Python Certification Training for Data Science
Edureka’s Data Science using Python programming certification course enables you to learn data science concepts from scratch.
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. 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.
Edureka's Python Certification Training not only focuses on fundamentals of Python, Statistics and Machine Learning but also helps one gain expertise in applied Data Science at scale using Python. The training is a step by step guide to Python and Data Science with extensive hands on. The course is packed with several activity problems and assignments and scenarios that help you gain practical experience in addressing predictive modeling problem that would either require Machine Learning using Python. Starting from basics of Statistics such as mean, median and mode to exploring features such as Data Analysis, Regression, Classification, Clustering, Naive Bayes, Cross Validation, Label Encoding, Random Forests, Decision Trees and Support Vector Machines with a supporting example and exercise help you get into the weeds.
Furthermore, you will be taught of Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to train your machine based on real-life scenarios using Machine Learning Algorithms.
Edureka’s Python course will also cover both basic and advanced concepts of Python like writing Python scripts, sequence and file operations in Python. You will use libraries like pandas, numpy, matplotlib, scikit, and master the concepts like Python machine learning, scripts, and sequence.
It's continued to be a favourite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging programs is a breeze in Python with its built in debugger.
It 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.
It has evolved as the most preferred Language for Data Analytics and the increasing search trends on Python also indicates that it is the " Next Big Thing " and a must for Professionals in the Data Analytics domain.
After completing this Data Science Certification training, you will be able to:
- Programmatically download and analyze data
- Learn techniques to deal with different types of data – ordinal, categorical, encoding
- Learn data visualization
- Using I python notebooks, master the art of presenting step by step data analysis
- Gain insight into the 'Roles' played by a Machine Learning Engineer
- Describe Machine Learning
- Work with real-time data
- Learn tools and techniques for predictive modeling
- Discuss Machine Learning algorithms and their implementation
- Validate Machine Learning algorithms
- Explain Time Series and its related concepts
- Perform Text Mining and Sentimental analysis
- Gain expertise to handle business in future, living the present
Edureka’s Data Science certification course in Python is a good fit for the below professionals:
- Programmers, Developers, Technical Leads, Architects
- Developers aspiring to be a ‘Machine Learning Engineer'
- Analytics Managers who are leading a team of analysts
- Business Analysts who want to understand Machine Learning (ML) Techniques
- Information Architects who want to gain expertise in Predictive Analytics
- 'Python' professionals who want to design automatic predictive models
- Introduction to Python
- Sequences and File Operations
- Deep Dive – Functions, OOPs, Modules, Errors and Exceptions
- Introduction to NumPy, Pandas and Matplotlib
- Data Manipulation
- Introduction to Machine Learning with Python
- Supervised Learning - I
- Dimensionality Reduction
- Supervised Learning - II
- Unsupervised Learning
- Association Rules Mining and Recommendation Systems
- Reinforcement Learning
- Time Series Analysis
- Model Selection and Boosting