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What Will You Learn - Curriculum
1. Introduction to Python
a. What is Python?
b. Where and how it is used?
c. Why Python over others?
d. Flavors of Python
e. Version 3.x
2. Installation & Environment Setup
a. Introduction to PYPI
b. Introduction to Pep8
c. Introduction to IDE
d. (Jupiter-notebook/Pycharm)
3. Datatypes & it’s methods
a. numbers
4. Program Flow Control
5. Functions
a. In built functions
b. Function definition & function calls
i. int
ii. float
iii. complex
i. for
ii. while
b. string
c. bool
d. tuple
e. list
f. set
g. dict
a. Branching statements
i. if
ii. elif ladder
iii. else
b. Loops
c. Positional Arguments
d. Default ArgumentsNumber of arguments
e. Keyword arguments
f. Anonymous function
6. Generators
a. What is generator?
b. Implementing generator
c. Advantages of generator
7. Decorators
a. What is decorators?
b. Implementing Decorators
c. Advanatages of decorator
8. Modules & Packages
a. What is a module?
b. How to use Builtin modules?
c. How to create user defined modules?
d. How to use Third party modules?
9. Object Oriented Paradigms
a. What is a class?
b. What is an object?
c. How to a create class & its objects??
d. Pillars of OOP
i. Encapsulation
ii. Inheritance
iii. Polymorphism
iv. Abstratction
10. Exception Handling
a. What is an error?
b. What is an Exception?
c. How to handle it?
11. File Handling
a. Introduction to file system
b. How to create a file?
12. Database
a. Intro to database
b. Advantages of database
c. What is MySql?
d. What is SQL?
e. Database connectivity in Python
f. Performing CRUD operations
13. Logging
a. Error logging
b. Logging user activities
c. Different modes to create a file system
d. Different formats of file system
14. Regular Expressions
a. Introduction to regular expressions
b. Implementing RegEx
15. JSON
a. What is JSON?
b. Adavantages of using JSON
c. Implementing JSON
16. Cron Jobs
a. Introduction to System Automation
b. Setting up CRON Jobs
17. ORM (Object Relational Mapping)
a. Introduction to ORM
b. Advantages of using ORM
c. Implementing ORM
18. Unit Testing
a. What is unit Testing?
b. Implementing unit testing
1. Introduction to Data Science
a. What is Data Science
b. What does data science involves
c. Life cycle of Data Science
d. Tools of Data Science
2.Python environment Setup and Essentials
a. Introduction to python tools for Data Science
b. Software installation
3. Mathematical Computing with Python
(Numpy)
a. Introduction to Numpy
b. Introduction to numpy arrays
c. How to Access Array Elements?
d. Indexing, Slicing, Iteration, Indexing with
Boolean Arrays
e. Dealing with Flat files using numpy
f. Mathematical functions
g. Statistical functions (mean, median, average,
standard deviation)
h. Operations with arrays
4. Data Manipulation with Pandas
a. Introduction to Pandas
b. Defining data structures
c. Understanding Dataframes
d. Importing Data from various sources
e. (Csv, txt, excel etc)
f. Handling Missing valuesData Operations
g. File read operations
h. Descriptive statistics
5. Data Visualization using Matplotlib
a. Create plots like scatter plot, histogram,
bar graph, pie chart using Matplotlib
b. Grids, axes, plots Markers, colour, fonts and
styling.
6. Machine learning using scikit-learn
a. Machine learning Process Life cycle
b. Machine learning categories
c. Feature selection and extraction in
machine learning
d. Supervised learning algorithms
e. Regression
f. Simple linear Regression
g. Applications of linear regression
h. Building regression models using python
i. Process to implement linear regression
j. Coefficient of determination (R- Squared)
k. Accuracy of model
l. Multiple linear Regression
m. Classification
n. Logistic Regression
o. Building Logistic Regression Model
p. Understanding standard model metrics
q. Validation of Logistic Regression Models
r. Standard Business Outputs
s. Decision Tree
t. Random Forest
u. Support Vector Machines
v. K – NN
w. Naïve Bayes classifier
x. Model evaluation techniques –
concepts of confusion matrix,
y. threshold evaluation with ROCR
z. Unsupervised machine learning algorithms
aa. K-Means Clustering
bb. Hierarchical Clustering
7. Web Scraping in Python
a. Parsing HTML and XML
b. Navigating the document
c. Handling CSV files
d. Parsing JSON into Python
Note : For Detailed Syllabus Download our course brochure*
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Study App/Material
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Mr.Sandeep Sir
10+ years Experienced Professional With a burning passion for coding and 10+ years of experience as a skilled Fullstack Java developer, I am thrilled to be your Fullstack Java Trainer. Throughout my journey in software development, I have honed my expertise in Fullstack Java programming and now, as a dedicated instructor, I am eager to share my knowledge with aspiring developers like you.
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