Enterprise data science in practice
Whether it is fighting fraud, detecting cancer, or predicting a hurricane, you need data and AI. Join a new wave of data-savvy professionals with access to millions of jobs available in the market.
IBM SkillsBuild for Academia
This is a survey course, exposing the learner to the Data science methodology; in order to address real-life enterprise business problems.
Looking for a job?
Gain a new set of data analytics skills, complement them with low-code AI-powered technologies, and your industry knowledge, to get on your way to join a data science team, as part of a new breed of data-savvy professionals with access to millions of jobs available in the market.
Looking for a better job?
If you already have a job and even some experience with data analytics, use this course to select a specialization and advance your career.
Play different roles within a Data Science team, solve real challenges within the enterprise, and leverage AI-powered technologies.
- Data science team roles
- Data science method
- Data analysis tools
- Real-world use cases
- Understand the composition and working of a Data science team, including the different roles, processes, and tools
- Key statistics concepts and methods essential to finding structure in data and making predictions
- Internalize the data science methodology by learning to: (a) Characterize a business problem; (b) Formulate a hypothesis; (c) Demonstrate the use of methodologies in the analytics cycle; (d) Plan for execution
- Construct usable data sets by identifying and collecting the data required, and manipulating, transforming, and cleaning the data; demonstrating the ability to deal with data anomalies such as missing values, outliers, unbalanced data, and data normalization
- Hands-on experience with IBM Watson Studio, Data Refinery Spark, Jupyter Notebooks, and Python libraries
- Visualize statistical analysis, identify patterns, and effectively communicate findings to executive sponsors for business-driven decision-making.
About this course
This course is divided into three practice levels. Each level covers more advanced topics and builds up on top of the concepts, practice and skills addressed in the previous practice levels.
Level 1 — Data science method
Explore people, process and tools required to build an effective data science team.
- 1. Data science landscape
- 2. Data science on the cloud
- 3. Data science methodology
Level 2 — Data wrangling
Perform data manipulation techniques to identify patterns and extract insights.
- 1. Explore and prepare data
- 2. Explore insurance claim data (interactive case study)
- 3. Represent and transform data
- 4. Discover patterns in claims fraud (interactive case study)
Level 3 — Decision support
Leverage visualization techniques to provide business impact analysis and support.
- 1. Data visualization and presentation
- 2. Fraud diagnostic analysis (interactive case study)
Skills you will need to have before joining this course offering:
Complete the Getting started with enterprise Data science course from the Data Science Practitioner series.
Alternatively, you will need prior knowledge of the following topics:
- The relevance of Data science projects in supporting the digital transformation of business across multiple industries
- Data science cross-disciplinary skillset found at the intersection of statistics, computer programming, and domain expertise
- Roles of a Data Science team: Data scientist, Data Engineer, Data analyst, and AI developer
- Data science collaboration platforms in the cloud, including IBM Watson Studio and Data Refinery
- Data ingestion and manipulation using a CSV dataset.
Enterprise Data Science in PracticeSee badge
About this badge
This badge earner has completed all the learning activities included in this online learning experience including hands-on labs, concepts, methods, and tools related to the data science methodology. They demonstrate skills and understanding of the Data science methodology by engaging in real-world scenarios and role-playing the process/tools used by a data science team; learning example: An insurance industry scenario leveraging cutting-edge fraud analytics approaches and technologies.
Data analyst, Data Engineer, Data Refinery, Data science, Data visualization, Data wrangling, Insurance fraud, Jupyter Notebooks, PixieDust, Python libraries, Watson Studio.
- Must attend a training session at a higher education institution implementing the IBM Skills Academy program.
- Must have completed the self-paced online course activities, and knowledge checks validating understanding of the covered topics.