Data Science is one of the hottest professions of the decade, and the demand for data scientists who can analyze data and communicate results to inform data driven decisions has never been greater. This Specialization from IBM will help anyone interested in pursuing a career in data science by teaching them fundamental skills to get started in this in-demand field.
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Data Science is one of the hottest professions of the decade, and the demand for data scientists who can analyze data and communicate results to inform data driven decisions has never been greater. This Specialization from IBM will help anyone interested in pursuing a career in data science by teaching them fundamental skills to get started in this in-demand field.
Data Science is one of the hottest professions of the decade, and the demand for data scientists who can analyze data and communicate results to inform data driven decisions has never been greater. This Specialization from IBM will help anyone interested in pursuing a career in data science by teaching them fundamental skills to get started in this in-demand field.
The specialization consists of 5 self-paced online courses that will provide you with the foundational skills required for Data Science, including open source tools and libraries, Python, Statistical Analysis, SQL, and relational databases. You’ll learn these data science pre-requisites through hands-on practice using real data science tools and real-world data sets.
Upon successfully completing these courses, you will have the practical knowledge and experience to delve deeper in Data Science and work on more advanced Data Science projects.
No prior knowledge of computer science or programming languages required.
This program is ACE® recommended—when you complete, you can earn up to 8 college credits.
What's inside
One course
Statistics for Data Science with Python
Statistics for Data Science with Python
Learning objectives
- Working knowledge of data science tools such as jupyter notebooks, r studio, github, watson studio
- Python programming basics including data structures, logic, working with files, invoking apis, and libraries such as pandas and numpy
- Statistical analysis techniques including descriptive statistics, data visualization, probability distribution, hypothesis testing and regression
- Relational database fundamentals including sql query language, select statements, sorting & filtering, database functions, accessing multiple tables
- Working knowledge of data science tools such as jupyter notebooks, r studio, github, watson studio
- Python programming basics including data structures, logic, working with files, invoking apis, and libraries such as pandas and numpy
- Statistical analysis techniques including descriptive statistics, data visualization, probability distribution, hypothesis testing and regression
- Relational database fundamentals including sql query language, select statements, sorting & filtering, database functions, accessing multiple tables
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