Basic Calculator
Build a Python calculator that performs basic arithmetic operations such as addition, subtraction, multiplication, and division.
4.6· 8+
reviews
1,120+ students
enrolled
Python for Data Analysis & Machine Learning
Data is becoming one of the most valuable resources in the modern world, while machine learning is changing how businesses analyze information, recognize patterns, and solve complex problems. Python connects these possibilities through data analysis, predictive modeling, and deep learning—creating new ways to turn raw data into meaningful insights and intelligent applications.
- DURATION
- 6 Month
- PROJECTS
- 6
- CLASSES
- 47
Offline . Theory + Lab
Online live
YOU'LL LEARN
OVERVIEW
COURSE OVERVIEW
This Python for Data Analysis & Machine Learning course builds your skills in Python programming, data manipulation, visualization, statistical analysis, machine learning, and deep learning. Work with real-world datasets, develop predictive models, explore computer vision and NLP, and learn to deploy models using Flask and Streamlit through practical projects.
- Python Programming & Problem Solving
- Data Analysis with Pandas & NumPy
- Data Visualization & Insights
- Machine Learning Algorithms & Statistics
- Model Optimization & Evaluation
- Machine Learning Projects & Deployment
- Deep Learning, Computer Vision & NLP
- Practical Projects & Career Applications
Who it's for
Freelancers
Freelancers
Graduates
Graduates
Job seekers
Job seekers
Students
Students
Prerequisites
Basic computer skills
Basic computer skills
Basic English reading
Basic English reading
Desktop or laptop
Desktop or laptop
Internet connection
Internet connection
Our Facilities
Career Placement Support
Career Placement Support
Class Videos
Class Videos
Lifetime Support
Lifetime Support
Practice Lab Support
Practice Lab Support
Review Class
Review Class
CURRICULUM
9 Modules, 47 Classes
47
on campus
6 projects
21 resources
Python foundations: syntax, variables, data types, operators, collections, conditions, loops and problem solving.
ON CAMPUS THEORY
5 classes
LAB PRACTICAL
1 class
CLASSES
| CLASS | TOPIC | TYPE |
|---|---|---|
| Class 1 | Introduction to Python; popularity; role in Data Science; AI vs ML vs DL vs Data Science | Theory |
| Class 2 | Variables, data types, operators, type conversion and casting | Theory |
| Class 3 | Lists: indexing, slicing, methods and list comprehension | Theory |
| Class 4 | Strings: indexing, slicing and common string methods | Theory |
| Class 5 | Tuples, sets, dictionaries and collection comparisons | Theory |
| Class 6 | Conditional statements and loops; dictionaries; practice | Lab |
CAREER PATH
Python Developer . Junior Data Analyst
Build problem-solving skills with iteration, functions, exceptions, functional tools and object-oriented programming.
ON CAMPUS THEORY
2 classes
LAB PRACTICAL
2 classes
CLASSES
| CLASS | TOPIC | TYPE |
|---|---|---|
| Class 1 | Loop practice and list-based problem solving | Lab |
| Class 2 | Problem solving with lists, strings and collections | Lab |
| Class 3 | Lambda, filter, map and reduce | Theory |
| Class 4 | Functions, arguments, return vs print, exceptions and OOP fundamentals | Theory |
CAREER PATH
Python Developer . Automation Developer
Strengthen Python programming with OOP concepts, reusable code, exception handling and practical problem-solving.
ON CAMPUS THEORY
2 classes
LAB PRACTICAL
2 classes
CLASSES
| CLASS | TOPIC | TYPE |
|---|---|---|
| Class 1 | Classes, objects, self, constructors and constructor types | Theory |
| Class 2 | Inheritance, polymorphism and encapsulation | Theory |
| Class 3 | Function design, reusable modules and exception handling practice | Lab |
| Class 4 | Advanced collection and problem-solving exercises | Lab |
CAREER PATH
Python Developer . ML Engineer (entry-level)
Introduction to the Python data ecosystem and core numerical, tabular and visualization libraries.
ON CAMPUS THEORY
1 class
LAB PRACTICAL
2 classes
CLASSES
| CLASS | TOPIC | TYPE |
|---|---|---|
| Class 1 | NumPy: arrays, dimensions, creation, indexing, slicing and broadcasting | Theory |
| Class 2 | NumPy: mathematical, aggregate and array manipulation functions | Lab |
| Class 3 | Pandas: Series/DataFrames, CSV/Excel, inspection, cleaning and transformations | Lab |
CAREER PATH
Data Analyst . Junior Data Scientist
Use Pandas, Matplotlib, Seaborn and Plotly for data preparation, exploratory analysis and visualization projects.
ON CAMPUS THEORY
2 classes
LAB PRACTICAL
2 classes
CLASSES
| CLASS | TOPIC | TYPE |
|---|---|---|
| Class 1 | Pandas: missing values, types, rename, select/drop, merge, join, loc and iloc | Lab |
| Class 2 | Matplotlib: line, scatter, bar, histogram, pie, area and subplots | Theory |
| Class 3 | Seaborn: countplot, histplot, pairplot, boxplot, hue and analysis | Theory |
| Class 4 | Plotly, t-SNE and 3D visualization; project workflow | Lab |
CAREER PATH
Data Analyst . Data Scientist
Learn descriptive statistics, data types, distributions, outliers and statistical foundations for machine learning.
ON CAMPUS THEORY
1 class
CLASSES
| CLASS | TOPIC | TYPE |
|---|---|---|
| Class 1 | Analytics types; population/sample; mean, median, mode, variance, Z-score, distributions, outliers and data measurement scales | Theory |
CAREER PATH
Data Analyst . Data Scientist
Learn NLP, preprocessing, dimensionality reduction, supervised and unsupervised algorithms, evaluation, deployment and Kaggle practice.
ON CAMPUS THEORY
14 classes
LAB PRACTICAL
3 classes
CLASSES
| CLASS | TOPIC | TYPE |
|---|---|---|
| Class 1 | Introduction to Machine Learning, real-world use cases and ML pipelines | Theory |
| Class 2 | NLP basics: cleaning, stemming, lemmatization, BoW, TF-IDF, embeddings, spaCy and NER | Theory |
| Class 3 | Dimensionality reduction with PCA: motivation, eigenvalues, eigenvectors and implementation | Theory |
| Class 4 | Data processing: missing values, standardization, normalization, robust/log/max-absolute scaling and encoding | Lab |
| Class 5 | Linear Regression: theory, mathematics and implementation | Theory |
| Class 6 | Logistic Regression: theory, mathematics and implementation | Theory |
| Class 7 | Naive Bayes: theory, mathematics and implementation | Theory |
| Class 8 | K-Nearest Neighbors (KNN): theory, mathematics and implementation | Theory |
| Class 9 | Decision Trees: theory, mathematics and implementation | Theory |
| Class 10 | Sampling, train/test split and cross-validation | Theory |
| Class 11 | Unsupervised learning: K-Means concepts | Theory |
| Class 12 | K-Means implementation and evaluation | Lab |
| Class 13 | Bagging, Boosting and Stacking approaches | Theory |
| Class 14 | Random Forest: theory and implementation | Theory |
| Class 15 | Gradient Boosting: theory and implementation | Theory |
| Class 16 | AdaBoost and XGBoost: concepts and implementation | Theory |
| Class 17 | ML pipelines; Flask, Postman, FastAPI, Streamlit, GitHub, CI/CD, Docker, model deployment and Kaggle | Lab |
CAREER PATH
Machine Learning Engineer . Data Scientist . NLP Engineer
Introduce research reading, paper structure, reproducibility and communicating experimental results.
ON CAMPUS THEORY
1 class
LAB PRACTICAL
1 class
CLASSES
| CLASS | TOPIC | TYPE |
|---|---|---|
| Class 1 | Reading research papers; problem statement, literature review and methodology | Theory |
| Class 2 | Results, citations, limitations and preparing a research paper draft | Lab |
CAREER PATH
Research Assistant . Graduate Applicant
Explore neural networks and deep learning foundations, including ANN, CNN, activation functions, optimization, chain rule, RNN and LSTM.
ON CAMPUS THEORY
6 classes
CLASSES
| CLASS | TOPIC | TYPE |
|---|---|---|
| Class 1 | Deep learning overview and neural network workflow (as needed) | Theory |
| Class 2 | Artificial Neural Networks (ANN) | Theory |
| Class 3 | Convolutional Neural Networks (CNN) | Theory |
| Class 4 | Activation functions and optimizers | Theory |
| Class 5 | Chain rule and backpropagation foundations | Theory |
| Class 6 | Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) | Theory |
CAREER PATH
Deep Learning Engineer (junior) . AI Engineer
7 more modules to explore
FREE CLASS
Try a free class before you enroll
Share your details and our team will reserve your seat in an upcoming free class of Python for Data Analysis & Machine Learning.
PRACTICAL LEARNING
Projects you'll actually build
Every project is portfolio-grade and presented with mockups, the way a client would receive it.
Grading System
Create a program that evaluates student marks and assigns grades using conditional statements.
Real-World Dataset Analysis
Analyze datasets to identify trends, patterns, and useful insights using Python.
Data Cleaning
Clean datasets by handling missing values, inconsistent data, and duplicate records.
Data Manipulation
Filter, transform, organize, and summarize data using Pandas and NumPy.
Data Visualization
Create charts and graphs to communicate insights from datasets using Matplotlib and Seaborn.
Iris Flower Prediction
Build a machine learning model that predicts an iris flower species based on its measured features.
House Rent Prediction
Develop a regression model to estimate house rent using relevant property features.
Heart Disease Prediction
Train a classification model to predict the likelihood of heart disease from relevant health data.
Question Pair Matching
Develop a model that determines whether two questions have similar meanings.
Sentiment Analysis of Reviews
Analyze text reviews and classify their sentiment, such as positive or negative.
Simple QA Bot
Create a basic question-answering bot that responds to user questions.
Named Entity Recognition (NER)
Identify and extract entities such as names, organizations, and locations from text.
Image Classification
Train a deep learning model to classify images into predefined categories.
Image Detection & Object Detection
Develop a computer vision model that identifies objects and their locations within images.
Image Segmentation
Build a model that separates an image into meaningful regions or object areas.
Extremist Comment Detection
Develop a text classification model to identify potentially extremist comments.
12+
more practical projects across the course modules.
CAREER OUTCOME
Where this course leads
-
STEP 01Career CounsellingCareer Counselling
Free consultation & path planning
-
STEP 02Admission & OnboardingAdmission & Onboarding
Enrol, orientation & kick-off
-
STEP 03Course StartCourse Start
4 month, 51 classes begin
-
STEP 04Learning & PracticeLearning & Practice
Theory, lab & AI workflows
-
STEP 05Project & Portfolio BuildingProject & Portfolio Building
27 client-grade builds
-
STEP 06Assessment & CertificationAssessment & Certification
Viva, exam & course certificate
-
STEP 07Career PreparationCareer Preparation
CV, interview & portfolio polish
-
STEP 08Career & EarningCareer & Earning
Placement referral or freelance
- JOB PLACEMENT Explore placement See how we help graduates land their first job. Learn more
Roles graduates move into
Creative IT's career placement department prepares your CV and portfolio and refers you to matched companies. We don't guarantee employment, outcomes depend on your own work.
Machine Learning Engineer (Junior/Intern)
Data Scientist (Entry-level)
NLP Engineer(Junior)
Data Analyst(Junior)
AI/ML Research Assistant
Freelance on ML/AI related task
STUDENT REVIEWS
4.6 rated by our students
Rakib HasanNetwork Administrator
"Lab support and mentor feedback were excellent. The portfolio I built here helped me get my first paid work."
Farzana AkterNetwork Engineer
"The Computer Networking with CCNA course was practical from day one. The mentors explained everything clearly and the projects helped me build real confidence."
Tanvir AhmedYouTube Editor
"Lab support and mentor feedback were excellent. The portfolio I built here helped me get my first paid work."
Nusrat JahanVideo Editor
"The Video Editing with Premiere Pro course was practical from day one. The mentors explained everything clearly and the projects helped me build real confidence."
Arif Hossain3D Modeler
"Lab support and mentor feedback were excellent. The portfolio I built here helped me get my first paid work."
Jannatul Ferdous3D Artist
"The 3D Modeling & Rendering with Blender course was practical from day one. The mentors explained everything clearly and the projects helped me build real confidence."
Mehedi HasanMarketing Executive
"Lab support and mentor feedback were excellent. The portfolio I built here helped me get my first paid work."
Sadia IslamDigital Marketer
"The Digital Marketing Professional course was practical from day one. The mentors explained everything clearly and the projects helped me build real confidence."
Video testimonials
Watch success
stories
COURSE FAQ
Before you enroll
To enroll at Creative IT Institute, choose your preferred course and enroll online or in person at your nearest campus. If needed, our counsellors can help you pick the course that suits you best.
Our counsellors will help you choose the right course based on your interests and career goals.
Our courses run online, offline and in a hybrid format that combines both, so students can learn in the way that suits them.
Yes, you get direct support and guidance from experienced mentors throughout the course.
Yes. Creative IT Institute is ISO 9001 certified and issues a course certificate after you complete your assessments and projects.
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Try a free class before you enroll
Share your details and our team will reserve your seat in an upcoming free class of Python for Data Analysis & Machine Learning.
