DATA SCIENCE COURSE CONTENT
Module 01: Introduction to Python Programming
- What is Python and brief history
- Why Python and who use Python
- Discussion on Python 2 and 3 Unique features of Python
- Discussion on various IDE’s
- Demonstration of practical use cases
- Python use cases using data analysis
Module 02: Python Setup and Software Installations
- Installing python
- Setting up Python environment for development
- Installation of Jupyter Notebook
- How to access python course material using Jupyter. Write your first program in python
Module 03: Data types and Operations in Python
- Python built-in functions
- Number objects and operations
- Variable assignment
- String
- Introduction
- Operations and Functions.
- Print formatting with strings
- List
- Introduction
- Operations and Functions
- Tuple
- Introduction
- Operations and Functions
- Dictionary
- Introduction
- Operations and Functions
- Sets and Boolean
- Introduction
- Operations and Functions
- Object and data structures assessment test
Module 04: Types of Statements and Uses in Python
- Introduction to Python statements
- If, elseif and else statements Comparison operators
- Chained comparison operators
- Range function
- What are loops
- While loops
- Useful operators
- List comprehensions
- Statement assessment test
- Game challenge
Module 05: Functions and Methods
- Introduction to Methods
- What are various types of functions
- Creating and calling user defined functions
- Function practice exercises
- Lambda Expressions
- Map and filter
- Nested statements and scope
- Args and kwargs
- Functions and methods assignment
- Milestone Project (Making tic-tac-toe in python)
Module 06: File and Exception Handling
- Process files using python
- Read/write and append file object
- File functions
- File pointer and operations
- Introduction to error handling
- Try, except and finally
- Python standard exceptions
- User defined exceptions
- Unit testing
- File and exceptions assignment
Module 07: Object Oriented Programming (OOP)
- Object oriented features
- Implement object oriented with Python
- Creating classes and objects
- Creating class attributes
- Creating methods in a class
- Inheritance
- Polymorphism
- Special methods for class
- Assignment – Creating a python script to replicate deposits and withdrawals in a bank with appropriate classes and UDFs.
Module 08: Advanced Python
- Collections module
- Datetime
- Python debugger
- Timing your code
- Regular Expressions
- StringIO
- Python decorators
- Python generators
Module 09: SQL
- SQL Introduction
- Data Definition Language (DDL)
- Data Manipulation Language (DML)
- SQL Server Summary
- SQL Server Management Studio
- Create a new Database
- Queries
- Create Table
- Database Modelling
- Create Tables using the Designer Tools
- SQL Constraints
- PRIMARY KEY
- FOREIGN KEY
- NOT NULL / Required Columns
- UNIQUE
- CHECK
- DEFAULTAUTO INCREMENT orIDENTITY
- ALTER TABLE
- CRUD Operation
- Introduction to SQL Query
- Commands like Select, Insert, Update, Delete
- The ORDER BY Keyword
- SELECT DISTINCT
- The WHERE Clause
- Operators
- LIKE Operator
- IN Operator
- BETWEEN Operator J Wildcards
- AND & OR Operators
- SELECT TOP Clause
- Alias
- Joins
- Different SQL JOINs
Module 10: Advance Excel in Data Analysis
- Introduction in Excel
- Data Cleaning & Preparation
- Formatting & Conditional Formatting
- Lookup Function
- Analyzing data with Pivot Tables
- Charts
- Data Visualization/Dashboarding using excel
- Data Analysis using statistics
Module 11: Data Analysis with Python
- Introduction to data analysis
- Why Data analysis?
- Data analysis and Artificial Intelligence Bridge and connecting it to database
- Introduction to Data Analysislibraries
- Data analysisintroduction assignment challenge
Module 12: Numpy
- Introduction to Numpy arrays
- Creating and applying functions
- Numpy Indexing and selection
- Numpy Operations
- Exercise and assignment challenge
Module 13: Pandas
- Introduction to Series
- Introduction to DataFrames
- Data manipulation with pandas
- Missing data
- Groupby
- Operations
- Data Input and Output
- Pandas in depth coding exercises
- Text data mining and processing
- Data mining applications in Data engineering
- File system integration with Pandas
- Excel integration with Pandas
- Operations on Excel using Dataframe
- Data aggregation on Excel Data
- Data visualization using Excel data
—– PROJECT —–
Module 14: Data Visualization with Python
- Matplotlib
- Plotting using Matplotlib
- Plotting Numpy arrays
- Plotting using object-oriented approach
- Subplots using Matplotlib
- Exercise and assignment challenge
- Matplotlib attributes and functions
- Matplotlib exercises
- Seaborn Visualization
- Categorical Plot using Seaborn
- Distributional plots using Seaborn
- Matrix plots
- Grids
- Heat Map
- Seaborn Exercises
Module 15: Mathematics and Statistics
- Variables and it’stypes
- Formats of Data Types
- Distribution, Correlations
- Testing-Confidence level, Central tendency
Module 16: Machine Learning
- Introduction to Machine learning and it’s uses in real world
- Future of Machine learning
- Opportunity after learning Machine Learning
- Linear, Multiple and Logistic regression
- Supervised learning
- Unsupervised learning
- Decision Tree
- Random forest for regression
- Evaluation methods for decision tree and random forest
- K-means Clustering
- Rule Mining
- Capstone Project
Module 17: Natural Language Processing (NLP) and Artificial Neural Network
- Introduction
- Libraries in NLP
- Classification of text
- Stemming Methods
- lemmatization techniques
- Neural network Elements
- Forward & Backward Propagation
Module 18: Data Visualization using Power BI
- Comparison Between Power BI & Programming Based Data Visualization
- Need Of Power BI
- Types Of Data Sources Supported by Power BI for Report Development
- How To Build Report & Dashboard in Power BI
- How To Build Charts in Power BI
- Data Visualization Using Power BI Features
- Types of Graphs
- Multiple graphs combinations
- Multiple file formats supported in Power BI
- Data analysis without visualization
- Data analysis with visualization