Data Science

DATA SCIENCE COURSE CONTENT

Module 01: Introduction to Python Programming

1. What is Python and brief history
2. Why Python and who use Python
3. Discussion on Python 2 and 3 Unique features of Python
4. Discussion on various IDE’s
5. Demonstration of practical use cases
6. Python use cases using data analysis

Module 02: Python Setup and Software Installations

1. Installing python
2. Setting up Python environment for development
3. Installation of Jupyter Notebook
4. How to access python course material using Jupyter. Write your first program in python

Module 03: Data types and Operations in Python

1. Python built-in functions
2. Number objects and operations
3. Variable assignment
4. String
5. Introduction
6. Operations and Functions.
7. Print formatting with strings
8. List
9. Introduction
10. Operations and Functions
11. Tuple
12. Introduction
13. Operations and Functions
14. Dictionary
15. Introduction
16. Operations and Functions
17. Sets and Boolean
18. Introduction
19. Operations and Functions
20. Object and data structures assessment test

Module 04: Types of Statements and Uses in Python

1. Introduction to Python statements
2. If, elseif and else statements Comparison operators
3. Chained comparison operators
4. Range function
5. What are loops
6. While loops
7. Useful operators
8. List comprehensions
9. Statement assessment test
10. Game challenge

Module 05: Functions and Methods

1. Introduction to Methods
2. What are various types of functions
3. Creating and calling user defined functions
4. Function practice exercises
5. Lambda Expressions
6. Map and filter
7. Nested statements and scope
8. Args and kwargs
9. Functions and methods assignment
10. Milestone Project (Making tic-tac-toe in python)

Module 06: File and Exception Handling

1. Process files using python
2. Read/write and append file object
3. File functions
4. File pointer and operations
5. Introduction to error handling
6. Try, except and finally
7. Python standard exceptions
8. User defined exceptions
9. Unit testing
10. File and exceptions assignment

Module 07: Object Oriented Programming (OOP)

1. Object oriented features
2. Implement object oriented with Python
3. Creating classes and objects
4. Creating class attributes
5. Creating methods in a class
6. Inheritance
7. Polymorphism
8. Special methods for class
9. Assignment – Creating a python script to replicate deposits and withdrawals in a bank with appropriate classes and UDFs.

Module 08: Advanced Python

1. Collections module
2. Datetime
3. Python debugger
4. Timing your code
5. Regular Expressions
6. StringIO
7. Python decorators
8. Python generators

Module 09: SQL

1. SQL Introduction
2. Data Definition Language (DDL)
3. Data Manipulation Language (DML)
4. SQL Server Summary
5. SQL Server Management Studio
6. Create a new Database
7. Queries
8. Create Table
9. Database Modelling
10. Create Tables using the Designer Tools
11. SQL Constraints
12. PRIMARY KEY
13. FOREIGN KEY
14. NOT NULL / Required Columns
15. UNIQUE
16. CHECK
17. DEFAULTAUTO INCREMENT orIDENTITY
18. ALTER TABLE
19. CRUD Operation
20. Introduction to SQL Query
21. Commands like Select, Insert, Update, Delete
22. The ORDER BY Keyword
23. SELECT DISTINCT
24. The WHERE Clause
25. Operators
26. LIKE Operator
27. IN Operator
28. BETWEEN Operator J Wildcards
29. AND & OR Operators
30. SELECT TOP Clause
31. Alias
32. Joins
33. Different SQL JOINs

Module 10: Advance Excel in Data Analysis

1. Introduction in Excel
2. Data Cleaning & Preparation
3. Formatting & Conditional Formatting
4. Lookup Function
5. Analyzing data with Pivot Tables
6. Charts
7. Data Visualization/Dashboarding using excel
8. Data Analysis using statistics

Module 11: Data Analysis with Python

1. Introduction to data analysis
2. Why Data analysis?
3. Data analysis and Artificial Intelligence Bridge and connecting it to database
4. Introduction to Data Analysislibraries
5. Data analysisintroduction assignment challenge

Module 12: Numpy

1. Introduction to Numpy arrays
2. Creating and applying functions
3. Numpy Indexing and selection
4. Numpy Operations
5. Exercise and assignment challenge

Module 13: Pandas

1. Introduction to Series
2. Introduction to DataFrames
3. Data manipulation with pandas
4. Missing data
5. Groupby
6. Operations
7. Data Input and Output
8. Pandas in depth coding exercises
9. Text data mining and processing
10. Data mining applications in Data engineering
11. File system integration with Pandas
12. Excel integration with Pandas
13. Operations on Excel using Dataframe
14. Data aggregation on Excel Data
15. Data visualization using Excel data

—– PROJECT —–

Module 14: Data Visualization with Python

1. Matplotlib
2. Plotting using Matplotlib
3. Plotting Numpy arrays
4. Plotting using object-oriented approach
5. Subplots using Matplotlib
6. Exercise and assignment challenge
7. Matplotlib attributes and functions
8. Matplotlib exercises
9. Seaborn Visualization
10. Categorical Plot using Seaborn
11. Distributional plots using Seaborn
12. Matrix plots
13. Grids
14. Heat Map
15. Seaborn Exercises

Module 15: Mathematics and Statistics

1. Variables and it’stypes
2. Formats of Data Types
3. Distribution, Correlations
4. Testing-Confidence level, Central tendency

Module 16: Machine Learning

1. Introduction to Machine learning and it’s uses in real world
2. Future of Machine learning
3. Opportunity after learning Machine Learning
4. Linear, Multiple and Logistic regression
5. Supervised learning
6. Unsupervised learning
7. Decision Tree
8. Random forest for regression
9. Evaluation methods for decision tree and random forest
10. K-means Clustering
11. Rule Mining
12. Capstone Project

Module 17: Natural Language Processing (NLP) and Artificial Neural Network

1. Introduction
2. Libraries in NLP
3. Classification of text
4. Stemming Methods
5. lemmatization techniques
6. Neural network Elements
7. Forward & Backward Propagation

Module 18: Data Visualization using Power BI

1. Comparison Between Power BI & Programming Based Data Visualization
2. Need Of Power BI
3. Types Of Data Sources Supported by Power BI for Report Development
4. How To Build Report & Dashboard in Power BI
5. How To Build Charts in Power BI
6. Data Visualization Using Power BI Features
7. Types of Graphs
8. Multiple graphs combinations
9. Multiple file formats supported in Power BI
10. Data analysis without visualization
11. Data analysis with visualization