Data Engineer

DATB-IPDSDE : Introduction to Python for Data Science and Data Engineering

Course Duration

2 Days

Delivery

ILT/VILT

Overview

Overview Course Curriculum Training Schedule Exam & Certification FAQs

This course is intended for complete beginners to Python to provide the basics of programmatically interacting with data. The course begins with a basic introduction to programming expressions, variables, and data types. It then progresses into conditional and control statements followed by an introduction to methods and functions. You will learn the basics of data structures, classes, and various string and utility functions. Lastly, you will gain experience using the pandas library for data analysis and visualization as well as the fundamentals of cloud computing. Throughout the course, you will gain hands-on practice through lab exercises with additional resources to deepen your knowledge of programming after the class.

What You'll Learn

Use the Databricks workspace for Python development.
Understand Python basics, including data types, functions, and control flow.
Work with common data structures and perform data manipulation.
Use pandas for data analysis and create simple visualizations.
Apply Python skills to practical data science and data engineering tasks.

Who Should Attend

Data scientists, ML engineers and analytics practitioners preparing datasets for machine-learning workflows on the Databricks Lakehouse Platform.
Professionals responsible for cleaning, transforming, enriching and engineering features from raw, semi-structured or structured datasets before model training.
Individuals using Python, Spark or Delta Lake who want to strengthen their skills in data wrangling, feature extraction, handling missing values and preparing high-quality training datasets.
Practitioners working with MLflow, Feature Store or AutoML who need to streamline data-preparation steps for reproducible and scalable ML pipelines.
Teams building end-to-end machine-learning solutions and aiming to standardise data-preparation best practices for improved model performance and reliability.

Prerequisites

While the course does not require prior experience, learners will benefit from having:

A basic understanding of data concepts such as tables, rows, and columns.
General familiarity with programming concepts (optional but helpful).
Comfort with using a computer, managing files, and working in a web browser.
Some exposure to analytical or data-related tasks.

Learning Journey

Coming Soon...

Module 1. Day 1

Introduction to the Databricks environment
Python overview
Variables and data types
Complex data types
Control flow
Loops
Functions
Classes

Module 2. Day 2

Using libraries
Data analysis with pandas
Advanced methods in Pandas
Data visualization
Cloud computing 101
Capstone and next steps

Frequently Asked Questions (FAQs)

None

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