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This course Data Science with Python will introduce to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. You will apply statistics, machine learning and information visualization.
Curriculum
- 15 Sections
- 28 Lessons
- 10 Weeks
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- Introduction to Data Science, Machine Learning1
- Python14
- 3.1Python Syntax30 Minutes
- 3.2Python Collections or Sequence30 Minutes
- 3.3Python Functions30 Minutes
- 3.4Python Modules30 Minutes
- 3.5Python File handling30 Minutes
- 3.6Object Oriented Python30 Minutes
- 3.7Polymorphism30 Minutes
- 3.8Variables, Basic Operators, Decision Making, Loops30 Minutes
- 3.9Numbers, Strings, Lists, Tuples, Dictionary30 Minutes
- 3.10Python Libraries30 Minutes
- 3.11Introducing Data Frames30 Minutes
- 3.12Exceptions30 Minutes
- 3.13Python MySQL Database Access30 Minutes
- 3.14Introducing Data Frames30 Minutes
- Introduction to Descriptive Statistics1
- Summarizing data1
- . Introduction to Inferential Statistics1
- Probability1
- . Permutations and Combinations1
- Combinatorics1
- Random Variables1
- Central Limit Theorem1
- Common Distributions1
- Skewness and Kurtosis1
- Accuracy1
- Machine Learning Algorithms1
- Project Work1
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