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Python for Data Analysis & Machine Learning
Delivery Mode
Theory
Lab Based
Online Live Class
Recorded Video
Python for Data Analysis & Machine Learning is a comprehensive 6-month hybrid program designed to provide students with the essential skills needed for data analysis and machine learning. Through a blend of hands-on lab sessions, online theory, nd self-paced learning, participants will gain expertise in Python programming and its applications in real-world data science problems. The course covers essential topics such as data cleaning, data visualization, machine learning algorithms, and advanced chniques like image classification, deep learning, and model evaluation. By the end of the program, students will be well-equipped to analyze complex datasets, build machine learning models, and apply AI techniques to solve real-world problems.
6 Month
Course Duration
7+
Industrial Project
63
Classes
Exclusive Solutions That Set Us Apart :
Online Live Batch
Review Class
Class Videos
Extra Class
Mentor Support
Key Highlights :
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•
Fundamentals of Python programming and data manipulation.
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•
In-depth learning of data analysis libraries: Pandas, NumPy, Matplotlib, and Seaborn.
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•
Data visualization using Python for meaningful insights
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Introduction to machine learning algorithms: Supervised and Unsupervised Learning.
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Hands-on projects with real-world datasets (classification, regression models, etc.).
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Deep learning basics with frameworks like TensorFlow and Keras.
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Model evaluation, optimization, and deployment techniques.
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Freelancing & marketplace training.
Job Opportunities:
Machine Learning Engineer (Junior/ Intern)
Data Scientist (Entry-level)
NLP Engineer(Junior)
Data Analyst(Junior)
BI analyst ((Junior)
AI/ML Research Assistant
Freelance on ML/AI related task
Class Structure & Distribution of Total 0 Classes:
(For Offline Course)
Offline Class - Onsite in Campus: 0 Class
On Campus (Theory)
0 Classes - Per Class 2 Hrs
On Campus (Lab)
0 Classes - Per Class 2 Hrs
Extra Online & Recorded Class: 0 Class
Online Extra Live Class
0 Classes - Per Class 1+ Hrs
Required Recorded Videos
0 Classes - Duration Variable
Course Prerequisite:
Basic Knowledge of Using Computer
Desktop / Laptop
Internet Connection
This Course is Designed for
(Everyone)
Job seekers
Students
Entrepreneurs
Software Engineers
Tech Learners
Freelancers
Business Owners
Softwares You'll Learn:
(AI Driven)
Google-Colab
Kaggle
Anaconda
Jupyter
VS Code
Course Outline Structure
Provide required PDF + Class Recordings to ensure the best learning for our students.
5 Modules
63 Classes
15 Projects
58 Topics
31 Resources
Duration: 6 Classes
Focus:
Introduction to Python programming, Understanding the core concepts of
Python, Building a foundation in problem-solving and coding logic
Class Type Breakdown:
- Offline (Theory): 4 Classes
- Lab (Practical): 1 Class
- Lab (Exam): 1 Class
Projects Included: 2 Projects
Career Path: Python Developer
Prerequisites: N/A
Key Highlights:
- • Core Python concepts essential for data science.
- • Hands-on experience with variables, data types, and data structures.
- • Use of conditional logic (if-else, elif) in coding challenges.
- • Project: Apply what you've learned to a real-world Python project
- • (e.g., a basic calculator, and a Grading system).
- • Foundation exam (MCQs & coding-based problems)
Projects:
- • Basic calculator
- • Grading system
| Class No. | Class Type | Topic/Lesson | Resource Type |
|---|---|---|---|
| Class 1 | Offline (Theory) | Fundamentals of Machine Learning |
PDF
|
| Class 2 | Offline (Theory) | Python Variables + operators |
Class Video
|
| Class 3 | Offline (Theory) | Python Data structure |
Class Video
|
| Class 4 | Lab (Practical) | Python Data structure |
N/A
|
| Class 5 | Offline (Theory) | Conditional Statement |
Class Video
|
| Class 6 | Lab (Exam) | Foundation exam (MCQs & Coding) |
N/A
|
Duration: 9 Classes
Focus:
Introduction to advanced Python programming, Understanding the core
concepts of Python loops, List comprehension, functions, exception handling, and
OOP, Building a foundation in problem-solving and coding logic with Hacker Rank
Class Type Breakdown:
- Offline (Theory): 3 Classes
- Lab (Practical): 5 Classes
- Lab (Exam): 1 Class
Projects Included: 2 Projects
Career Path: Python Developer
Prerequisites: Basics of Python Programming
Key Highlights:
- • Understanding of core Python concepts critical for data science
- • Hands-on experience with loops, functions, and exception handling
- • Object-Oriented Programming (OOP) principles such as classes, objects,
- • and inheritance, method over loading and riding.
- • Solving real-world problems through problem-solving techniques
- • Project: Apply your learning to build a grading system (using loops,
- • functions, and exception handling) and a basic calculator with
- • enhanced features.
- • Practical Exam (coding-based problems)
Projects:
- • Basic calculator with
- • enhanced features
- • Grading system with
- • enhanced features
| Class No. | Class Type | Topic/Lesson | Resource Type |
|---|---|---|---|
| Class 1 | Offline (Theory) | Python loops with problems |
PDF
|
| Class 2 | Offline (Theory) | Python loops + Functions |
Class Video
|
| Class 3 | Offline (Theory) | Exception Handling + Functions |
Class Video
|
| Class 4 | Lab (Practical) | Problem Solving |
N/A
|
| Class 5 | Lab (Practical) | Problem Solving |
N/A
|
| Class 6 | Lab (Practical) | Problem Solving + Hacker Rank |
N/A
|
| Class 7 | Lab (Practical) | OOP |
N/A
|
| Class 8 | Lab (Practical) | OOP + Decorator |
N/A
|
| Class 9 | Lab (Exam) | Coding |
N/A
|
Duration: 8 Classes
Focus:
Master the essential Python libraries—Pandas, NumPy, Matplotlib, and
Seaborn for efficient data analysis. Gain proficiency in data cleaning, exploration, and
visualization techniques crucial for data science. Develop hands-on problem-solving
skills through project-based learning with real-world datasets using Python.
Class Type Breakdown:
- Offline (Theory): 3 Classes
- Lab (Practical): 4 Classes
- Lab (Exam): 1 Class
Projects Included: 2+ Projects
Career Path: Data Analyst, Junior Data Analyst, Data Science Intern
Prerequisites: Advanced Python and Problem Solving
Key Highlights:
- • Gain in-depth knowledge of Pandas, NumPy, Seaborn, and Matplotlib for data
- • manipulation and visualization.
- • Hands-on experience with Pandas for data cleaning, exploration, and handling
- • missing data, and NumPy for efficient numerical operations.
- • Data visualization skills: Learn to create impactful visualizations with Matplotlib
- • and Seaborn, including line plots, scatter plots, histograms, box plots, and
- • heatmaps.
- • Develop the ability to analyze complex datasets, uncover insights, and
- • communicate findings through clear and effective visualizations.
- • Project: Apply your learning to analyze real-world datasets, perform data
- • cleaning, explore trends, and visualize key insights.
- • Practical Exam: Solve coding challenges focused on data manipulation and
- • visualization to demonstrate your skills with Pandas, NumPy, Seaborn, and
- • Matplotlib.
Projects:
- • Analyze real-world datasets
- • Data cleaning
- • Visualize key insights
- • Data manipulation
| Class No. | Class Type | Topic/Lesson | Resource Type |
|---|---|---|---|
| Class 1 | Offline (Theory) | NumPy |
Class Video
|
| Class 2 | Offline (Theory) | Pandas for data analysis and Manipulations |
Class Video
|
| Class 3 | Offline (Theory) | Pandas for data analysis and Manipulations |
Class Video
|
| Class 4 | Lab (Practical) | Matplotlib for data visualization |
N/A
|
| Class 5 | Lab (Practical) | Seaborn for data visualization (2D,3D) |
N/A
|
| Class 6 | Lab (Practical) | Working with Datasets |
N/A
|
| Class 7 | Lab (Practical) | Working with Datasets |
N/A
|
| Class 8 | Lab (Exam) | Data analysis and visualization |
N/A
|
Duration: 26 Classes
Focus:
Statistics for ML, ML algorithms, Flask API, Kaggle competition, Streamlit for
model deploy.
Class Type Breakdown:
- Offline (Theory): 5 Classes
- Recorded Video: 7 Classes
- Lab (Practical): 12 Classes
- Online Theory: 1 Class
- Lab (Exam): 1 Class
Projects Included: 5+ Projects
Career Path: ML Engineer, Data Scientist, AI Researcher, Business Intelligence, Analyst, ML Researcher Intern
Prerequisites: Working with Libraries and Data Analysis
Key Highlights:
- • Statistics for machine learning.
- • Apply machine learning algorithms and deploy models using Flask and Streamlit.
- • Solve real-world problems via Kaggle competitions.
- • Gain hands-on experience in data analysis, model building, and API creation.
Projects:
- • Data analysis,
- • Model building
- • API creation
| Class No. | Class Type | Topic/Lesson | Resource Type |
|---|---|---|---|
| Class 1 | Offline (Theory) | Statistics |
N/A
|
| Class 2 | Offline (Theory) | Linear Regression |
N/A
|
| Class 3 | Recorded Video | Ridge, Lasso + GitHub |
Recorded Video
|
| Class 4 | Lab (Practical) | Logistic Regression + GitHub |
N/A
|
| Class 5 | Lab (Practical) | Gradient Descent + GitHub |
N/A
|
| Class 6 | Lab (Practical) | K-Nearest Neighbors |
N/A
|
| Class 7 | Lab (Practical) | K-Means Cluster (clustering) |
N/A
|
| Class 8 | Recorded Video | DBSCAN |
Recorded Video
|
| Class 9 | Offline (Theory) | Naïve Bayes |
Class Video
|
| Class 10 | Recorded Video | Naïve Bayes (code) |
Recorded Video
|
| Class 11 | Online Theory | Decision Tree |
Class Video
|
| Class 12 | Lab (Practical) | Decision Tree with pruning |
N/A
|
| Class 13 | Lab (Practical) | Sampling and Cross- Validation |
N/A
|
| Class 14 | Lab (Practical) | Model Optimization |
N/A
|
| Class 15 | Offline (Theory) | Ensemble Techniques + Boosting Algorithm |
PDF
|
| Class 16 | Lab (Practical) | Boosting Algorithm |
N/A
|
| Class 17 | Lab (Practical) | Bagging Algorithm (Random Forest) |
N/A
|
| Class 18 | Lab (Practical) | Stacking model |
N/A
|
| Class 19 | Lab (Practical) | Flask Framework, POSTMAN |
N/A
|
| Class 20 | Recorded Video | Streamlit for ML |
Recorded Video
|
| Class 21 | Lab (Practical) | Model deployment |
N/A
|
| Class 22 | Recorded Video | Kaggle for ML |
Recorded Video
|
| Class 23 | Recorded Video | Kaggle competition |
Recorded Video
|
| Class 24 | Recorded Video | Named Entity Recognition |
Recorded Video
|
| Class 25 | Offline (Theory) | Research |
Class Video
|
| Class 26 | Lab (Exam) | Develop ML models |
N/A
|
Duration: 14 Classes
Focus:
Neural Network, Backpropagation, Chain Rule, Optimizers, Activation Functions,
CV, and NLP for deep learning
Class Type Breakdown:
- Recorded Video: 7 Classes
- Offline (Theory): 3 Classes
- Lab (Practical): 4 Classes
Projects Included: 4 Projects
Career Path: DL Engineer, CV Engineer, NLP Engineer, Data Scientist, ML Engineer
Prerequisites: Statistics for Machine Learning & Machine Learning with Flask API and Kaggle
Key Highlights:
- • Neural Network and Deep Learning concepts: backpropagation, CNNs, RNNs,
- • LSTMs, and Transformers.
- • Learn image classification, segmentation, and digital image processing
- • techniques.
- • Hands-on experience with activation functions, optimizers, and model evaluation.
- • Build, train, and deploy deep learning models for real-world tasks.
Projects:
- • Image classification
- • Object detection
- • Image Segmentation
- • Extremist comment
- • detection
| Class No. | Class Type | Topic/Lesson | Resource Type |
|---|---|---|---|
| Class 1 | Recorded Video | Introduction to deep learning |
Recorded Video
|
| Class 2 | Recorded Video | Artificial Neural Network |
Recorded Video
|
| Class 3 | Offline (Theory) | Basics of CNN |
Class Video
|
| Class 4 | Lab (Practical) | CNN for image classification |
N/A
|
| Class 5 | Lab (Practical) | Transfer Learning |
N/A
|
| Class 6 | Recorded Video | Digital Image Processing |
Recorded Video
|
| Class 7 | Offline (Theory) | Image Localization |
Class Video
|
| Class 8 | Recorded Video | Image Segmentation |
Recorded Video
|
| Class 9 | Recorded Video | Image Segmentation |
Recorded Video
|
| Class 10 | Offline (Theory) | Object Detection |
Class Video
|
| Class 11 | Recorded Video | Introduction to NLP |
Recorded Video
|
| Class 12 | Lab (Practical) | RNN |
N/A
|
| Class 13 | Lab (Practical) | LSTM |
N/A
|
| Class 14 | Recorded Video | Transformer basics |
Recorded Video
|
Admission Is Going On
Enroll now to any of our Offline Courses (On- Campus)
Course Fee Offline
BDT. 50,000
Online (Live Class) courses as per your suitable time.
Course Fee Online
BDT. 20,000
Some of the Creative Projects Done by Our Students
Students Shared Their Feedback
Tazim
5.0
Creative IT Institute is one of the best IT training institute that i know of. They're well behaved & professional 👍 Environment was calm although i noticed some disturbance while i was in there, some of the students as they were leaving, they were shouting & laughing carelessly., it shouldn't have happened 🙂 but everything else is fine & right by me 👍
Mahmudul Hasan Wali
5.0
Creative IT Institute is No 1 IT institute in Bangladesh. If you want to develop your skills then you can visit Creative IT Institute
Nurul Amin (Shah)
5.0
One of best training institution in Dhaka, Bangladesh
Course Related FAQ
Answer : This course teaches you Python programming, data analysis, machine learning, and deep learning from scratch. You’ll learn how to build real-world ML models using tools like Scikit-learn, Pandas, NumPy, and TensorFlow. It covers everything from Python basics to deploying ML models and even working with CNNs for image classification.
Answer : No prior experience needed. This course is beginner-friendly and starts with the fundamentals of Python. You’ll be guided step by step, even if you’re completely new to coding or data science.
Answer : Python is the most popular and beginner-friendly language for machine learning. ● Easy to Learn and Use ● Rich Ecosystem of Libraries ● Large Community Support ● Used by Top Companies (Google, Netflix, Amazon, and Facebook) ● Career-Boosting Skills (Data Science, Machine Learning, Data Engineering, AI Research)
Answer : The course includes multiple hands-on projects using real datasets (CSV, image, text). You’ll also learn to deploy models via Flask or Streamlit and can showcase your work on GitHub or Kaggle.
Answer : Absolutely. This course is designed to prepare you for real-world careers in data and AI. By the end of the course, you'll have the technical skills, portfolio projects, and tools needed to apply for roles such as: ● Data Analyst ● Machine Learning Engineer (Entry Level) ● Data Scientist (Junior) ● AI/ML Developer ● Python Developer (Data-focused) ● NLP Engineer (Beginner Level) ● Freelancer in Data Science or ML.
Answer : Intel Core i5 8GB RAM (having GPU support is good for future work)
Answer : Jupyter Notebook, Anaconda, TensorFlow, scikit-learn, Pandas, and NumPy.
Answer : Not mandatory, but helpful for training large models.
Course Services
Job Placement Support
Creative IT offers students an exclusive gateway to their dream careers through its Job Placement Support. Throughout the course, learners receive targeted training in CV crafting, portfolio development, and essential soft skills, ensuring they are industry-ready. Upon completion, students are strategically referred to companies that match their skills and potential.
Backup Class
A Backup Class is a special facility where students can revisit lessons missed due to illness or emergencies. It helps maintain learning continuity and ensures that students’ progress is not interrupted. However, the number of backup classes may vary depending on the course. Therefore, it is advisable not to miss classes unless absolutely necessary.
Short Support
Our Short Support is designed for students who need quick assistance with a tricky topic or a small doubt in their course. Students can access 20–30 minute sessions twice a week, guided by expert faculty to resolve their issues effectively. Regular class attendance is required, and the question should be clear and specific. This facility empowers students to confidently overcome challenges and master difficult concepts with ease.