DATA ANALYSIS WITH PYTHON BY IBM – FREE IBM COURSES
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Data Analysis with Python by IBM |
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! You will learn how to perform data analytics in Python using
these popular Python libraries and you will do it using hands-on labs using
real Python tools like Jupyter notebook in JupyterLab.
LEARN TO ANALYZE DATA WITH PYTHON
Learn how to analyze data using Python. This course will
take you from the basics of Python to exploring many different types of data.
You will learn how to prepare data for analysis, perform simple statistical
analyses, create meaningful data visualizations, predict future trends from
data, and more!
You will learn how to:
- Import data sets
- Clean and prepare data for analysis
- Manipulate pandas DataFrame
- Summarize data
- Build machine learning models using scikit-learn
- Build data pipelines
Data Analysis with Python is delivered through lecture,
hands-on labs, and assignments. It includes following parts:
Data Analysis libraries: will learn to use Pandas
DataFrames, Numpy multi-dimentional arrays, and SciPy libraries to work with a
various datasets. We will introduce you to pandas, an open-source library, and
we will use it to load, manipulate, analyze, and visualize cool datasets. Then
we will introduce you to another open-source library, scikit-learn, and we will
use some of its machine learning algorithms to build smart models and make cool
predictions.
COURSE SYLLABUS
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 Evaluation Using Visualization
- 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
REQUIREMENTS
- Some Python experience is expected
- Python for Data Science
COURSE CODE:
DA0101EN
AUDIENCE:
Anyone who wants to use Python to analyze data
COURSE LEVEL:
Intermediate
TIME TO COMPLETE:
8 hours
BADGE:
Data Analysis with Python
GENERAL INFORMATION
- Free of Cost.
- Self-paced course.
- Life time validity.
- Can be audited as many times as you wish.
- Python programming, Statistics
Join Our Telegram Group (BBA / MBA) : Click Here
Join Our Telegram Group (Engineering) : Click Here
Join Our Telegram Group (Experienced) : Click Here
Join Our Telegram Group (Freshers) : Click Here
Join Our Telegram Group (Free Online Courses) : Click Here
DATA ANALYSIS WITH PYTHON BY IBM – FREE IBM COURSES
Reviewed by Jobs Barrel
on
May 27, 2020
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