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In this course, you will :
- Knowledge of Data Science Tools such as Jupyter Notebooks, R Studio, GitHub, and Watson Studio is required.
- Python programming fundamentals such as data structures, logic, working with files, invoking APIs, and libraries such as Pandas and Numpy are covered.
- Descriptive statistics, data visualisation, probability distribution, hypothesis testing, and regression are all examples of statistical analysis techniques.
- SQL query language, select statements, sorting and filtering, database functions, and accessing multiple tables are all fundamentals of relational databases.
Syllabus :
- Tools for Data Science
- Python for Data Science, AI & Development
- Python Project for Data Science
- Statistics for Data Science with Python
- Databases and SQL for Data Science with Python
