Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. It’s easy to learn simple syntax is very accessible to new programmers and is similar to Matlab, C/C++, Java, or Visual Basic. Python is general purpose and comparatively easy to learn with an increased adoption for analytical and quantitative computing. For over a decade, Python has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing. Course Objectives After the completion of the Big Data Analytics with Python Course at Edureka, you should be able to: – Master the Basic and Advanced Concepts of Python – Understand Python Scripts on UNIX/Windows,Python Editors and IDEs – Master the Concepts of Sequences and File operations – Learn how to use and create functions,sorting different elements ,Lambda function,error handling techniques and Regular expressions ans using modules in Python – Gain expertise in machine learning using Python and also build a Real Life Machine Learning application – Understand the supervised and unsupervised learning and concepts of Scikit-Learn – Master the concepts of MapReduce in Hadoop – Learn to write Complex MapReduce programs – Understand what is PIG and HIVE ,Streaming feature in Hadoop,MapReduce job running with Python – Implementing a PIG UDF in Python,Writing a HIVE UDF in Python,Pydoop and/Or MRjob Basics – Master the concepts of Web scraping in Python – Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience – Who should go for this course? Experienced Professional or a Beginner, Anyone who wants to learn to programming with Python can start right away! This course is exclusively designed for professionals aspiring to make a career in Big Data Analytics using Python. Software Professionals, Analytics Professionals, ETL developers, Project Managers, Testing Professionals are the key beneficiaries of this course. Other professionals who are looking forward to acquire a solid foundation of this widely-used open source general-purpose scripting language, can also opt for this course. Pre-requisites Although there are no hard prerequisites,attendees having prior programming experience and familiarity with basic concepts such as variables/scopes, flow-control, and functions would be beneficial. Prior exposure to object-oriented programming concepts is not required, but definitely beneficial. Why Learn Python for Big Data Analytics ? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python is continued to be a favourite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next “Big Thing” and a must for Professionals in the Data Analytics domain. Course Length: 2:53:34 hours Source: Edureka
FAQ
Most frequent questions and answers
Co-operative education is a three-way partnership between the university, students and employers. Students apply their classroom knowledge in a series of four-month work experiences. You, the employer, enhance a student’s education, while reaping the unique benefits of CO-OP employees.
- Year-round access to well-motivated, qualified employees.
- Access to potential full-time staff in a controlled environment, reducing your costs and risks.
- Access to a cost-effective source of temporary employees for peak periods or special projects.
- A say in what students learn by working with the university.
- Promotion of your organization as one that believes in developing the potential of young people.
- Access to a great pool of French-speaking, English-speaking and bilingual students.
Most work terms run at least 15 weeks, or four months. They can be no shorter than 13 weeks. Some master’s students, as well as some science and engineering students, are available for 8 or 12 months’ work terms.
All jobs are reviewed by a CO-OP Program Coordinator, and only those providing students with work experience related to their professional development are approved. Administrative activities involved in a job should be less than 10% of the entire workload.
When you first contact SSC, you are assigned one of our Program Coordinators, depending on your discipline of interest. This person is your main contact in our office. As you move through the recruitment process, you also work with a representative from CO-OP Administrative Services, who assists with job posting and interview scheduling.
Internships offer usually one discipline-specific, supervised, structured paid or unpaid, and for academic credit work experience or practice placement.
Work Experience intersperses one or two work terms (typically full-time) into an academic program, where work terms provide experience in a workplace setting related to the student’s field of study and/or career goals.
Community Service Learning (CSL) integrates meaningful community service with classroom instruction and critical reflection to enrich the learning experience and strengthen communities. In practice, students work in partnership with a community-based organization to apply their disciplinary knowledge to a challenge identified by the community.
Field Placement provides students with an intensive part-time/short term intensive hands-on practical experience in a setting relevant to their subject of study. Field placements may not require supervision of a registered or licensed professional and the completed work experience hours are not required for professional certification. Field placements account for work-integrated educational experiences not encompassed by other forms, such as co-op, clinic, practicum, and internship.
