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Python for Data Science Course
Rated 4.5/5 based on 751 customer reviews

Python for Data Science Course

Master the versatile language for Data Science-Python

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Modes of Delivery

Online Classroom

Collaborative, enriching virtual sessions, led by world class instructors at time slots to suit your convenience.

Classroom

Our classroom training provides you the opportunity to interact with instructors and benefit from face-to-face instruction.

Team/Corporate Training

Our Corporate training is carefully structured to help executives keep ahead of rapidly evolving business environments.
Group Discount: 10.00% for 2 people 15.00% for 3 to 4 people 20.00% for 5 and above people

3 Months FREE Access to all our E-learning courses when you buy any course with us

Curriculum

Module 1- Python Basics

Learning Objectives: Learn how to install Python distribution - Anaconda
Learn basic data types, strings & regular expressions

Topics

  • Install Anaconda
  • Data Types & Variables

Hands-on: Install Anaconda - Python distribution

Module 2- String & Regular Expressions

Learning Objectives: Convert messy text into something useful. Learn to use regular expressions to utilize search methods and patterns

Topics

  • String Operations
  • Uses of Regular Expressions
  • Methods of Regular Expressions
  • Commonly used Operators

Hands-on : Write Python code to use regular expressions and implement search methods

Module 3- Data Structures in Python

Learning Objectives: Data structures that are used in Python

Topics

  • Arrays
  • Lists
  • Tuples
  • Dictionaries
  • Sets
Hands-on: Write Python Code to understand and implement Python Data Structures

Module 4- Control & Loop Statements in Python

Learning Objectives: Learn all about loops and control statements in Python

Topics

  • For Loop
  • While Loop
  • Break Statement
  • Next Statements
  • Repeat Statement
  • if, if…else Statements
  • Switch Statement

Hands-on: Write Python Code to implement loop and control structures in R

Module 5- Functions & Classes in Python

Learning Objectives: Write user-defined functions in Python. Learn about Lambda function. Learn object oriented way of writing classes & objects

Topics

  • Writing your own functions (UDF)
  • Calling Python Functions
  • Functions with Arguments
  • Calling Python Functions by passing Arguments
  • Lambda Functions

Hands-on: Write Python Code to create your own custom functions without or with arguments. Know how to call them by passing arguments wherever required

Module 6- Object Oriented Programming

Learning Objectives:  Gain knowledge on OOPs to code easily and efficiently. Learn to construt classes and define objects

Topics

  • Introduction to Python Classes
  • Defining Classes
  • Initializers
  • Instance Methods
  • Properties
  • Class Methods and Data
  • Static Methods
  • Private Methods and Inheritance
  • Module Aliases and Regular Expressions

Hands-on: Write Python code to construct a class and define objects

Module 7- Working with Data

Learning Objectives: Learn how to import datasets into Python. Also learn how to write output into files from Python

Topics

  • Reading files using Pandas library
  • Understanding function parameters
  • Writing files from Python
  • Reading files using Pandas library
  • Reading various other file types in python

Hands-on: Write Python Code to read and write data from/to Python

Module 8- Data Manipulation using Pandas

Learning Objectives: Manipulate & learn to transform raw data using Pandas library. Learn generating insights from your data

Topics

  • Clean & Prepare Datasets
  • Data Transformations
  • Encoding
Hands-on : Write Python code to manipulate data rames and churn insights using various python libraries

Module 9- Exploratory Data Analysis

Learning Objectives: Learn to summarize dataset through descriptive statistics. Use variety of measurements to better understand you data. Learn to treat missing values.Also, learn how to discover patterns in your data

Topics

  • Summarize Data
  • Statistical analysis of Data
  • Extensive Data Exploration for deeper insights
  • Missing value treatment
  • Quality Analysis
Hands-on : Write Python code to summarize dataset through descriptive statistics and treat the missing values after analysis

Module 10- Data Visualization with Matplotlib

Learning Objectives: Master in a commonly used Python graphic module, Matplotlib. Learn to create charts such as histogram, pie-chart, box plots and so on using matplotlib

Topics

  • Installing matplotlib
  • Importing matplotlib
  • Modules of matplolib: pyplot, image, animation
  • Charts using matplotlib: box plot, pie-charts, scatterplots, histogram, bar-chart
Hands-on: Write Python code using Python library: matplotlib to visualize data and represent it graphically

Module 11- Data Visualization with Seaborn

Learning Objectives: Learn how to use one of Python’s most convenient library, Seaborn for data visualization. Build charts such as violin plots, scatterplot, heat-maps and so on using Seaborn

Topics

  • Installing Seaborn
  • Importing Seaborn
  • Applications of Seaborn
  • Visualizing statistical relationships
  • Seaborn figure styles
Hands-on: Write Python code using Python library: Seaborn to visualize data and represent it graphically

Module 12- Data Visualization with ggplot

Learning Objectives: Jump into some advanced visualization technique, ggplot in Python. Understand Grammar of Graphics and apply it to create charts
Topics

  • Grammar of Graphics
  • Installing ggplot
  • Importing ggplot
  • Components of ggplot
  • Charts using ggplot: Bar charts, Scatterplots, Polar plots

Hands-on: Write Python code using ggplot to visualize data and represent it graphically

Module 13- Analyze, Visualize & Treat Missing Values

Learning Objectives: Learn to implement data analyzing methods, visualize the same and understand missing value treatment methods

Topics

  • Checking missing values
  • Visualize data after analyzing
  • Methods to impute missing values
Hands-on: Write Python code to analyze, visualize and treat missing values

Module 14- Case Study

Learning Objectives: Hands-on session on a real-life case study

Topics

  • Real-Life Case Study

Hands-on: Case Study: House Attributes and Sales Price data. Use this data to explore more. Deep Dive into advanced explorations. Analyze and Visualize missing data, treat missing data to missing value imputation. Visualize data with various libraries. Gain deep insights on your data

Projects

Covers Exploratory Data Analysis and Data Visualization

Key Features

24 hours of Instructor led Training
70+ hours of Assignments
Analyze and Visualize data with Python libraries
Generate insights from data

Our Students See All

Attended a 2 day weekend course by Knowledgehut for the CSM certification. The instructor was very knowledgeable and engaging. Excellent experience.

Attended workshop in April 2018

The CSPO Training was awesome and great. The trainer Anderson made all the concepts look so easy and simple. Using his past experience as examples to explain various scenarios was a plus. Moreover, it was an active session with a lot of participant involvement which not only made it interactive but interesting as well. Would definitely recommend this Training.

Attended workshop in July 2018

Great course. An interesting and interactive session to better understand how to succeed in formulating a business case and how to present it effectively.

Attended workshop in May 2018

The training was very interactive and engaging with the attendees.

Attended workshop in June 2018
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Jin Shi

Director at Timber creek Asset Management from Toronto, Canada
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Richard Dsouza

Business Analyst at Valtech from Bangalore, India
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Wily Salim

Services Project Engineer at Lendlease from Sydney, Australia
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Anish Maidh

Senior Project Manager at Telstra from Melbourne, Australia

Frequently Asked Questions

Python is a rapidly growing high-level programming language which enables clear programs on small and large scales. Its advantage over other programming languages such as R is in its smooth learning curve, easy readability and easy to understand syntax. With the right training Python can be mastered quick enough and in this age where there is a need to extract relevant information from tons of Big Data, learning to use Python for data extraction is a great career choice.
Our course will introduce you to all the fundamentals of Python and on course completion you will know how to use it competently for data research and analysis. Payscale.com puts the median salary for a data scientist with Python skills at close to $100,000; a figure that is sure to grow in leaps and bounds in the next few years as the demand for Python experts continues to rise.

On completing this course, you will be competent in:

  • Using Jupyter Notebooks in Anaconda
  • Creating user defined functions
  • Manipulating & analyzing data
  • Visualizing data using Python libraries like Matplotlib and Seaborn

Python programmers are in much demand and by the end of this course you will be able to land a role as a Python developer or data science expert. You would have gained knowledge on the use of data science techniques and the Python language to build applications on data statistics, and write reusable, testable and efficient code

There are no restrictions but participants would benefit if they have elementary programming knowledge and familiarity with statistics.

On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.

Your instructors are Python experts who have years of industry experience.

Any registration cancelled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of written request for refund. Kindly go through our Refund Policy for more details: http://www.knowledgehut.com/refund

KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor.

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