Find out how to use Python for data analysis.Learn Python data analysis techniques. You will learn the fundamentals of Python in this course and DataStructure Basics You'll discover how to organise/prepare data for analysis, perform fundamental statistical analysis, make insightful data visualisations, forecast future trends using data, and more! The details of manipulating, processing, cleaning, and crunching data in Python are the focus of Python for Data Analysis.
Data Analysis with Python
Data analysis is a technique for arranging, gathering or transforming data in order to forecast the future and make informed data-driven decisions. Data analysis also aids in the discovery of potential solutions to business problems. Data analysis covers 6 stages. 1). Ask or Specify Data Requirements 2).Prepare or Collect Data 3).Clean and Process 4).Analyze 5).Share 6),Act or Report.
Student Journey
Soon after enrolling in the course, you will be trained by professionals experienced with 10+ of experience. By the end of the course, you will be able to... understand data structure basics ,Understand python methods and functions, understand object oriented programming, data anayltics concepts ,Numpy, Pandas, Metplotlib, Seaborn and Hypothesis testing in Python.This course presents the tools you need to clean and validate data, to visualize distributions and relationships between variables, and to use regression models to predict and explain. This course is designed forDevelopers, Project Managers, and Business Analyst, Data Analyst and many more. There are no specific prerequisites for this training, anyone can get training on this course.
Course Content
- Numbers
- Arithmetic operations
- Variable assignment
- Strings : Creation, indexing, attributes, methods, print formatting
- List : Creation, importing, index, processing, types of array, ways of creation, operations, attributes, indexing, slicing.
- Dictionary : Operations, dictionary methods, sorting elements, conversions, ordered dictionary.
- Tuples : Creation, immutable type, indexing, processing, attributesTuples : Sets : Creation, methods (union, intersection)Tuples : Boolean
- Tuples: Files– Input,output,file modes.
- Comparison Operators : greater than (>), less than (<), equals(==), not equal(!=)
- Logical operators: and,or,not < /li>
- Bitwise operators: & ,| ,~ < /li>
- Pandas Series – Creation, connection with numpy, useful methods, indexing, slicing
- Pandas Dataframe– Creation,Conditional filtering < /li>
- Useful methods– apply,describe,info,corr,indexing,slicing,nlargest,duplicates,map,unique,value counts < /li>
- Missing Data < /li>
- Group By < /li>
- Text Methods < /li>
- Time Methods < /li>
- Inputs and outputs < /li>
- Pivots < /li>
