Python and Statistics for Data Science

Understand Python for data science from the ground up

Python is one of the world’s most popular programming languages and an essential skill for modern data science. On this three-week course, you’ll build a strong foundation in Python while developing the statistical knowledge needed to analyse data with confidence.

You’ll start with Python fundamentals, including variables, data types, conditional logic, loops, and functions, before progressing to more advanced programming techniques.

By the end of the course, you’ll be able to write clear, efficient, and reusable Python code for data science applications.

Analyse data using NumPy and Pandas

Discover how Python is used to process and explore data using two of the most widely used libraries in data science: NumPy and Pandas.

You’ll perform numerical computing with NumPy arrays and vectorised operations before learning how to work with real-world datasets using Pandas. From loading and merging data to handling missing values and creating meaningful summaries, you’ll develop practical data analysis skills that form the foundation of data science and machine learning.

Apply statistics to make data-driven decisions

Effective data science relies on more than programming alone. You’ll build your understanding of statistics by exploring descriptive statistics, probability, sampling, and the normal distribution, giving you the tools to interpret data accurately.

Through hands-on exercises each week, you’ll gain the confidence to draw meaningful conclusions from data and support evidence-based decision-making.

Learn from experienced data science professionals at Edureka

Throughout the course, you’ll learn from the experts at Edureka. Their project-based approach will help you prepare for future study and careers in data science, data analytics, and machine learning.

This course is designed for aspiring data professionals, software developers, analysts, engineers, and business professionals who want to build practical data science skills with Python. No prior data science or machine learning experience is required, though basic computer literacy and comfort with logical problem-solving will be helpful.

It is ideal for career changers moving into data roles, working professionals looking to add analytics capabilities to their current role, and graduates preparing for data analyst, data scientist, or machine learning engineer positions. It is also valuable for managers and domain experts who want to understand how data-driven decisions are made.

This course is designed for aspiring data professionals, software developers, analysts, engineers, and business professionals who want to build practical data science skills with Python. No prior data science or machine learning experience is required, though basic computer literacy and comfort with logical problem-solving will be helpful.

It is ideal for career changers moving into data roles, working professionals looking to add analytics capabilities to their current role, and graduates preparing for data analyst, data scientist, or machine learning engineer positions. It is also valuable for managers and domain experts who want to understand how data-driven decisions are made.

• Explain how core Python programming concepts and data structures support data science workflows.
• Apply Python, NumPy, and Pandas to perform numerical operations, manipulate structured data, and generate analytical summaries.
• Interpret descriptive statistics, probability distributions, sampling results, correlations, and outliers to identify meaningful patterns.
• Evaluate statistical evidence using hypothesis testing and related measures to support data-driven conclusions.
• Investigate datasets by inspecting their structure, filtering records, combining sources, and addressing common data-quality issues.