TechMaster - Analyzing Data with Python

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TechMaster - Analyzing Data with Python

TechMaster - 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.

Self-Paced

Mentored

BEGINNER

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This course is part of a program:

It is not possible to enroll for individual courses on this program. If you wish to take this course, please enroll for the full program.

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 dont worry, youre 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

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Earn your certificate

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

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Why Learn with SkillUp Online?

We believe every learner is an individual and every course is an opportunity to build job-ready skills. Through our human-centered approach to learning, we will empower you to fulfil your professional and personal goals and enjoy career success.

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Reskilling into tech? We’ll support you.

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Upskilling for promotion? We’ll help you.

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Cross-skilling for your career? We’ll guide you.

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Personalized Mentoring & Support

1-on-1 mentoring, live classes, webinars, weekly feedback, peer discussion, and much more.

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Practical Experience

Hands-on labs and projects tackling real-world challenges. Great for your resumé and LinkedIn profile.

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Best-in-Class Course Content

Designed by the industry for the industry so you can build job-ready skills.

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Job-Ready Skills Focus

Competency building and global certifications employers are actively looking for.

Course Offering

certificate

Type of certificate

IBM Certificate

course

About this course

06 Modules

06 Skills

includes

Includes

Discussion space

05 Hands-on labs

19 Quizzes

05 Graded quizzes

27 Videos

01 Final assignment

create

Create

Analyzing and prediction model

exercises

Exercises to explore

Importing data sets

Data wrangling

Exploratory data analysis

Model development

Model evaluation and refinement

This course has been created by

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Joseph Santarcangelo

PhD., Data Scientist at IBM

View on LinkedIn

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FAQs

Python is a high-level, open-source programming language that provides an easy-to-use approach to object-oriented programming. It is one of the most widely used languages for machine learning, as well as data science and AI as a whole. It is employed in many different projects and applications. However, Python is often the go-to language to use for machine learning because it contains many features that are helpful for working with statistics, and scientific functions.

Python's prominence in the scientific and research disciplines is due to its simple syntax and ease of usage. It is easy to understand, especially for those without a background in engineering or computing, and it's also popular for quick prototyping

Python is a high-level, open-source programming language that provides an easy-to-use approach to object-oriented programming. It is one of the most widely used languages for machine learning, as well as data science and AI as a whole. It is employed in many different projects and applications. However, Python is often the go-to language to use for machine learning because it contains many features that are helpful for working with statistics, and scientific functions.

Python's prominence in the scientific and research disciplines is due to its simple syntax and ease of usage. It is easy to understand, especially for those without a background in engineering or computing, and it's also popular for quick prototyping

Python is a high-level, open-source programming language that provides an easy-to-use approach to object-oriented programming. It is one of the most widely used languages for machine learning, as well as data science and AI as a whole. It is employed in many different projects and applications. However, Python is often the go-to language to use for machine learning because it contains many features that are helpful for working with statistics, and scientific functions.

Python's prominence in the scientific and research disciplines is due to its simple syntax and ease of usage. It is easy to understand, especially for those without a background in engineering or computing, and it's also popular for quick prototyping

Python is a high-level, open-source programming language that provides an easy-to-use approach to object-oriented programming. It is one of the most widely used languages for machine learning, as well as data science and AI as a whole. It is employed in many different projects and applications. However, Python is often the go-to language to use for machine learning because it contains many features that are helpful for working with statistics, and scientific functions.

Python's prominence in the scientific and research disciplines is due to its simple syntax and ease of usage. It is easy to understand, especially for those without a background in engineering or computing, and it's also popular for quick prototyping

TechMaster - Analyzing Data with Python

Course Offering

certificate

Type of certificate

IBM Certificate

course

About this course

06 Modules

06 Skills

includes

Includes

Discussion space

05 Hands-on labs

19 Quizzes

05 Graded quizzes

27 Videos

01 Final assignment

create

Create

Analyzing and prediction model

exercises

Exercises to explore

Importing data sets

Data wrangling

Exploratory data analysis

Model development

Model evaluation and refinement

This course has been created by

profile-image

Joseph Santarcangelo

PhD., Data Scientist at IBM

View on LinkedIn

Newsletters & Updates

Subscribe to get the latest tech career trends, guidance, and tips in your inbox.