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Description
Python has been widely accepted as the programming language of choice for high-level data processing because of its no-mess syntax, easy readability, and easy comprehension. Its numerous data structures, classes, nested functions, iterators, the flexible function calling syntax and extensive libraries make it apt for data analyzing, extracting information and making informed business decisions.
According to a survey by Coding Dojo which analyzed job postings on several career websites, Python is, without argument, the most in-demand coding language in America. The popularity of data science in driving business has pushed the demand for Python experts and programmers. Keeping this in mind we have designed a Python course for you which will help you master the language and use it to develop powerful data statistics applications.
This course will walk you through Python basic programming and usage of critical libraries like Numpy, Pandas, Matplotlib, Seabon, and ggplot. Python for Data Science course prepares you with Python Programming capabilities for Data Manipulation, Exploratory Data Analysis and Data Visualization which are an absolute must for being a Data Science expert. Our intensive prep will help you clear any interview and land coveted positions. Python is a disruptive technology and mastering it can ensure that you land a lucrative job in the field of data science.
What You Will Learn
1. Basics of Python
Learn to install Anaconda and Python distribution
2. Python fundamentals
Explore Python language fundamentals, including basic syntax, variables, and types
3. Data Structures
Learn about the different data structures Python can handle. Create and manipulate regular Python lists, tuple etc.
4. Conditional Statements
Learn about control and loops statements
5. Object-Oriented Programming
User defined function, an object-oriented way of writing classes/objects, use functions, import packages
6. Analyzing Data
Manipulate and analyze a dataset in Python using Pandas
7. Python for advanced analysis
Build Numpy arrays, and perform interesting calculations
8. Data Visualization
Use various Python libraries to visualize data. Create and customize plots on real data
Prerequisites
Anybody who is a Data Science aspirant with coding or non-coding background
3 Months FREE Access to all our E-learning courses when you buy any course with us
Who should Attend?
- Those interested in data science who want to learn essential skills
- Those new to Python and looking for a more robust, structured learning program
- Software or Data Engineers interested in learning Python for Data Science
KnowledgeHut Experience
Instructor-led Live Classroom
Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.
Curriculum Designed by Experts
Our courseware is always current and updated with the latest tech advancements.Stay globally relevant and empower yourself with the training.
Learn through Doing
Learn theory backed by practical case studies, exercises, and coding practice. Get skills and knowledge that can be effectively applied.
Mentored by Industry Leaders
Our support team will guide and assist you whenever you require help.
Advance from the Basics
Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.
Code Reviews by Professionals
Get reviews and feedback on your final projects from professional developers.
Curriculum
Learning Objectives:
Learn how to install Python distribution - Anaconda, the basic data types, strings & regular expressions.
Topics Covered:
- Install Anaconda
- Data Types & Variables
Hands-on:
Install Anaconda - Python distribution.
Learning Objectives:
In this module you will learn to convert messy text into something useful. You will also learn to use regular expressions to utilize search methods and patterns.
Topics Covered:
- 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.
Learning Objectives:
Learn about the Data structures that are used in Python
Topics Covered:
- Arrays
- Lists
- Tuples
- Dictionaries
- Sets
Hands-on:
Write Python Code to understand and implement Python Data Structures.
Learning Objectives:
Learn all about loops and control statements in Python.
Topics Covered:
- 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.
Learning Objectives:
Write user-defined functions in Python. Learn about Lambda function and also the object-oriented way of writing classes & objects
Topics Covered:
- 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.
Learning Objectives:
Gain knowledge on OOPs to code easily and efficiently. Learn to construct classes and define objects.
Topics Covered:
- 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.
Learning Objectives:
Learn how to import datasets into Python. Also, learn how to write output into files from Python.
Topics Covered:
- 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.
Learning Objectives:
Manipulate & learn to transform raw data using Pandas library. Learn to generate insights from your data.
Topics Covered:
- Clean & Prepare Datasets
- Data Transformations
- Encoding
Hands-on:
Write Python code to manipulate data frames and churn insights using various python libraries.
Learning Objectives:
Learn to summarize dataset through descriptive statistics. Use a variety of measurements to better understand your data. Learn to treat missing values. Also, learn how to discover patterns in your data.
Topics Covered:
- 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.
Learning Objectives:
Master in a commonly used Python graphics module, Matplotlib. Learn to create charts such as histogram, pie-chart, box plots and so on using matplotlib.
Topics Covered:
- 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.
Learning Objectives:
Learn how to use one of Python’s most convenient libraries, Seaborn, for data visualization. Build charts such as violin plots, scatterplot, heat-maps and so on using Seaborn.
Topics Covered:
- 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.
Learning Objectives:
Jump into an advanced visualization technique, that is ggplot in Python. Understand Grammar of Graphics and apply it to create charts.
Topics Covered:
- Grammar of Graphics
- Installing ggplot
- Importing ggplot
- Components of ggplot
- Charts using ggplot: Bar charts, Scatterplots, Polar PlotsHands-on:: Write Python code using ggplot to visualize data and represent it graphically
Learning Objectives:
Learn to implement data analyzing methods, visualize the same and understand missing value treatment methods.
Topics Covered:
- Checking missing values
- Visualize data after analyzing
- Methods to impute missing values
Hands-on:
Write Python code to analyze, visualize and treat missing values.
Learning Objectives:
Hands-on session on a real-life case study.
Topics Covered:
- 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
Create informative visualizations using matplotlib
Write Python code using Python library: matplotlib to visualize data and represent it graphically
Create informative visualizations using Seaborn
Write Python code using Python library: Seaborn to visualize data and represent it graphically
Using House Attributes and Sales Price, perform exploratory data analysis
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
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The Course
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 of the use of data science techniques and the Python language to build applications on data statistics and write reusable, testable and efficient code.
Tools and Technologies used for this course are
-Python
-Anaconda
There are no restrictions but participants would benefit if they have elementary programming knowledge and familiarity with statistics.
Yes, KnowledgeHut offers virtual training.
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.
Finance Related
Any registration canceled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% reduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of written request for a 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 http://www.knowledgehut.com/refund.
The Remote Experience
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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