Data Analysis with Python

• Python as Calculator

  • Numbers
  • Arithmetic operations
  • Variable assignment

• Data Structure Basics

  • 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.

• Python Comparison Operators

  • Comparison Operators : greater than (>), less than (<), equals(==), not equal(!=)
  • Logical operators: and,or,not < /li>
  • Bitwise operators: & ,| ,~ < /li>

• Python Statements

  • If, elif, else
  • b.For loop
  • While loop
  • While loop
  • List comprehension

• Methods and Functions

  • Methods
  • Functions :Map, filter, reduce
  • Nested statements and scope < /li>
  • Args and kwargs
  < /ul>

• Object Oriented Programming

  • Class & Objects
  • Inheritance
  • Polymorphism < /li>
  • Special Methods – len, str, repr, add
  < /ul>

• Modules and Packages :Subpackage

• Numpy – Creation, methods, broadcasting, filtering, reshaping

• Pandas

  • 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>

• Matplotlib

  • Basics, Figures, Subplots, Styling-plots

• Seaborn

  • Scatterplot, Distribution plot, categorical plots, comparison plot, grids, facets, matrix plots

• Hypothesis testing in Python

  • T-test, chi2, correlation, anova

More about Data Analysis with Python

Gain the career-building Data Analysis with Python skills you need to succeed. In this data science and analytics with python course, you'll learn how to integrate, manage, and visualize data - all skills that any aspiring data professional or researcher needs. You'll master some of the most well-known Python libraries through interactive, hands-on exercises, including Numpy, pandas, and many more. Additionally, you'll also gain experience with real-world datasets. Sign up for this course today, grow your Data Analysis with Python skills, and begin your journey to becoming a competent data scientist.

Course FAQs

What you'll learn in this Data Analysis with Python Course?

• Describe Python data acquisition and analysis techniques

• Predictive Modeling.

• Learn how to use vectorized operations, Pandas Series, and Numpy arrays.

• Application of Python libraries with one-dimensional \and two-dimensional data.

• Create data groups from different files, then combine them.

• Explore the sample of datasets using Numpy and Matplotlib.

Who is this Data Analysis with Python course for?

• Python beginners

• Data Scientists

• Programmers

• Anyone who wants to use Python for data analysis and visualization.

Are there any Prerequisites for taking Data Analysis with Python training?

To take this course, you should be comfortable programming Python and familiar with concepts like classes, objects, and modules.

Why take this Data Analysis with Python Course?

• A recent study predicts that the big data and business analytics market will increase by $680 billion by 2030.

• According to ZipRecruiter, the average salary for professionals with Data Science and Python skills is nearly $101,023 in the United States.

• Therefore, enrolling in this course is a solid initial step toward comprehending Python's data analysis workflow.