Data Analysis with Python Certification Course

The Data Analysis with Python Certification teaches the fundamentals of data analysis using Python. With this certification, you'll learn how to read data from sources like CSV and SQL, and how to process and visualize data using libraries like Numpy, Pandas, Matplotlib, and Seaborn. Data analytics techniques enable companies to use raw data to uncover patterns and derive valuable insights.  Data analysis helps companies make informed decisions, develop more effective marketing strategies, improve customer experience, streamline operations, and more.

The Data Analysis with Python Certification teaches the fundamentals of data analysis using Python. With this certification, you'll learn how to read data from sources like CSV and SQL, and how to process and visualize data using libraries like Numpy, Pandas, Matplotlib, and Seaborn.

Data Analytics using Python Data

                  Day 1

• Introduction to Python

  • Variables, Operators, Input/output,

  • If, If- else, Nested if-else

  • Looping, For, While,

  • Uses of Break & Continue

  • Pass, Strings

  • String Operations

• Defining Lists and tuples

  • Accessing list

  • Operations in Lists

  • using tuples operations in tuples

• Function and Methods

  • Defining a function

  • Calling a function

  • Types of functions

  • Function Arguments

  • Anonymous functions

  • Global and local variables

  • Using Optional and Named Arguments

  • Using type, str, dir, and Other Built-In Functions

• sets

  • Defining sets

  • set operators

  • Frozen sets

• Dictionaries

  • Introducing Dictionaries

  • Defining Dictionaries

  • Modifying Dictionaries

  • Deleting Items from Dictionaries

     

    Day 2

• Exception Handling

  • Except clause

  • Try finally clause

• Python Libraries

  • Working with Pandas, Numpy

  • Data wrangling with Pandas

  • Data cleaning with Python

  • Data visualization using matplotlib, seaborn

• Introduction data analytics

  • Visualization and Exploring Data

  • Descriptive Statistical Measures

  • Probability Distribution and Data

  • Sampling and Estimation

  • Statistical Interfaces

  • Predictive modelling and analysis

  • Forecasting Techniques

  • Optimization, Linear regression, Multiple linear regression, Nonlinear Regression (Logistic Regression)

    Day 3

• Regression Analysis

  • Probability Estimation.

  • Overfitting, underfitting and Its Avoidance

  • Generalization,

  • Hypothesis testing using ANOVA

  • Performance of classifiers and regressors