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BCT Academy
Home
About Us
Our Courses
Digital Marketing
UI/UX Design
Data Science
Web Development with Spring Boot
Data Analysis
Front-End Development Course
Mobile Application Development with React Native
Web Development with ASP.NET Core
Backend Development with MERN Stack Course
Web Development with Django
Blog
Contact
Home
Data Science
Data Science with Python Course
Data Science with Python Course
Curriculum
12 Sections
53 Lessons
12 Weeks
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Module 1
Data Types and operators
5
1.0
Python installation using either the jupyter notebook or Spyder Anaconda
1.1
Arithmetic operator Variable
1.2
Integers and Floats
1.3
Boolean Comparison operator
1.4
Strings and String methods
Module 2
Data Structures
5
2.0
Lists and membership operators List methods
2.1
Tuples
2.2
Sets Dictionary and Identity Operators
2.3
When to use Dictionaries
2.4
Compound data structures
Module 3
Control Flow
6
3.5
Conditional Statements
3.6
Boolean Expression For Conditions
3.7
For-loops Iterating through a Dictionary
3.8
While-loops
3.9
Loop Controls
3.10
List comprehensions
Module 4
Functions
4
4.0
Defining a Function
4.1
Variable Scopes
4.2
Lambda Expressions
4.3
Documentations
Module 5
Scripting
7
5.0
Configuring Git Bash for python
5.1
Running a python script
5.2
Editing a python script
5.3
Scripting with raw input
5.4
Errors and exception
5.5
Handling errors
5.6
Reading and writing files
Module 6
Object Oriented Programming
7
6.0
Classes
6.1
Abstraction
6.2
Polymorphism
6.3
Iterators
6.4
Generators
6.5
Encapsulation
6.6
Inheritance
Module 7
Numpy
4
7.0
Creating a numpy array
7.1
Accessing, Deleting and inserting into a numpy array
7.2
Slicing, Boolean indexing, set operations and sorting
7.3
Arithmetic operators and broadcasting
Module 8
Panda
5
8.0
Introduction to Pandas
8.1
Panda data types, Creating pandas series
8.2
Accessing and deleting elements in a pandas series
8.3
Arithmetic operators in a pandas series
8.4
Manipulating a pandas series
Module 9
Panda II
5
9.0
Creating pandas data frames
9.1
Accessing elements in a pandas data frame
9.2
Dealing with NaN
9.3
Manipulating a data frame
9.4
Loading data into pandas
Module 10
Machine Learning
2
10.0
Introduction to machine learning
10.1
Types of machine learning algorithms (Supervised and unsupervised)
Module 11
Machine Learning II
3
11.0
Supervised learning-Logistic regression
11.1
Decision trees
11.2
Random Forest
PROJECT
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