Python For Data Science Online Training

Python For Data Science Online Training

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

Key Features Python and Math for Data Science Course ContentFAQs
  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

Who Are The Trainers?
Our trainers have relevant experience in implementing real-time solutions on different queries related to different topics. Spiritsofts verifies their technical background and expertise.
What If I Miss A Class?
We record each LIVE class session you undergo through and we will share the recordings of each session/class.
How Will I Execute The Practical?
Trainer will provide the Environment/Server Access to the students and we ensure practical real-time experience and training by providing all the utilities required for the in-depth understanding of the course.
If I Cancel My Enrollment, Will I Get The Refund?
If you are enrolled in classes and/or have paid fees, but want to cancel the registration for certain reason, it can be attained within 48 hours of initial registration. Please make a note that refunds will be processed within 30 days of prior request.
Will I Be Working On A Project?
The Training itself is Real-time Project Oriented.
Are These Classes Conducted Via Live Online Streaming?
Yes. All the training sessions are LIVE Online Streaming using either through WebEx or GoToMeeting, thus promoting one-on-one trainer student Interaction.

Is There Any Offer / Discount I Can Avail?
There are some Group discounts available if the participants are more than 2.
Who Are Our Customers?
As we are one of the leading Python for Data Science Training providers of Live Instructor LED training, We have customers from USA, UK, Canada, Australia, UAE, Qatar, NZ, Singapore, Malaysia, Sydney, France, Finland, Sweden, Spain, Russia Moscow, Denmark, London, England, South Africa, Switzerland, Kenya, Philippines, Japan, Indonesia, Pakistan, Saudi Arabia,  Qatar, Kuwait, Germany, Frankfurt Berlin Munich, Poland, Belarus, Belgium Brussels Netherlands Amsterdam, India and other parts of the world.

We are located in USA. Offering Online Training in Cities like New York, New jersey, Dallas, Seattle, Baltimore, Tempe, Chandler, Scottsdale, Peoria, Honolulu, Columbus, Raleigh, Nashville, Plano, Toronto, Montreal, Calgary, Edmonton, Saint John, Vancouver, Richmond, Mississauga, Saskatoon, Kingston, Kelowna, Houston, Minneapolis, Los Angeles, San Francisco, San Jose, San Diego, Washington DC, Chicago, Philadelphia, St. Louis, Edison, Jacksonville, Towson, Salt Lake City, Davidson, Murfreesboro, Atlanta, Alexandria, Sunnyvale, Santa Clara, Carlsbad, San Marcos, Franklin, Tacoma, California, Bellevue, Austin, Charlotte, Garland, Raleigh-Cary, Boston, Orlando, Fort Lauderdale, Miami, Gilbert.

Hyderabad (Ameerpet), Kukatpally, Vizag, Nellore, Lucknow, Coimbatore, Marathahalli, Electronic city , Silk board, Kakinada, Goa, Vijayawada, Bangalore, Noida, Chennai, Kolkata, Pune, Whitefield, Mumbai, Delhi NCR, Dubai, Doha, Melbourne, Brisbane, Perth, Wellington, Leeds, Manchester, Liverpool, Ireland Dublin, Oxford, Cambridge, Brighton, Cardiff, Bristol, Lithuania,  Latvia, Italy, San Marion, China Beijing, Auckland etc…

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