EduCourse’s Data Science Specialized Program, developed in collaboration with Leading IITs, NITs, and Industry Partners, offers optimal practical exposure. This program integrates statistics, data analysis, and related methodologies to comprehend and analyze real-world phenomena effectively. Enroll in our Data Science Industrial Program to discover exclusive insights crucial for informed data management.
What Will I Learn ?
Industrial Training
✓ Introduction to Python Programming
✓ Importance of Industrial Training in Software Development
✓ Industry Best Practices in Python Coding
✓ Building Real-world Projects
Anaconda
✓ Introduction to Anaconda Distribution
✓ Installing Anaconda and Setting up the Environment
✓ Using Jupyter Notebooks
✓ Managing Libraries with Conda
Data Types in Python
✓ Numeric Data Types (int, float, complex)
✓ Sequence Types (list, tuple, range)
✓ Mapping Type (dictionary)
✓ Set Types (set, frozenset)
✓ Boolean and None Type
All about Strings
✓ String Declaration and Manipulation
✓ String Indexing and Slicing
✓ String Immutability
✓ Escape Characters in Strings
String Methods
✓ Common String Methods (split, join, replace, find, etc.)
✓ String Formatting (f-strings, format(), % formatting)
✓ Case Conversion Methods
✓ String Checking Methods (isalpha, isdigit, etc.)
All about Lists
✓ List Creation and Access
✓ List Slicing and Indexing
✓ List Methods (append, remove, sort, etc.)
✓ List Comprehensions
✓ Nested Lists
Control Structure and Flows in Python
✓ Introduction to Control Flow
✓ If-else Conditional Statements
✓ Loop Structures (for, while)
✓ Break and Continue Statements
Conditional Statements in Python
✓ if, elif, else Statements
✓ Nested Conditional Statements
✓ Boolean Expressions and Comparison Operators
Loops in Python
✓ While Loops
✓ For Loops
✓ Nested Loops
✓ Loop Control (break, continue, pass)
What are Functions
✓ Defining Functions
✓ Function Syntax and Return Statements
✓ Variable Scope (Local vs Global Variables)
✓ Lambda Functions
Arguments in Functions
✓ Positional Arguments
✓ Keyword Arguments
✓ Default Arguments
✓ Variable-Length Arguments (*args, **kwargs)
Decorator
✓ Understanding Decorators in Python
✓ Writing and Applying Decorators
✓ Use Cases of Decorators
✓ Function Wrapping with Decorators
Generator
✓ Introduction to Generators
✓ yield Keyword and Generator Functions
✓ Use Cases for Generators
✓ Generator Expressions
Objects and Classes
✓ Object-Oriented Programming (OOP) Concepts
✓ Defining Classes and Creating Objects
✓ Class Attributes and Methods
✓ Inheritance and Polymorphism
Files
✓ Reading and Writing Files in Python
✓ File Handling Modes (r, w, a)
✓ Working with File Paths
✓ Context Managers (with open)
Exception Handling in Python
✓ Handling Exceptions with try, except, finally
✓ Raising Custom Exceptions
✓ Common Built-in Exceptions
✓ Exception Hierarchy in Python
Python Packages
✓ Introduction to Python Packages and Modules
✓ Importing Packages and Modules
✓ Creating Custom Python Packages
✓ Popular Python Libraries (NumPy, Pandas, Matplotlib, etc.)
Introduction to SQL
✓ Basics of SQL (Structured Query Language)
✓ SQL Databases vs NoSQL Databases
✓ Common SQL Commands (SELECT, INSERT, UPDATE, DELETE)
✓ Data Definition Language (DDL) vs Data Manipulation Language (DML)
Getting started with SQL
✓ Creating and Managing Databases
✓ Writing Queries to Fetch Data
✓ Joining Tables and Relationships
✓ Using Aggregate Functions (SUM, COUNT, AVG)
Advance Internship Projects
1. Scraping a website
✓ Introduction to Web Scraping
✓ Libraries for Web Scraping (BeautifulSoup, Scrapy)
✓ Extracting Data from Jumia’s Website
✓ Storing Scraped Data in CSV/Database
2. Amazon Forest Fires Analysis using Pandas and Matplotlib
✓ Dataset Exploration with Pandas
✓ Data Cleaning and Preprocessing
✓ Visualizing Data Trends with Matplotlib
✓ Drawing Insights from the Fire Incidents
3. Fake News Detection
✓ Natural Language Processing (NLP) for Text Analysis
✓ Dataset Preprocessing (Tokenization, Stop Words Removal)
✓ Building a Classification Model (Logistic Regression, Random Forest)
✓ Evaluating Model Performance
4. Cab Fare Prediction
✓ Dataset Overview and Feature Engineering
✓ Using Regression Models (Multiple Linear Regression)
✓ Data Preprocessing (Handling Missing Data, Feature Scaling)
✓ Model Evaluation (R² Score, RMSE)
5. Santander Bank Customer Transaction Prediction using Python
✓ Data Analysis and Feature Selection
✓ Machine Learning Models for Prediction (Random Forest, XGBoost)
✓ Handling Imbalanced Data
✓ Model Tuning and Validation
Careers Opportunity
✓ Business Intelligence(BI) Developer
✓ Data Architect
✓ Application Architect
✓ Infrastructure Architect
✓ Data Scientist
✓ Data Analyst
✓ Machine Learning Scientist
