Python For Data Analytics
Vitti offers certified project based training program in advanced Python for Data Analytics
This Python Programming Course covers both concepts and
project of Python like Data Structures & Types, writing python
scripts, sequence and file operations, Graphs & Plots, Data
Wrangling, Statistical Computation etc. This course will also offer
practical exposure to some of the important and widely used
Python Programs like Pandas, Numpy, Matplotlib, Anaconda,
Especially developed in collaboration with academia & AI Industry experts to reskill and retool working professionals towards Artificial Intelligence space.This program offers the benefits of Experienced Faculty Led Sessions: Live-Interactive Online classes and Project based Learning. Program content & structure designed in collaboration with Industry experts.
Python is a very powerful open-source language that has powerful libraries for data manipulation and analysis. For a longtime, Python has been used in scientific computing and highly quantitative domains such as Healthcare, Transportation, Retail & Finance. As of today, it is the most preferred language for Artificial Intelligence, Robotics and Web Development.
What you will learn from this course
- Installation of Python
- Interactive Shell
- User Interface or IDE , Pycharm, Eclipse, Visual Studio
- Program structure in Python
- Execution steps
- Executable or script files.
- Installation of packages
- Using Anaconda, Numpy Pandas
- Scipy, Matplotlib
- Arithmetic and Logical operations
- Date and Time
- Assignments, Expressions and prints
- If tests and conditional logic
- While and For Loops
- Iterations and Comprehensions
- Texts and Strings
- Converting Strings to numbers
- Converting numbers to strings
- Concatenation of strings
- Matplotlib package
- Interactive graphs and Image files
- Plotting graphs
- Lines and Markers
- Plotting several graphs in one figure
- Controlling the graph
- Histogram and different graphs
- Opening a file
- Closing a file
- searching inside text file
- Working with csv files
- Working with Binary files
- Filtering out missing data.
- Filling in missing data
- Finding outliers.
- Re-shaping and Pivoting
- Removing duplicates.
- Replacing values.
- Web Scraping
- Correlation & Co-variance.
- Polynomials & Liner regression.
- Statistical tests
- Files versus Database
- Visual Studio and SQl
- SQL queries in Python projects