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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.
Learn to install Anaconda and Python distribution
Explore Python language fundamentals, including basic syntax, variables, and types
Learn about the different data structures Python can handle. Create and manipulate regular Python lists, tuple etc.
Learn about control and loops statements
User defined function, an object-oriented way of writing classes/objects, use functions, import packages
Manipulate and analyze a dataset in Python using Pandas
Build Numpy arrays, and perform interesting calculations
Use various Python libraries to visualize data. Create and customize plots on real data
Anybody who is a Data Science aspirant with coding or non-coding background
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Learning Objectives:
Learn how to install Python distribution - Anaconda, the basic data types, strings & regular expressions.
Topics Covered:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
Learning Objectives:
Learn to implement data analyzing methods, visualize the same and understand missing value treatment methods.
Topics Covered:
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:
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.
Write Python code using Python library: matplotlib to visualize data and represent it graphically.
Write Python code using Python library: Seaborn to visualize data and represent it graphically.
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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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.
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 the written request for a refund. Kindly go through our Refund Policy for more details.
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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