Course HighlightsCOURSE
Data Analysis with Python

Data Analysis 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.

Data Analysis with Python Highlights

  Course duration

Duration

  • 5 weeks
    2-4 hours/week
  Course Fee

Fee

US$ 99 - US$ 199

Course duration

Duration

  • 5 weeks
    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 FutureSkills Prime/IBM course comprises five purposely designed modules that take you on a carefully defined learning journey.  

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 videos, reading material, and online exam questions.  

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 will vary across packages.  

Once you have successfully completed the course, you will earn your Certificate of Completion. You will also receive an IBM Certificate. 

After completing this course students 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.

This course is aligned with industry-approved occupational standards set by SSC NASSCOM. Once you’ve successfully completed this course, you will receive a Certificate of Completion that confirms you have:

  • Job-ready competencies
  • Practical experience
  • Assessed technical knowledge

The national occupational standards to which this course is aligned relates to the following job roles:

  • Business Intelligence Analyst
  • Data Scientist
  • Data Quality Analyst

Course Outline

General Information
Learning Objectives
Syllabus
Grading Scheme
Change log
Copyrights and Trademarks
Introduction to Data Analysis with Python 0:51
The Problem 1:51
Understanding the Data 2:26
Python Packages for Data Science 2:28
Importing and Exporting Data in Python 4:13
Getting Started Analyzing Data in Python 4:14
Lab 1:Introduction
Graded Review Questions (7 Questions) Review Question
Pre-processing Data in Python 2:09
Dealing with Missing Values in Python
Data Formatting in Python 3:23
Data Normalization in Python 3:34
Binning in Python 1:53
Turning categorical variables into quantitative variables in Python 2:00
Unit
Graded Review Questions
Exploratory Data Analysis 1:20
Descriptive Statistics 4:39
GroupBy in Python 3:20
Analysis of Variance ANOVA 3:58
Correlation 2:29
Correlation - Statistics 2:37
Lab 3
Graded Review Questions (5 Questions) Review Question
Model Development 1:44
Linear and Multiple Linear Regression 6:34
Model Evaluation using Visualization 4:44
Polynomial Regression and Pipelines 4:25
Measures for In-Sample Evaluation 3:37
Prediction and Decision Making
Lab 4
Model Evaluation and Refinement 0:22
Model Evaluation and Refinement 0:22
Model Evaluation 7:31
Overfitting, Underfitting and Model Selection 4:21
Ridge Regression 4:27
Grid Search 4:34
Module 5 Model Evaluation and Improvement
Graded Review Questions (5 Questions) Review Question
Exam Instructions
Exam (20 Questions) Timed Exam Final Exam
Retake Exam
Course Certificate

Earn your certificate

Once you have completed this course, you will earn your certificate.

Preview digital certificate
Data Analysis with Python

FAQs

Python is a very effective tool for data analysis at every stage of the process. It is particularly useful because the Python libraries were for general use and are available for download. According to Python documentation, data mining, data processing, and modelling, as well as data visualization, are the most common ways in which Python is used for data analysis.  

There are 5 modules in Data Analysis with Python.  

Yes, you will receive a Certificate of Completion once you have successfully completed this course.  

Data Analysis with Python is an online course. It is self-paced and can be completed at a pace that suits you. You will therefore need a good connection to the internet in order to be able to access the course materials. The contents for this course will be available through your dashboard as soon as you have enrolled. You will not be required to attend any classes in person to obtain the course materials. 

In contrast to scheduled live sessions or webinars, self-paced courses do not follow a predetermined timetable. If you work through the modules and complete the course by the deadline, you are free to work at your own pace; you are not required to work at a particular pace.