DATA ANALYTICS WITH PYTHON PROGRAMMING

Enquire Now

Introduction

The part of why Python has become so popular is because it is widely used among data scientists. It is one of the easiest languages to learn and has impressive libraries and works perfectly for every stage of data science.

So the short answer to the question of whether Python is good for data analysis is yes. We will discuss its pros and cons later in the article so stick around to find a more detailed explanation to the question..

Goal of Course

Data Mining from different data sources
Data Wrangling using advanced Python libraries
Data Anlaytics using pandas, numpy, SciPi data libraries
Data Visualization using matplotlib, seaborn, and plotly.

Key Modules

Core Python Programming
File Operations
Data Analysis with Pandas
Vectorizing Data in Numpy
Connecting SQL Database
Data Visualization
Basic Statistics

Introduction to Python Programming

Program Structure In Python
Introduction To Jupyter Editor
Data Types And Operations
Assignments, Expressions And Prints
If Tests And Syntax Rules
While And For Loops
Iterations And Comprehensions
Function definition and call , Function Scope
Anonymous Functions
Opening a file , Using Files

Data Analysis with Pandas

Using Series, Dataframe, Panels
Data Wrangling , Sorting And Filtering Data
Aggregate Operations
Visualization With Pandas

Vectorizing Data in Numpy

Creating Numpy Arrays
Common Operations On Matrices
Using Analytics Functions
Views And Broadcasting On Numpy Arrays
Optimizing Performance By Avoiding Loops

Connecting SQL Database

Introduction Of Sql Connectivity
Using Pymysql Library
Performing Select Query Operation On Database
Performing Data Modification Language Operation On Database

Data – Visualization

Using Matplotlib, Seaborn, Plotlly
Creating Graphs- Bar/Pie/Linechart/Histogram/Boxplot/Scatter/Density Etc)
Important Packages For Exploratory Analysis(Numpy Arrays,Matplotlib, Pandas And Scipy.Stats Etc

Basic Statistics

Basic Statistics - Measures Of Central Tendencies And Variance
Building Blocks - Probability Distributions - Normal Distribution -Central Limit Theorem
Inferential Statistics -Sampling - Concept Of Hypothesis Testing

Note :you can speak to our team for detailed content and available batch timings.