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।

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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

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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

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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

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Module 3 — NumPy & Numerical Computing

• Introduction to NumPy
• NumPy Arrays
• Array Operations
• Indexing & Slicing
• Reshaping
• Mathematical Operations
• Statistical Functions
• Aggregation
• Broadcasting
• Numerical Data Processing

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Module 4 — Pandas & Data Manipulation

• Series & DataFrame
• Importing CSV & Excel Data
• Selecting Data
• Filtering
• Sorting
• GroupBy
• Aggregation
• Merge & Join
• Concatenation
• Pivot Tables
• Data Transformation

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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)

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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।

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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।