Certified Training on Data Science and Machine Learning using Python

Certified Training on Data Science and Machine Learning using Python

This is a specialization course which will help you to get a break into Data Science and Machine Learning with a project.

course at a glance

  • Date : 11 Aug - 10 Oct 2024
  • No. of Classes/ Sessions : 26
  • Total Hours : 66
  • Last Date of Registration : 8 Aug 2024
  • Class Schedule :
    • Sunday - 6:00 PM - 8:30 PM
    • Tuesday - 6:00 PM - 8:30 PM
    • Thursday - 6:00 PM - 8:30 PM
  • venue : BASIS Institute of Technology & Management Limited BDBL Bhaban (3rd Floor - East), 12 Kawran Bazar, Dhaka -1215

Price: TK. 18,000
(including VAT & TAX)
One-time full payment: BDT 15,000

Data Science and Machine Learning using Python 

Day-1 An Introduction to Python.
A Brief History of Python Versions
Installing Python

Local & Global Variables

Data Types
Dynamic Types
Python Reserved Words
Naming Conventions

Your First Python Program

How Python Code Gets Executed

Difference between Compiler and Interpreter

Day-2 Basic Python Syntax

Basic Syntax Comments

Receiving Input

Type Conversion/Casting

Numeric Data Types

Formatted Float

Boolean Data Types



Formatted Strings

String Methods

Day-3 Language Components & Loop
Arithmetic Operations

Operator Precedence

Math Functions


If Statements

Logical Operators

Letter Grade Program

Comparison/Relational/Conditional Operators

Leap Year Program

Assignment Operators

Ternary Operators

Weight Converter Program

While Loops

Break & Continue Statement

Sum of n Numbers Program

Building a Guessing Game

Building the Car Game

For Loops

For-While Comparison

For with Range Function

Nested Loops

Day-4 List


Bubble Sort 

List Methods

Range Function in a list

2D Lists/Matrix



Set (Union/Intersection/Difference)


Day-5 Functions



Keyword Parameters/Arguments

Default Parameter Value

xargs and xxargs

Return Statement

Lambda Function

Map and Filter function

List Comprehensions

Zip Function



Exception Handling

Day-6 Classes, Objects & Method

Object Oriented Programing (OOP)



Introducing Method

Default Constructors
Parameterized Constructor

Pass Statement

Class/Static Variable

Instance Variable

Intro to Inheritance
Single Inheritance
Hierarchical Inheritance
Multilevel Inheritance
Multiple Inheritance
Method Overloading
Method Overriding

Day-7 SQL Normalization/Relationship/Joining

Understanding the RDBMS

Database Normalization

NoSQL database

Select Statement

Filtering with where clause

Joining multiple conditions

Order By/Distinct/TOP/Like

Sql server join types

Inner join

Left Join

Right Join

Full Join


Union all



Day-8 Data Analysis using NumPy

A brief introduction

Installation instructions.

NumPy arrays

Built-in methods

Array methods and attributes.

Indexing, slicing


Boolean masking

Arithmetic Operations

Universal Functions   

Exercises Overview

Exercises Solutions

Day-9 Data Analysis using Pandas-Part 1

A brief introduction and installation instructions.

Pandas Introduction.

Pandas Data Structures - Series

Pandas Data Structures – DataFrame

Day-10 Data Analysis using Pandas-Part 2

Hierarchical Indexing

Handling Missing Data

Data Wrangling - Combining, Merging, Joining, Group by

Useful Methods and Operations

Day-11 Capstone Project- Data Analysis using Pandas

Project 1

Customer Purchases Data (Overview)

Customer Purchases Data (Solutions)

Project 2

Chicago Payroll Data (Overview)

Chicago Payroll Data (Solutions)

Day-12 Statistics Part-1

Quantitative Analysis

Frequency Distribution

Data Presentation

Bar Graph vs Histogram

Methods of Center Measurement (Mean, Median, Mode)

Methods of Variability Measurement

Range, Variance, Standard deviation

Quartiles, Deciles, Percentiles, Coefficient of Variation

Five Number Summary/Box Plot

Day-13 Statistics Part-2

Coefficient of Correlation

Standared Score-Z Score-T Score

Normal Distribution

Hypothesis Test, Z test, T test

A/B Testing

Day-14 Data Visualization using Matplotlib-Part 1

Basic Plotting

Creating multiple plot on the same canvas

Matplotlib "Object Oriented" approach

Creating inset plot

Creating a figure and a set of subplots

Saving figures, Decorating figures

Project 1

Data Analysis & Visualization (Overview)

Data Analysis & Visualization (Solutions)

Day-15 Data Visualization using - Pandas-Part 2

Pandas Built-in Data Visualization

Style Sheets

Area Plot, Bar/Barh Chart

Histogram, Line Chart

Scatter Plot, Box Plot

Hexagonal Bin Plot, Pie Chart                          

Kernel Density Estimation Plot (KDE)

Project 2

Data Analysis & Visualization (Overview)

Data Analysis & Visualization (Solutions) 

Day-16 Data Visualization using - Seaborn

Distribution plot, Lmplot

Jointplot, Pairplot, Kdeplot,

Stripplot, Swarmplot, Boxplot

Violinplot, Pointplot

Axis grids, Matrix Plot, Heatmap

Seaborn figure styles

Day-17 Introduction to Machine Learning

Introduction to ML - What, Why

Machine Learning Applications

Supervised Learning

Unsupervised Learning

What is Machine Learning Model?

Training and Test Sets: Splitting Data

K Fold Cross Validation

Underfitting and Overfitting

Confusion Matrix (Precision, Recall, f1 score)

Day-18 Scikit-learn - Feature Engineering

Feature Scaling Theory

Feature Scaling - Hands-on

Principal Component Analysis (PCA)

Principal Component Analysis (PCA) - Hands-on

Label Encoding - Hands-on

Ordinal Encoding- Hands-on

One Hot Encoding - Hands-on

Outlier Removing - Hands-on

Day-19 Scikit-learn - Linear Regression, Multiple Regression

Training and Testing Data Separation

Linear Regression Theory

Simple Linear Regression Model - Hands-on

Multiple Linear Regression Theory

Multiple Linear Regression Model - Hands-on


Insurance Data Project (Overview)

Insurance Data Project (Solutions)

Day-20 Scikit-learn - K Nearest Neighbors & Logistic Regression

Binary Logistic Regression Theory

Binary Logistic Regression Algorithm

Binary Logistic Regression Model - Hands-on

K Nearest Neighbors Theory

K Nearest Neighbors Algorithm

K Nearest Neighbors Pen & Paper Exercise

K Nearest Neighbors - Hands-on


K Nearest Neighbors (Overview)

K Nearest Neighbors (Solutions)

Day-21 Scikit-learn - Naive Bayes Classification & Feature Engineering

How to save and load your trained Machine Learning

K-Fold Cross Validation Implementation

Introduction to Kaggle

Introduction to Goggle Colab

Naive Bayes Classification Theory

Naive Bayes Classification Algorithm

Naive Bayes Classification Pen & Paper Exercise

Naive Bayes Classification Hands-on

Day-22 Scikit-learn - Decision Tree, Random Forests & Ensemble Learning

Decision Tree Theory

Entropy, IG

Decision Tree Hands-on

Ensemble Learning,

Bagging, Random Forest, Boosting Theory

Bagging Hands-on

Random Forests Hands-on

Day-23 Scikit-learn - Support Vector Machines (SVM)

Finding best model & hyper parameter tuning using GridSearchCV

Support Vector Machines Theory

Support Vector Machines Algorithm

Support Vector Machines (SVMs)

Support Vector Machines - Hands-on (SVMs)


Support Vector Machines (Overview)

Support Vector Machines (Solutions)

Day-24 Scikit-learn - K Means Clustering

K Means Clustering Theory

K Means Clustering Algorithm

Modified K Means Clustering Algorithm

K Means Clustering Pen & Paper Exercise

K Means Clustering - Hands-on


K Means Clustering (Overview)

K Means Clustering (Solutions)

Day-25 NLTK – Spacy - Natural Language Processing (NLP)

What is NLP?

NLP Pipeline

Application of NLP

NLP- Hands-on: Segmentation, Tokenization, Stemming,

Lemmatization, POS Tagging,

Punctuation, Regex, Stop_words

Project: Email-Ham or Spam

Day-26 Tensorflow - Deep Learning-Artificial Neural Network (ANN)

What is Neurons?

Biological Neural Network (BNN)

Artificial Neural Network (ANN)

The Architecture of Artificial Neural Network (ANN)

How do Artificial Neural Network (ANN) works

Backward Error Propagations 

Project: Handwritten digits classification using neural network



Certified Training on Data Science and Machine Learning using Python Certified Training on Data Science and Machine Learning using Python 65 Hrs

Tentative Class Start

11th August, 2024

Available Seat

10 / 25

who can join

People who have basic computer knowledge. Who want to learn about Data Science and Machine Learning.

Meet the Instructor