Python Online training is designed to make students familiar with the concepts, tools and programming skills which will be used for Data science main course.
The Concepts includes Introduction to Python, Python for Data Science, Data Visualization in Python, Data Analysis using SQL and Math for Data Analysis
- 30 hours of Instructor Training Classes
- 24/7 Support
- The course materials include:
- Concept reference chapters.
- Practical which include suggested homework assignments.
- Slide deck for fast review.
- Lifetime Access to Recorded Sessions
- Practical Approach Real World use cases and Scenarios
- Expert & Certified Trainers
What does the course cover?
Python and Math for Data Science course includes five modules which is divided into multiple sessions and sub-sessions depends on 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 Dataframes
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
