COURSE INTRODUCTION
Python is a multi-paradigm or versatile programming language that can be considered as a sort of swiss knife for the coding world. This is because it supports structured programming, Object Oriented Programming, and even functional programming patterns. The versatility of Python undoubtedly makes it the best-suited programming language for the data scientists. Here are some of the other advantages of python for data science, which will help you understand why you should learn data science with Python:
- Python is a powerful open source programming language, which means that it’s free to use while having all the properties that a programming language should have.
- It is a versatile programming language that supports Object-Oriented Programming, Structured Programming, and functional programming patterns.
- Python has some 72,000 libraries in the Python Package Index that aid in scientific calculations and machine learning applications.
- Python sports an easy to understand and readable syntax that ensures that the development time is cut into half when compared with other programming languages.
- Python enables you to perform data analysis, data manipulation, and data visualization, which are very important in data science.
All the above mentioned advantages of Python programming language make it ideal to be used for data science by the data scientists. Owing to the extensibility and general purpose nature, it is recommended that you learn data science with Python
KEY FEATURES
- 68 hours of in-depth learning
- 4 real-life industry-based projects in the domains of telecom, stock market, etc.
- Interactive learning with Jupyter notebooks labs
- Lifetime access to self-paced learning
- Dedicated mentoring session from our faculty of industry experts
COURSE OBJECTIVES
After finish the course, student will have knowledge and skills to:
- Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics
- Install the required Python environment and other auxiliary tools and libraries
- Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
- Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions
- Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave
- Perform data analysis and manipulation using data structures and tools provided in the Pandas package
- Gain expertise in machine learning using the Scikit-Learn package
- Gain an in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline
- Use the Scikit-Learn package for natural language processing
- Use the matplotlib library of Python for data visualization
- Extract useful data from websites by performing web scrapping using Python
- Integrate Python with Hadoop, Spark and MapReduce
AUDIENCE
- There is a booming demand for skilled data scientists across all industries that make this course suited for participants at all levels of experience. We recommend this Data Science with Python training particularly for the following professionals:
- Analytics professionals who want to work with Python
- Software professionals looking to get into the field of analytics
- IT professionals interested in pursuing a career in analytics
- Graduates looking to build a career in analytics and data science
- Experienced professionals who would like to harness data science in their fields
- Anyone with a genuine interest in the field of data science
EXAM & CERTIFICATION
Unlock Simplilearn certificate
To become a Certified Data Scientist with Python, you must fulfil the following criteria:
- Complete one project out of the two provided in the course. Submit the deliverables of the project in the LMS which will be evaluated by our lead trainer
- Score a minimum of 60% in any one of the two simulation tests
- Complete 85% of the course
- Attend one complete batch.
COURSE CONTENTS
Lesson 00 - Course Overview
Lesson 01 - Data Science Overview
Lesson 02 - Data Analytics Overview
Lesson 03 - Statistical Analysis and Business Applications
Lesson 04 - Python Environment Setup and Essentials
Lesson 05 - Mathematical Computing with Python (NumPy)
Lesson 06 - Scientific computing with Python (Scipy)
Lesson 07 - Data Manipulation with Pandas
Lesson 08 - Machine Learning with Scikit–Learn
Lesson 09 - Natural Language Processing with Scikit Learn
Lesson 10 - Data Visualization in Python using matplotlib
Lesson 11 - Web Scraping with BeautifulSoup
Lesson 12 - Python integration with Hadoop MapReduce and Spark