the index function
- Using the match function
- How to use HLOOKUP
Pivot table
- What is a pivot table
- How to create a pivot table
- How to add fields to pivot tables
- How to rearrange fields in the pivot table
- How to adjust aggregate function in pivot table
- Styling pivot table
- How to add slicers
- How to filter pivot table
- How to sort pivot table
- How to group pivot table
- How to create a pivot chart
- How to change chart type
- How to work with pivot chart options
- How to filter pivot chart
- Pivot chart
Task: Analyzing sales data with excel
Visualizing the data for better
communication with stakeholders using pivot tables and pivot chart
SQL for database manipulation
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, SOME
- ALL
SQL Operators
- BETWEEN
- IN
- LIKE
- INTERSECT
- MINUS
- UNION
- DISTINCT
Project: Design a database for a social network
Personal task: Design a database for a school management system
Key takeaway: Ability to design and create a database
Implement the database design
Perform CRUD operation
Power BI for Business Intelligence
Introduction to PowerBI
- What is PowerBI?
- Installing Power BI
- Desktop
- Adjusting Settings in
- PowerBI
Connecting and shaping data
- Types of Data Connectors
- The Power BI Query Editor (Power Query)
- Basic Table Transformations
- Working with Numerical Values
- Working with Date & Time Tools
- Generating Index & Conditional Columns
- Grouping & Aggregating Records
- Merging Queries in
- Power BI Desktop
- Appending Queries
- Configuring Data Source Settings
- Defining Hierarchies
- Power BI Data Connection Best Practices
Table relationship and data model
- What is a “Data Model”?
- Principles of Database Normalization
- Understanding Data Tables vs. Lookup Tables
- Understanding Table
- Relationships vs. Merged Tables
- Creating Table Relationships
- Managing & Editing Table Relationships
- Connecting Multiple Data Tables
- Hiding Fields from the
- Power BI Report View
- Power BI Data Model Best Practices
Data Analysis Expressions (DAX)
- Introduction
- DAX Calculated Columns
- DAX Measures
- Adding Columns & DAX Measures
- Implicit vs. Explicit DAX Measures
- Filter Context Examples in Power BI
- Understanding DAX Syntax & Operators
- Common DAX Function
- Date & Time Functions
- Conditional & Logical Functions (IF/AND/OR)
- Common Text Functions
- Joining Data with RELATED
- Basic Math & Stats Functions
- COUNT Functions (COUNTA, DISTINCTCOUNT, COUNTROWS)
- CALCULATE, ALL & FILTER
- Iterator Functions (SUMX, RANKX)
- DAX Best Practices
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
Key takeaway: Ability to understand how to use PowerBI to learn data, build relationships between tables and create visualization and reports
Testimonials
Our students work at
Frequently Asked Questions
Yes we accept installment payment. You can discuss with our team members on available payment options.
No prior programming experience is required. However, you need to know how to operate the computer efficiently. Our training approach is beginner friendly, and we work on several real world projects to help our student have deeper understanding of each module.
People prefer Tech365 because our training are:
- Project-based training
- Job recommendation
- World-class support
- Up-to-date curriculum
- Career guidance and mentoring
- Access to support community
- Certificate of completion
- Flexible payment plan
- Flexible timing
Our training is
- Globally relevant
- Beginner friendly
- Futuristic
- Relevant across industries
- In high demand
- Financially rewarding
We do not guarantee job. However, we do refer our students for jobs as different organization do reach out to us to hire our students. Some of our students have gotten job through this process.
Yes, we offer weekend classes for those who can’t attend weekdays due to their job or other reasons
Our training schedule are around January, May and September. You can talk to one of our representative for more information.
Yes our training is physical at Ikeja, Lagos. However, those outside Nigeria or living far away can join us online via zoom. The experience is similar.
Yes. In fact, this is our strength. We are committed to supporting our students to ensure they succeed.
Yes, it is recommended to 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.
Yes, we provide internet access to our students.
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.
Best Data Science/ Training
You can choose available options below based on your need.
Data Science
- Complete Data Analytics
- Machine Learning
Artificial Intelligence
- Complete Data analytics
- Machine Learning
- Artificial Intelligence
