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IBM: Analyzing Data with Python

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In this course, you will learn how to analyze data in Python using multi-dimensional arrays in numpy, manipulate DataFrames in pandas, use SciPy library of mathematical routines, and perform machine learning using scikit-learn!

Analyzing Data with Python
5 semanas
2–4 horas por semana
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Comienza el 19 abr
Termina el 30 jun

Sobre este curso

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Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!

LEARN TO ANALYZE DATA WITH PYTHON

Learn how to analyze data using Python in this introductory course. You will go from understanding the basics of Python to exploring many different types of data through lecture, hands-on labs, and assignments. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!

Premios

Analizando datos con Python

De un vistazo

  • Institution IBM
  • Subject Análisis de datos
  • Level Introductory
  • Prerequisites

    Some Python Experience

  • Language English
  • Video Transcripts اَلْعَرَبِيَّةُ, Deutsch, English, Español, Français, हिन्दी, Bahasa Indonesia, Português, Kiswahili, తెలుగు, Türkçe, 中文
  • Associated programs
  • Associated skillsSciPy, Machine Learning, Scikit-learn (Machine Learning Library), Data Visualization, NumPy, Basic Math, Python (Programming Language), Data Analysis, Pandas (Python Package)

Lo que aprenderás

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  • Import data sets, clean and prepare data for analysis, summarize data, and build data pipelines
  • Use Pandas, DataFrames, Numpy multidimensional arrays, and SciPy libraries to work with various datasets
  • Load, manipulate, analyze, and visualize dataset
  • Build machine-learning models and make predictions with scikit-learn

Plan de estudios

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Module 1 - Importing Datasets

  • Learning Objectives
  • Understanding the Domain
  • Understanding the Dataset
  • Python package for data science
  • Importing and Exporting Data in Python
  • Basic Insights from Datasets

Module 2 - Cleaning and Preparing the Data

  • Identify and Handle Missing Values
  • Data Formatting
  • Data Normalization Sets
  • Binning
  • Indicator variables

Module 3 - Summarizing the Data Frame

  • Descriptive Statistics
  • Basic of Grouping
  • ANOVA
  • Correlation
  • More on Correlation

Module 4 - Model Development

  • Simple and Multiple Linear Regression
  • Model EvaluationUsingVisualization
  • Polynomial Regression and Pipelines
  • R-squared and MSE for In-Sample Evaluation
  • Prediction and Decision Making

Module 5 - Model Evaluation

  • Model Evaluation
  • Over-fitting, Under-fitting and Model Selection
  • Ridge Regression
  • Grid Search
  • Model Refinement

Este curso es parte del programa IBM Data Science Professional Certificate

Más información 
Instrucción por expertos
10 cursos de capacitación
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1 año
3 - 6 horas semanales

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