Data Analysis with Python
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Course
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.
Self-Paced
Mentored
Beginner
Duration
2-4 hours/week
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 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 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.
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.
This course is aligned with industry-approved occupational standards set by SSC NASSCOM. Once you've successfully completed this course, you will receive a cobranded FutureSkills Prime/SkillUp Online Certificate of Participation 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