The IBM Data Science Professional Certificate is a comprehensive program offered by Coursera in collaboration with IBM. This certificate program is designed to equip individuals with the skills and knowledge needed to pursue a career in data science. Through a series of hands-on projects and interactive lessons, participants learn essential concepts, techniques, and tools used in data science.
- Industry-Relevant Curriculum: The course curriculum is designed in collaboration with industry experts from IBM, ensuring that participants learn the most up-to-date and relevant skills in data science.
- Hands-On Projects: Participants have the opportunity to work on real-world projects, allowing them to apply theoretical concepts to practical scenarios and build a strong portfolio.
- Flexible Learning: The course is available online through Coursera, providing flexibility for participants to learn at their own pace and schedule.
- IBM Credential: Upon completion of the program, participants receive the IBM Data Science Professional Certificate, which is recognized by employers globally and can enhance career prospects.
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- Introduction to the field of data science
- Understanding the role and importance of data scientists
- Exploring various applications and domains of data science
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- Proficiency in using essential tools for data science, such as Jupyter Notebooks, GitHub, and Watson Studio
- Understanding version control with Git and GitHub
- Familiarity with IBM Cloud services for data science projects
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- Learning the data science methodology for tackling data science projects
- Understanding the lifecycle of a data science project, including problem definition, data preparation, modeling, evaluation, and deployment
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Python for Data Science, AI & Development
- Mastery of Python programming language for data science and AI applications
- Understanding data structures, control flow, functions, and object-oriented programming in Python
- Proficiency in using libraries such as NumPy, Pandas, and scikit-learn for data manipulation and machine learning
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Python Project for Data Science
- Application of Python programming skills to real-world data science projects
- Experience in solving data science problems using Python libraries and tools
- Developing critical thinking and problem-solving skills through project-based learning
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Databases and SQL for Data Science with Python
- Proficiency in working with databases and SQL for data analysis
- Understanding database management systems (DBMS) and relational database concepts
- Learning SQL queries for data manipulation, querying, and management
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- Advanced data analysis techniques using Python
- Exploratory data analysis (EDA) methods for understanding data distributions, correlations, and patterns
- Statistical analysis and hypothesis testing using Python libraries such as SciPy and StatsModels
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Data Visualization with Python
- Mastery of data visualization techniques using Python libraries like Matplotlib, Seaborn, and Plotly
- Creating informative and visually appealing plots, charts, and graphs to communicate insights from data effectively
- Understanding principles of data visualization design and best practices
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- Understanding fundamental concepts of machine learning algorithms and techniques
- Hands-on experience in building and evaluating machine learning models using Python
- Knowledge of supervised and unsupervised learning methods, model evaluation, and hyperparameter tuning
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- Integration of knowledge and skills acquired throughout the program in a real-world data science project
- Experience in problem formulation, data collection, data cleaning, exploratory data analysis, modeling, and presentation of results
- Collaboration and teamwork in a capstone project environment
For more information and enrollment, visit the IBM Data Science Professional Certificate page on Coursera.