Course Overview
Data Science Professional (60h) হলো একটি Advanced, Practical & Career-Focused Course, যেখানে data ব্যবহার করে real-world problems analyse, solve এবং actionable insights তৈরি করার skills শেখানো হবে।
এই course-এ Python, NumPy, Pandas, Data Cleaning, EDA, Statistics, Data Visualization, Feature Engineering, Machine Learning, Supervised & Unsupervised Learning এবং Model Evaluation শেখানো হবে।
Real-World Data Science Projects-এর মাধ্যমে learners problem define, appropriate model select, results interpret এবং business-oriented insights তৈরি করার professional skills অর্জন করবে।
What You Will Learn
✔ Data Science Fundamentals
✔ Python for Data Science
✔ NumPy
✔ Pandas
✔ Data Cleaning
✔ Data Preprocessing
✔ Exploratory Data Analysis (EDA)
✔ Data Visualization
✔ Statistics for Data Science
✔ Probability Fundamentals
✔ Correlation & Distribution
✔ Outlier Detection
✔ Feature Engineering
✔ Feature Selection
✔ Machine Learning Fundamentals
✔ Supervised Learning
✔ Unsupervised Learning
✔ Regression
✔ Classification
✔ Clustering
✔ Model Training
✔ Train-Test Split
✔ Model Evaluation
✔ Cross-Validation Basics
✔ Hyperparameter Tuning Basics
✔ Scikit-learn
✔ Business Problem Solving
✔ Real-World Data Science Projects
Course Curriculum
Module 1 — Data Science Fundamentals
- What is Data Science?
- Data Science Lifecycle
- Data Scientist vs Data Analyst
- Business Problem vs Data Problem
- Types of Data
- Structured & Unstructured Data
- Data Science Workflow
- Defining Business Questions
- Data-Driven Decision Making
Module 2 — Python for Data Science
- Python Fundamentals
- Variables & Data Types
- Lists, Tuples, Sets & Dictionaries
- Conditional Statements
- Loops
- Functions
- Lambda Functions
- File Handling
- Exception Handling
- Working with CSV & Data Files
Module 3 — NumPy & Numerical Computing
- Introduction to NumPy
- NumPy Arrays
- Array Operations
- Indexing & Slicing
- Reshaping
- Mathematical Operations
- Statistical Functions
- Aggregation
- Broadcasting
- Numerical Data Processing
Module 4 — Pandas & Data Manipulation
- Series & DataFrame
- Importing CSV & Excel Data
- Selecting Data
- Filtering
- Sorting
- GroupBy
- Aggregation
- Merge & Join
- Concatenation
- Pivot Tables
- Data Transformation
Module 5 — Data Cleaning & Preprocessing
- Understanding Raw Data
- Missing Values
- Duplicate Data
- Inconsistent Data
- Data Type Conversion
- String Cleaning
- Date & Time Data
- Outlier Handling
- Data Validation
- Data Preprocessing Workflow
Module 6 — Exploratory Data Analysis (EDA)
- What is EDA?
- Dataset Exploration
- Descriptive Statistics
- Mean, Median & Mode
- Variance & Standard Deviation
- Distribution Analysis
- Correlation Analysis
- Outlier Detection
- Pattern Identification
- Trend Analysis
- Finding Business Insights
Module 7 — Data Visualization
- Principles of Data Visualization
- Matplotlib
- Seaborn
- Line Charts
- Bar Charts
- Histograms
- Scatter Plots
- Box Plots
- Heatmaps
- Distribution Plots
- Choosing the Right Visualization
- Data Storytelling
Module 8 — Statistics & Probability for Data Science
- Statistics Fundamentals
- Population vs Sample
- Measures of Central Tendency
- Measures of Dispersion
- Probability Basics
- Probability Distributions
- Normal Distribution
- Correlation
- Covariance
- Sampling Concepts
- Statistical Interpretation
- Introduction to Hypothesis Testing
Module 9 — Feature Engineering
- What is Feature Engineering?
- Feature Creation
- Feature Transformation
- Encoding Categorical Data
- Label Encoding
- One-Hot Encoding
- Scaling & Normalization
- Handling Outliers
- Feature Selection
- Preparing Data for Machine Learning
Module 10 — Machine Learning Fundamentals
- What is Machine Learning?
- Machine Learning Workflow
- Supervised vs Unsupervised Learning
- Training Data
- Testing Data
- Train-Test Split
- Features & Target
- Model Training
- Predictions
- Overfitting & Underfitting
- Generalization
Module 11 — Regression
- What is Regression?
- Linear Regression
- Multiple Linear Regression
- Regression Workflow
- Model Training
- Predictions
- Mean Absolute Error
- Mean Squared Error
- Root Mean Squared Error
- R² Score
- Practical Regression Project
Module 12 — Classification
- What is Classification?
- Logistic Regression
- Decision Tree
- Random Forest Overview
- Classification Workflow
- Confusion Matrix
- Accuracy
- Precision
- Recall
- F1 Score
- ROC-AUC Overview
- Practical Classification Project
Module 13 — Unsupervised Learning
- What is Unsupervised Learning?
- Clustering Fundamentals
- K-Means Clustering
- Choosing Number of Clusters
- Customer Segmentation
- Cluster Analysis
- Introduction to Dimensionality Reduction
- Practical Clustering Project
Module 14 — Model Evaluation & Improvement
- Model Evaluation
- Cross-Validation Basics
- Bias & Variance
- Overfitting
- Underfitting
- Feature Selection
- Hyperparameter Tuning Basics
- Model Comparison
- Performance Interpretation
- Selecting the Right Model
Module 15 — End-to-End Data Science Project
Course-এর শেষ পর্যায়ে শিক্ষার্থীরা একটি complete end-to-end Data Science Project তৈরি করবে।
Project Workflow:
📥 Step 1 — Problem Definition
- Business Problem Understanding
- Defining Objectives
- Identifying KPIs
🧹 Step 2 — Data Preparation
- Data Collection/Import
- Data Cleaning
- Missing Value Handling
- Duplicate Removal
- Data Transformation
🔎 Step 3 — EDA
- Statistical Analysis
- Pattern Identification
- Correlation Analysis
- Outlier Detection
- Visualization
⚙️ Step 4 — Feature Engineering
- Feature Creation
- Encoding
- Scaling
- Feature Selection
🤖 Step 5 — Machine Learning
- Model Selection
- Training
- Prediction
- Model Evaluation
- Model Comparison
📊 Step 6 — Final Insights
- Business Findings
- Model Results
- Data Visualization
- Recommendations
- Final Project Report
Practical Projects
কোর্সে বিভিন্ন ধরনের real-world dataset নিয়ে Practical কাজ করা হবে, যেমন—
📈 Sales Prediction
- Historical Sales Analysis
- Feature Engineering
- Sales Prediction
- Model Evaluation
👥 Customer Segmentation
- Customer Data Analysis
- Purchasing Behavior
- K-Means Clustering
- Customer Group Identification
🏦 Customer Churn Analysis
- Customer Behavior
- Data Preprocessing
- Classification Model
- Churn Prediction
- Business Recommendations
🏠 Price Prediction
- Property Data Analysis
- Feature Engineering
- Regression Model
- Price Prediction
Course Features
✅ 60 Hours Live Class
✅ বাংলা + English Explanation
✅ Intermediate to Advanced Training
✅ Python for Data Science
✅ NumPy & Pandas
✅ Data Cleaning & Preprocessing
✅ Exploratory Data Analysis
✅ Statistics & Probability
✅ Data Visualization
✅ Feature Engineering
✅ Machine Learning Fundamentals
✅ Regression & Classification
✅ Clustering
✅ Model Evaluation
✅ Scikit-learn
✅ Real-World Datasets
✅ Multiple Practical Projects
✅ Complete End-to-End Data Science Project
✅ Instructor Support
✅ Class Recording (যদি প্রযোজ্য হয়)
✅ Certificate of Completion (যদি প্রদান করা হয়)
Who Can Join?
- Aspiring Data Scientists
- Data Analysts
- Business Analysts
- Python Developers
- Software Developers
- CSE & ICT Students
- Diploma Engineering Students
- Statistics Students
- Mathematics Students
- Researchers
- Business Professionals
- Job Seekers
- Machine Learning Beginners
- যারা Data Science Career শুরু করতে চান
Basic Python এবং Statistics জানা থাকলে সবচেয়ে ভালো।
After Completing This Course
এই কোর্স শেষ করার পর আপনি—
- Data Science-এর complete workflow বুঝতে পারবেন।
- Python ব্যবহার করে Data Process ও Analyze করতে পারবেন।
- NumPy ও Pandas দিয়ে বিভিন্ন ধরনের Dataset নিয়ে কাজ করতে পারবেন।
- Raw Data Clean ও Preprocess করতে পারবেন।
- Exploratory Data Analysis (EDA) পরিচালনা করতে পারবেন।
- Statistics ও Probability ব্যবহার করে Data Interpret করতে পারবেন।
- Matplotlib ও Seaborn দিয়ে Professional Data Visualization তৈরি করতে পারবেন।
- Machine Learning-এর জন্য Feature Engineering ও Data Preparation করতে পারবেন।
- Regression ব্যবহার করে Numerical Prediction করতে পারবেন।
- Classification ব্যবহার করে বিভিন্ন Category বা Outcome Predict করতে পারবেন।
- K-Means-এর মতো Clustering Algorithm ব্যবহার করে Customer বা Data Segmentation করতে পারবেন।
- Model Performance বিভিন্ন Evaluation Metrics দিয়ে Measure করতে পারবেন।
- Overfitting, Underfitting এবং Model Generalization-এর Concepts বুঝতে পারবেন।
- Scikit-learn ব্যবহার করে Machine Learning Workflow তৈরি করতে পারবেন।
- Business Problem থেকে Data-driven Solution তৈরি করার Process বুঝতে পারবেন।
- End-to-End Data Science Project সম্পন্ন করতে পারবেন।
- Advanced Machine Learning, Deep Learning, NLP বা AI শেখার জন্য Strong Foundation তৈরি করতে পারবেন।