Curriculum
- 14 Sections
- 95 Lessons
- 10 Weeks
Expand all sectionsCollapse all sections
- Python fundamentals10
- 1.1Software Installation and introduction of the interface
- 1.2Python understanding and used cases in Data science
- 1.3Python syntax and first code and some Basics
- 1.4Variables and Literals
- 1.5Python operators
- 1.6Python Datatypes in detail
- 1.7Control flow statements
- 1.8User defined function
- 1.9Lambda, Range, Map, Enumeration Zip
- 1.10Patterns understanding
- Numpy11
- 2.1Introduction to Numpy and Why to learn Numpy
- 2.2Installing and Importing
- 2.3Numpy arrays introduction
- 2.4Ways of creating Numpy arrays
- 2.5Numpy arrays attribute
- 2.6Slicing and Indexing
- 2.7Reshaping array
- 2.8Changing array dimension
- 2.9Sta s cal and mathematical function
- 2.10Random Number Generation
- 2.11Handling Missing values
- Pandas19
- 3.1Introduction to pandas and what it is used for ?
- 3.2Installation and importing
- 3.3Series vs dataframe
- 3.4Crea ng dataframe and series using List, dictionaries and numpy arrays
- 3.5Setting index
- 3.6Atributes head(), tail(), sample()
- 3.7Statistical description
- 3.8Loc and iLoc
- 3.9Renaming the column name
- 3.10Adding new column
- 3.11Dropping columns and rows
- 3.12conditional filtering using “&” and “|”
- 3.13apply function on single column
- 3.14apply function multiple column using lambda function
- 3.15sortting method
- 3.16Aggregate functions
- 3.17Value counts, unique, nunique, replace, map function
- 3.18Treatment of duplicate values
- 3.19Nlargest, nsmallest
- Matplotlib (Data Visualisation)10
- Seaborn (Data Visualisation)13
- Plotly (Data Visualisation)1
- EDA10
- Maths for ML3
- Supervised machine Learning4
- Unsupervised Machine Learning4
- Deep Learning6
- NLP0
- Transformer and Hugging Face0
- Git and Github4
Python understanding and used cases in Data science
Next