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Programming for Data Science using Python
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Overview
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Valued CertificationYou will be given an industry-wide recognized course completion certificate by Manipal ProLearn. -
Comprehensive CurriculumThe course curriculum encompasses all the aspects of Python essential in Data Science. -
Elaborate Course DesignThe course is designed and developed by subject matter experts and experienced professionals in the field of Data Science. -
Updated ContentManipal ProLearn ensures timely updates/changes in course content in accordance with the syllabus of Manipal Global Academy of Data Science. -
Enhanced LearningAssignments and assessments ensure that the candidate gains acumen in all the concepts.
Valued Certification
You will be given an industry-wide recognized course completion certificate by Manipal ProLearn.
Comprehensive Curriculum
The course curriculum encompasses all the aspects of Python essential in Data Science.
Elaborate Course Design
The course is designed and developed by subject matter experts and experienced professionals in the field of Data Science.
Updated Content
Manipal ProLearn ensures timely updates/changes in course content in accordance with the syllabus of Manipal Global Academy of Data Science.
Enhanced Learning
Assignments and assessments ensure that the candidate gains acumen in all the concepts.
About Course
Python is one of the prominent tools used in the field of Data Science, and proficiency in Python programming is highly desired if you are seeking a job in the field. The course is designed to equip you with detailed knowledge about the programing language, starting from the fundamentals. Python provides a software environment for Data Science and Machine Learning. From this course, you will learn the basic and complex programming concepts in Python. Lessons on the use of Python in data analysis using NumPy and Pandas – python libraries for Data Science – are part of the course. You will also gain knowledge on Python’s role in data visualization, statistical computations and building models for machine learning.
What skills you learn
Manipal ProLearn ensures you gain functional knowledge in Python to start a career in Data Science. Through its elaborate curriculum, you will learn about the basic concepts and then get introduced to the complicated aspects of Python. After the course completion, you are sure to become highly skilled in Python programming and ready to take up a relevant job. Some outcomes of taking up the course are
- Knowledge about data structures, control structures, creating user-defined functions and lambda expressions in Python
- A comprehensive understanding of the use of NumPy to slice and dice multi-dimensional arrays
- Ability to manipulate and transform data using the Pandas library in Python
Who Should Attend this course
Candidates pursuing a career in the Data Science or Data Analytics domain
Engineering and non-engineering graduates aspiring for a career as a data scientist or data analyst
Beginners in the analytics domain
Candidates pursuing a career in the Data Science or Data Analytics domain | Engineering and non-engineering graduates aspiring for a career as a data scientist or data analyst |
Beginners in the analytics domain |
Key Features
All-inclusive Course Material
The course has an e-learning based curriculum comprising of online video tutorials featuring experts and additional reading material for reference.
Enabling Self-learning
Online tests, quizzes and assignments enable self-assessment and a better understanding of concepts.
Hands-on Experience
Learning through real-time case studies and problem-solving using Python provides empirical knowledge.
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Course Curriculum
1.1 Basics Of Python
Introduction to Python
Installation of Anaconda
Getting Started with Python
1.2 Data Structures In Python
Data structures and Data types in Python
Working with Data Types and Data Structures - Part 1
Working with Data Types and Data Structures - Part 2
Working with Programming Constructs in Python
2.1 OO & Functional Programming In Python
Creating UDF's in Python
Lambda's and its applications
Applications of lambda - map, reduce, filter
Object, Methods and Classes
Creating modules in Python
Exception handling in Python
Regular expressions in Python
File operations in Python
2.2 Using The NumPy Package
Understanding the need for NumPy package
Creating arrays in NumPy
NumPy Indexing and Slicing
Array operations in NumPy
Copies and Views
Broadcasting
Array shape Manipulation & Array sorting
2.3 Exploring The Pandas Package
Understanding the pandas library
Creating panda series and data frames
Operations on series and data frames
3.1 Operations In Pandas, Part I - Indexing & Selecting
Basic Indexing
Label, Integer and mixed Indexing
Boolean Indexing
Operations on indexes
Using .loc and .iloc operators
3.2 Operations In Pandas, Part II - Grouping, Merging & Reshaping
Grouping of data
Merging and joining data frames
Pivoting and reshaping data
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