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Courses / Tech Academy / Python for Data Science
📘 Beginner ⏱ 45 Hours

Python for Data Science

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।

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

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

Module 2 — Python Data Structures

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

Module 3 — Conditional Statements & Loops

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

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

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

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

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

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

Module 9 — Data Transformation & Analysis

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

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

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

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

Module 13 — Statistical Analysis for Data Science

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

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

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)

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।

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।