Python with Data Science

Course Overview

Data science involves using data to find patterns, make predictions, and help make better decisions. It combines skills from statistics, computer science, and domain knowledge to analyze and interpret complex data.

This course provides a comprehensive introduction to data science using Python. You’ll start with Python basics and explore key libraries used in data science. The training covers essential concepts in statistics and probability, data pre-processing techniques, and effective data visualization methods. You’ll also learn how to develop and evaluate machine learning models to extract valuable insights from your data. Ideal for anyone looking to build a solid foundation in data science.

Launch your career in Data Science by developing in-demand skills and become job-ready in 30 hours or less.

Highlights

Upgrade your career with top notch training

• Enhance Your Skills: Gain invaluable training that prepares you for success.
• Instructor-Led Training: Engage in interactive sessions that include hands-on exercises for practical experience.
• Flexible Online Format: Participate in the course from the comfort of your home or office.
• Accessible Learning Platform: Access course content on any device through our Learning Management System (LMS).
• Flexible Schedule: Enjoy a schedule that accommodates your personal and professional commitments.
• Job Assistance: Benefit from comprehensive support, including resume preparation and mock interviews to help you secure a position in the industry.

Outcomes 

Upon successfully completing the Advanced Python Training course, participants will gain:

• Proficiency in Python Programming: A solid understanding of Python programming fundamentals, including syntax, data types, control structures, functions, and object-oriented programming, equipping them to write efficient Python code.
• Data Manipulation Techniques: Master data manipulation using libraries such as Pandas and NumPy, allowing them to clean, transform, and analyze data from diverse sources effectively.
• Data Visualization: Learn how to create visualizations to represent data insights using libraries like Matplotlib, enhancing their ability to communicate findings clearly and effectively.
• Statistical Analysis: Understand the basics of statistical concepts, including descriptive statistics, probability distributions, and inferential statistics, enabling participants to apply statistical methods to analyze data.
• Introduction to Machine Language: Gain foundational knowledge of machine learning concepts and algorithms, including supervised and unsupervised learning, and how to implement basic machine learning models.

Key Learnings

• Master the fundamentals of Python programming, including syntax, data types, operators, and control flow structures, which form the foundation for coding in data science.
• Learn to use the Pandas library for data manipulation, including data cleaning, transformation, and handling missing values.
• Gain proficiency in using NumPy for numerical operations and array manipulations. Understand how to perform complex mathematical computations efficiently.
• Explore various data visualization libraries, such as Matplotlib, to create informative graphs and charts that effectively communicate data insights.
• Understand key statistical concepts and techniques, including descriptive statistics, hypothesis testing, and probability distributions, essential for analyzing and interpreting data.
• Get acquainted with machine learning concepts, including the difference between supervised and unsupervised learning, and explore algorithms like regression, classification, and clustering.
• Learn how to develop machine learning models, including training, testing, and evaluating model performance with metrics such as accuracy, precision, and recall.

Pre-requisites 

• Basic Computer Skills: Participants should have a general understanding of using computers, including file management, navigating the operating system, and using web browsers effectively.
• Familiarity with Python: While prior programming experience is not required, a basic understanding of programming concepts using Python is beneficial. You can take our < Programming Essentials using Python> course prior to this course.
• Basic Mathematics: A basic understanding of mathematics, particularly statistics, is helpful, as data science relies heavily on mathematical concepts and statistical analysis.

Job roles and career paths 

• This training will equip you for the following job roles and career paths:
• Data Scientist
• Data Analyst
• Machine Learning Engineer
• Data Engineer
• Business Intelligence Developer

Python with Data Science

The demand for the Python with Data Science course is influenced by several key trends in the tech industry and the job market. The demand for data scientists and professionals with data analysis skills continues to grow across various industries, including finance, healthcare, marketing, and technology. Organizations are increasingly leveraging data to drive business decisions. Data science roles are among the fastest-growing job categories, with positions for data scientists, analysts, and engineers being widely advertised. This creates a significant need for professionals with data science skills.