Industry recognized certification enables you to add this credential to your resume upon completion of all courses

Need Custom Training for Your Team?
Get Quote
Call Us

Toll Free (844) 397-3739

Inquire About This Course
Veysel Kocaman, Instructor - Introduction to Python

Veysel Kocaman

Has a B.S degree in Computer Engineering and a M.S degree in Operations Research (IE&OR dual titled) from Penn State University (State College, PA, USA). He's the Head of AI and CTO at Talent Envoy, based in CA, USA. Provides hands-on consulting services in statistics, data science, operations research, simulation and machine learning to several start-ups around the globe. He designed multiple algorithms in NLP, web scraping, insurance and fintech projects, data preparation, predictive analytics, AdWorks, CRT prediction, bulk email scheduling, Dockers implementation, MySQL, Postgre, AWS EC2, code optimisation, debugging, multi criteria decision making, utilizing the API interfaces ( Bing, Diffbot, Gensim, Spacy, Google KG, Algorithmia and TextRazor), ML classification and several others..He also worked on mapping the patent details/claims to product descriptions and clustering the products using recent NLP tools such as word2vec, doc2vec, Spacy and several other semantic tools and unsupervised ML algorithms.

Instructor: Veysel Kocaman

Learn the most popular programming language of Data Science community

  • Learn why Python dominates the Data Science community around the world 
  • Learn the most powerful programming language in Machine Learning.
  • Instructor has a B.S degree in Computer Engineering and a M.S degree in Operations Research from Penn State University. He's the Head of AI and CTO at Talent Envoy.

Course Description

This course will show you: Why Python is so popular in the Data Science community How to set up the development environment (which will be Jupyter Notebook) and install the base packages for Python Learning how to use Jupyter Notebook features The basics of Python including the syntax and the commonly used commands How to write efficient and clear code

What am I going to get from this course?

Learn why Python dominates data science community around the world, practical aspects,
Python basics including the most popular interactive development environment which is called Jupyter Notebook.
Python syntax in detail and fundamental data structures and algorithms.

Python that would help you practice real world data science problems.

Prerequisites and Target Audience

What will students need to know or do before starting this course?

Even though it's not strictly required, a basic understanding of programming languages with an introduction level of data structures and algorithms, would be helpful before taking this course.

Who should take this course? Who should not?

Anyone looking for a career in data science or aspiring data scientists who want to learn the most powerful programming language in Machine Learning.


Module 1: Module 1

Lecture 1 Introduction and Setting up Development Environment

- Why Python is popular in Data Science community - Python and Anaconda distribution for easy setup - What is Jupyter Notebook (IDE) - Important libraries, packages and modules

Lecture 2 Python Basics

We're going to cover basic syntax, variable definition, simple math operations, comparison operators and type conversions.

Lecture 3 Strings in Python

We'll cover print () function, string formatting, useful string functions and getting inputs from user.

Lecture 4 Data Structures in Python

We'll cover fundamental data structures in Python such as lists, dictionaries, sets and tuples.

Lecture 5 Conditional States and Loops

We'll cover if-else conditional statements, for loops and while loops.

Lecture 6 List and Dict Comprehensions

We'll cover one of the most practical and efficient way of creating and managing the lists and dictionaries with list and dict comprehensions.

Lecture 7 Input/ output operations

We'll learn how to read and write files

Lecture 8 Functions

We'll learn how to create a Python function to carry out a specified task.

Lecture 9 Embedded Functions

We'll cover the most useful embedded functions such as all(), any (), enumerate, lambda, filter and map.

Lecture 10 Exceptions and Error Handling

We'll learn how to handle errors and special conditions in a program we write. Then we'll cover catching the certain type of errors and how to prevent our scripts from crashing.

Lecture 11 Modules

We'll learn how to create modules, importing previously written scripts and other useful Python packages.