Python Programming with Data science

Introduction

This course provides an introduction to programming , the Python language and how to use python in Data Science. Students are introduced not only to core programming concepts like  conditionals statements, loops, variables, functions and data structures but also the various tools such as Numpy, Pandas, Matplotlib. This course includes an overview of the various tools available for writing and running Python, and gets students coding quickly. It also provides hands-on coding exercises using commonly used data structures, writing custom functions, and reading and writing to files. After completion of this course student will be able to do work as Software engineer, Data Scientist, Data Analyst.

1. Introduction to python programming

1
1. Introduction to python programming

2. Data Types in Python

1
2. Data Types in Python

3. Operators in Python

1
3.1 Arithmetic Operator, Comparision or Relational Operator, Logical Operator
2
3.2 Assigment Operator, Membership Operator, Identity Operator,Bitwise Operator

4.Python Conditional Statement

1
4.1 What is conditional statement if statement
2
4.2 elif or if – elif – else statement
3
4.3 Assignments on if,elif,else statement
4
4.4 Nested if statement

5. Python looping Statement

1
5.1 for loop
2
5.2 Range Function
3
5.3 While Loop
4
5.4 Control Statement
5
5.5 Nested for loop
6
5.6. Assignments of nested for loop

6. Python Sequence

1
6.1 List 1
2
6.2 List 2
3
6.3 Tuple
4
6.4 Set
5
6.5 Dictionary 1
6
6.6 Dictionary 2

7. Functions in python

1
7.1 Introduction to python and custom function with example
2
7.2 Arguments and its type
3
7.3 Lambda and map fuction
4
7.4 Folter and reduce function

8. File handling

1
8. File handling

9. Exception Handling

1
9. Exception Handling

10. Regular expression

1
10. Regular expression

11. Web Scrapping

1
11. Web Scrapping

12. OOps

1
12.1 Introduction to oops & its syntax
2
12.2 oops methods
3
12.3 Method overriding , encasulations, Inheritance, abstraction

13. Numpy

1
13.1 Introduction to Numpy
2
13.2 Numpy Functionality- arrange {}

14. Pandas

1
14.1 Introduction to Pandas, creating object and reading csv file
2
14.2 creation of series and dataframes
3
14.3 Working with Columns in pandas
4
14.4 Data Processing functions
5
14.5 Introduction to concate
6
14.6 Example of concate functions
7
14.7 joins using merge

15. Visualization Library

1
15. Visualization Library
2
15.2 Data Analysis
3
15.3 Matplotlib
4
15.4 Seaborn

Be the first to add a review.

Please, login to leave a review
Get course
Enrolled: 580 students
Duration: 6 weeks
Lectures: 44
Level: Intermediate

Archive

Working hours

Monday 24 Hrs Online
Tuesday 24 Hrs Online
Wednesday 24 Hrs Online
Thursday 24 Hrs Online
Friday 24 Hrs Online
Saturday 24 Hrs Online
Sunday 24 Hrs Online