Course HighlightsCOURSE
Analyzing Data with Python

Analyzing Data with Python

Learn how to analyze data using Python. Discover how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, and predict future trends from data.

Build your competence in this critical skill and kick-start your career in data science.

Analyzing Data with Python Highlights

  Course duration

Duration

  • 5 weeks, online
    2-4 hours/week
  Course Fee

Fee

US$ 99 - US$ 199

Course duration

Duration

  • 5 weeks, online
    2-4 hours/week
Course Fee

Fee

US$ 99 - US$ 199

The power of data science is enabling businesses to glean crucial insights from large pools of information. To explore, analyze, and manipulate this data quickly and accurately, data scientists require excellent knowledge of languages such as Python. With this knowledge, they can then develop one of the most sought-after skill sets.

In this course you will acquire key data analysis skills for predicting future trends using Python. You will explore how to import data sets, clean and prepare data for analysis, summarize data, and build data pipelines. You will use Pandas DataFrames, NumPy multidimensional arrays, and SciPy libraries to work with various datasets. You will load, manipulate, analyze, and visualize datasets, and build machine-learning models to make predictions with scikit-learn.

Learning to analyze data with Python is a critical competence for individuals keen to excel in the world of data science. This course will give you an excellent foundation in using Python for data science, and also enable you to take another step towards gaining an IBM Data Science Professional Certificate.

This course comprises six purposely designed modules that take you on a carefully defined learning journey. If you are thinking about taking the course separately, it is worth noting that it is part of the IBM Data Science Professional Certificate Program and you may want to consider enrolling for the whole program rather than just enrolling for one course at a time.

It is a self-paced course, which means it is not run to a fixed schedule with regard to completing modules or submitting assignments. To give you an idea of how long the course takes to complete, it is anticipated that if you work 2-4 hours per week, you will complete the course in 5 weeks. However, as long as the course is completed by the end of your enrollment, you can work at your own pace. And don’t worry, you’re not alone! You will be encouraged to stay connected with your learning community and mentors through the course discussion space.

The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include discussion space, videos, reading material, quizzes, hands-on labs, quizzes and final assignment.

Once you have successfully completed the course, you will earn your IBM Certificate.

As part of our mentoring service you will have access to valuable guidance and support throughout the course. We provide a dedicated discussion space where you can ask questions, chat with your peers, and resolve issues. Depending on the payment plan you have chosen, you may also have access to live classes and webinars, which are an excellent opportunity to discuss problems with your mentor and ask questions. Mentoring services may vary package wise.

You will be able to:

  • Import data sets, clean and prepare data, and summarize data.
  • Build data pipelines.
  • Use Pandas DataFrames.
  • Use Numpy multidimensional arrays.
  • Use SciPy libraries.
  • Load, manipulate, analzye, and visualize datasets with pandas.
  • Build machine learning models.
  • Make predictions with scikit-learn.
  • Individuals looking to learn how to work with different kinds of data.
  • Individuals wanting to perform analysis on data.
  • Individuals wanting an introduction to Python for data science.

    There are no prerequisites for this course.

Course Outline

Course Overview
Pre-reqs
Changelog
Syllabus
Grading Scheme
Copyrights and Trademarks
Learning Objectives
The Problem (1:56)
Understanding the Data (2:31)
Practice Quiz: Understanding the Data
Python Packages for Data Science (2:33)
Practice Quiz: Python Packages for Data Science
Importing and Exporting Data in Python (4:18)
Practice Quiz: Importing and Exporting Data in Python
Getting Started Analyzing Data in Python (4:19)
Practice Quiz: Getting Started Analyzing Data in Python
Accessing Databases with Python (4:07)
Hands-on Lab: Importing Data Sets
Graded Quiz: Importing Data Sets
Learning Objectives
Pre-processing Data in Python (2:14)
Dealing with Missing Values in Python (6:02)
Practice Quiz: Dealing with Missing Values in Python
Data Formatting in Python (3:28)
Practice Quiz: Data Formatting in Python
Data Normalization in Python (3:39)
Practice Quiz: Data Normalization in Python
Binning in Python (1:53)
Turning Categorical Variables into Quantitative Variables in Python (2:05)
Practice Quiz: Turning Categorical Variables into Quantitative Variables in Python
Hands-on Lab: Data Wrangling
Graded Quiz: Data Wrangling
Learning Objective
Exploratory Data Analysis (1:24)
Descriptive Statistics (4:44)
Practice Quiz: Descriptive Statistics
GroupBy in Python (3:26)
Practice Quiz: GroupBy in Python
Correlation (2:33)
Practice Quiz: Correlation
Correlation - Statistics (2:42)
Practice Quiz: Correlation - Statistics
Hands-on Lab: Exploratory Data Analysis
Graded Quiz: Exploratory Data Analysis
Learning Objectives
Model Development (1:49)
Linear Regression and Multiple Linear Regression (6:34)
Practice Quiz: Linear Regression and Multiple Linear Regression
Model Evaluation using Visualization (4:49)
Practice Quiz: Model Evaluation using Visualization
Polynomial Regression and Pipelines (4:30)
Practice Quiz: Polynomial Regression and Pipelines
Measures for In-Sample Evaluation (3:41)
Practice Quiz: Measures for In-Sample Evaluation
Prediction and Decision Making (5:08)
Hands-on Lab: Model Development
Graded Quiz: Model Development
Learning Objectives
Model Evaluation and Refinement (7:35)
Practice Quiz: Model Evaluation
Overfitting, Underfitting and Model Selection (4:21)
Practice Quiz: Overfitting, Underfitting and Model Selection
Reading: Ridge Regression Introduction
Ridge Regression (4:27)
Practice Quiz: Ridge Regression
Grid Search (4:34)
Hands-on Lab: Model Evaluation and Refinement
Graded Quiz: Model Refinement
Introduction
Guidelines for Submission
Peer-Graded Assignment
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Course Certificate

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Analyzing Data with Python