Course Overview

Python for Data Science (45h) হলো একটি Practical & Career-Focused Course, যেখানে Python ব্যবহার করে data clean, analyze, visualize এবং Machine Learning-এর জন্য প্রস্তুত করার skills শেখানো হবে।

এই course-এ Python Fundamentals, NumPy, Pandas, Data Cleaning, EDA, Matplotlib, Seaborn, Statistical Analysis এবং Data Visualization শেখানো হবে।

Real-World Data Science Projects-এর মাধ্যমে learners dataset analyse, meaningful questions তৈরি এবং data থেকে actionable insights বের করার practical skills অর্জন করবে।

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What You Will Learn

✔ Python Fundamentals for Data Science

✔ Variables & Data Types

✔ Operators

✔ Conditional Statements

✔ Loops

✔ Functions

✔ Lists, Tuples, Sets & Dictionaries

✔ String Manipulation

✔ File Handling

✔ Exception Handling

✔ NumPy

✔ Pandas

✔ DataFrames & Series

✔ Data Cleaning

✔ Missing Data Handling

✔ Duplicate Data Handling

✔ Data Transformation

✔ Exploratory Data Analysis (EDA)

✔ Matplotlib

✔ Seaborn

✔ Statistical Analysis Basics

✔ Data Visualization

✔ Correlation Analysis

✔ Outlier Detection

✔ Real-World Data Analysis

✔ Data Science Project

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Course Curriculum

Module 1 — Python Fundamentals

• Introduction to Python
• Python Installation & Environment
• Variables
• Data Types
• Numbers & Strings
• Boolean Values
• Operators
• Type Conversion
• Basic Input & Output
• Writing Your First Python Program

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Module 2 — Python Data Structures

• Lists
• Tuples
• Sets
• Dictionaries
• Indexing & Slicing
• Adding & Removing Elements
• Iterating Through Data
• Nested Data Structures
• Practical Data Handling

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Module 3 — Conditional Statements & Loops

• if Statement
• elif & else
• Comparison Operators
• Logical Operators
• for Loop
• while Loop
• break & continue
• Nested Loops
• Practical Data Processing

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Module 4 — Functions & Modules

• What is a Function?
• Creating Functions
• Parameters & Arguments
• Return Values
• Default Arguments
• Lambda Functions
• Built-in Functions
• Importing Modules
• Creating Reusable Code

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Module 5 — File Handling & Error Management

• Reading Files
• Writing Files
• CSV Files
• Working with Text Data
• Exception Handling
• try / except
• finally
• Handling Data Errors
• Basic Code Organization

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Module 6 — NumPy for Data Science

• Introduction to NumPy
• NumPy Arrays
• Array Operations
• Indexing & Slicing
• Reshaping Arrays
• Mathematical Operations
• Statistical Functions
• Aggregation
• Broadcasting Basics
• NumPy Practical Exercises

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Module 7 — Pandas Fundamentals

• Introduction to Pandas
• Series
• DataFrame
• Creating DataFrames
• Reading CSV Files
• Reading Excel Data
• Selecting Rows & Columns
• Filtering Data
• Sorting Data
• Renaming Columns
• Adding & Removing Columns

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Module 8 — Data Cleaning with Pandas

• Understanding Raw Data
• Missing Values
• Handling NULL/NaN
• Removing Duplicates
• Data Type Conversion
• String Cleaning
• Date & Time Data
• Replacing Values
• Handling Inconsistent Data
• Data Quality Checks

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Module 9 — Data Transformation & Analysis

• GroupBy
• Aggregation
• Merge
• Join
• Concatenation
• Pivot Tables
• Apply Functions
• Transforming Data
• Creating Calculated Columns
• Preparing Data for Analysis

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Module 10 — Exploratory Data Analysis (EDA)

• What is EDA?
• Understanding Dataset Structure
• Descriptive Statistics
• Mean, Median & Mode
• Minimum & Maximum
• Standard Deviation
• Distribution Analysis
• Correlation
• Outlier Detection
• Finding Patterns & Trends
• Asking Data-Driven Questions

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Module 11 — Data Visualization with Matplotlib

• Introduction to Matplotlib
• Line Chart
• Bar Chart
• Histogram
• Scatter Plot
• Pie Chart
• Labels & Titles
• Legends
• Customizing Charts
• Choosing the Right Visualization

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Module 12 — Data Visualization with Seaborn

• Introduction to Seaborn
• Statistical Visualization
• Count Plot
• Bar Plot
• Box Plot
• Violin Plot
• Scatter Plot
• Heatmap
• Distribution Plot
• Relationship Analysis

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Module 13 — Statistical Analysis for Data Science

• Basic Statistics
• Central Tendency
• Dispersion
• Distribution
• Correlation
• Covariance
• Outliers
• Probability Basics
• Statistical Interpretation
• Making Data-Driven Conclusions

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Module 14 — Real-World Data Science Project

কোর্সের শেষে শিক্ষার্থীরা একটি complete Real-World Data Science Project সম্পন্ন করবে।

Project-এর মধ্যে থাকবে—

📊 Data Collection

• Dataset Understanding
• Importing Data
• Data Structure Analysis

🧹 Data Cleaning

• Missing Values
• Duplicate Records
• Data Type Issues
• Inconsistent Data

🔎 Exploratory Data Analysis

• Descriptive Statistics
• Trends
• Patterns
• Correlation
• Outlier Analysis

📈 Data Visualization

• Charts
• Graphs
• Statistical Visualizations
• Data Storytelling

💡 Final Insights

• Key Findings
• Business Insights
• Recommendations
• Final Analysis Report

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Course Features

✅ 45 Hours Live Class

✅ বাংলা + English Explanation

✅ Beginner-Friendly Python Training

✅ Python for Data Science

✅ NumPy

✅ Pandas

✅ Data Cleaning

✅ Exploratory Data Analysis

✅ Matplotlib

✅ Seaborn

✅ Statistics Fundamentals

✅ Data Visualization

✅ Real-World Dataset

✅ Practical Exercises

✅ Complete 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 Analyst Beginners
• Students
• CSE & ICT Students
• Diploma Engineering Students
• Job Seekers
• Business Analysts
• Software Developers
• Python Beginners
• Researchers
• Business Professionals
• Freelancers
• Machine Learning Beginners
• যারা Data Science Career শুরু করতে চান

আগে থেকে Python জানা বাধ্যতামূলক নয়।

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After Completing This Course

এই কোর্স শেষ করার পর আপনি—

• Python-এর প্রয়োজনীয় Programming Fundamentals বুঝতে পারবেন।
• Python দিয়ে Data Load, Process ও Analyze করতে পারবেন।
• NumPy ব্যবহার করে Numerical Data নিয়ে কাজ করতে পারবেন।
• Pandas ব্যবহার করে DataFrame তৈরি ও Manipulate করতে পারবেন।
• Missing, Duplicate ও Inconsistent Data Clean করতে পারবেন।
• বিভিন্ন Dataset Merge, Join, Group এবং Transform করতে পারবেন।
• Exploratory Data Analysis (EDA) পরিচালনা করতে পারবেন।
• Basic Statistics ব্যবহার করে Dataset-এর গুরুত্বপূর্ণ Characteristics বুঝতে পারবেন।
• Matplotlib ও Seaborn ব্যবহার করে Professional Data Visualization তৈরি করতে পারবেন।
• Correlation, Distribution এবং Outlier Analysis করতে পারবেন।
• Data থেকে Meaningful Patterns ও Insights বের করতে পারবেন।
• Real-World Data Science Project সম্পন্ন করতে পারবেন।
• পরবর্তী ধাপে Machine Learning, Advanced Data Science, Deep Learning বা AI শেখার জন্য Strong Foundation তৈরি করতে পারবেন।