What does the course cover?
Python and Math for Data Science course include five modules which are divided into multiple sessions and sub-sessions depends on the complexity of concept:
Module 1: Introduction to Python
Session 1: Data Structures in Python
1.1: Inrtoduction_installtion of Python and NoteBook
1.2: Basics
1.3: Lists
1.4: Tuples
1.5: Dictionaries
1.6: Sets
Session 2: Control Structures and Functions
2.1: If, else, if-else
2.2: Loops
2.3: List, Dictionary comprehensions
2.4: Functions
2.5: Map, Filter and Reduce
Module 2: Python for Data Science
Session 1: Introduction to NumPy
1.1-NumPy Basics
1.2-Creating NumPy Arrays
1.3-Structure and Content of Arrays
1.4-Subset, Slice, Index, and Iterate through Arrays
1.5-Multidimensional Arrays
1.6-Computation Times in NumPy and Standard Python Lists
Session 2: Operations on NumPy Arrays
2.1-Basic Operations
2.2-Operations on Arrays
2.3-Basic Linear Algebra Operations
Session 3: Introduction to Pandas
3.1-Pandas Basics
3.2-Indexing and Selecting Data
3.3-Merge and Append
3.4-Grouping and Summarizing Data frames
3.5-Lambda function & Pivot tables
Session 4: Getting and Cleaning Data
4.1-Reading Delimited and Relational Databases
4.2-Reading Data From Websites
4.3-Getting Data From APIs
4.4-Reading Data From PDF Files
4.5-Cleaning Datasets
Module 3: Data Visualisation in Python
Session 1: Basics of Visualisation
1.1-Data Visualisation Toolkit
1.2-Components of a Plot
1.3-Sub-Plots
1.4-Functionalities of Plots
Session 2: Plotting Data Distributions
2.1-Univariate Distributions
2.2-Univariate Distributions – Rug Plots
2.3-Bivariate Distributions
2.4-5-Bivariate Distributions – Plotting Pairwise Relationships
Session 3: Plotting Categorical and Time-Series Data
3.1-Plotting Distributions Across Categories
3.2-Plotting Aggregate Values Across Categories
3.3-Time Series Data
Module 4: Data Analysis using SQL
Session 1: Basics of SQL
1.1-An introduction to RDBMS and SQL
1.2-Basics of SQL
1.3-Data Retrieval with SQL
1.4-Compound Functions and Relational Operators
1.5-Pattern Matching with Wildcards
1.6-Basics of Sorting
1.7-Session Summary
Session 2: Advanced SQL
2.1-Order by Clause
2.2-Aggregate Functions
2.3-Group by Clause
2.4-Having Clause
2.5-Nested Queries
2.6-Inner Join
2.7-Multi-Join
2.8-Outer Join
Session 3: SQL Practice Questions
3.1-SQL Practice
Module 5: Math for Data Analysis
Session 1: Vectors and Vector Spaces
1.1-Introduction to Linear Algebra
1.2-Vectors The Basics
1.3-Vector Operations
1.4-Vector Spaces
Session 2: Linear Transformation And Matrices
2.1-Matrices The Basics
2.2-Matrix Operations
2.3-Representing Linear Transformations As Matrices
2.4-Linear Independence
2.5-Determinants
2.6-Inverse of a Matrix
2.7-Hands-on Exercises on Linear Transformations
Session 3: Eigenvalues And Eigenvectors
3.1-Eigenvectors What Are They
3.2-Calculating Eigenvalues
3.3-Application of Eigenvalues and Eigenvectors
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