Python for Data Analytics & Data Science [2026]

Name: Python for Data Analytics & Data Science [2026]
Rating: 4.1 (5 reviews)

Python from 0 to interview questions

Role Play

Created byIbritics Academy

Last updated 6/2026

English

What you'll learn

Categorical variables and how to include them into model
Missing values and how to handle them?
What is a Correlation Matrix's role?
How to check relationship between variables?
How to interpret the regression analysis?
How to use polynomial model?
What is an overfitting? How to prevent it?

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

Course content

6 sections • 39 lectures • 2h 51m total length

Environment setup for introduction to Python2:43

Variables in Python11:50
Variables are essentially labeled containers used to store data values. Instead of remembering a specific number or string, you give it a name like user_age = 25.
Exercise Variables in Python
Solution to Variables in Python0:17
Operations in Python5:07
These are the symbols that perform actions on variables and values.
Arithmetic: +, -, *, /.
Comparison: == (equal to), != (not equal), > (greater than).
Exercise Operations in Python
Solution Operations in Python0:20
Explore solution operations in Python for data analytics and data science, applying practical coding patterns to streamline problem-solving workflows.
Lists in Python Part 17:51
Lists are ordered collections of items stored in a single variable, defined by square brackets [].

Accessing Elements in a List
Modifying Elements in a List
Adding Elements to a List
Removing Elements from a List
Removing an Item by Value
Exercise Lists
IF statement2:59
This is how your code makes decisions. It checks if a condition is True or False. If it’s true, the code inside runs; if not, it skips it (or goes to an else).
Lists in Python Part 29:54
For Loop in Python Part 14:41
For Loop Part 23:54
Nested Loop6:25
Dictionaries in Python5:05
List of Dictionaries3:13
From dictionary to Dataframe5:40

Datasets to download0:02
You will need these datasets for the next exercises
Introduction to Pandas8:53
Introduction to Pandas library in Python using Google Colab
- Import pandas as pd
- Upload datasets
- Explore datasets
Selecting Columns Part 12:33
You are going to learn how to select specific columns in dataframe
Selecting Columns Part 23:42
Learn to select data frame rows in Python for data analytics by filtering on salary above 80000, using string contains, and combining conditions with and and or, with proper brackets.
Detect Missing Values2:15
- Here you are going to use isnull() and sum() functions to detect missing value in dataset
Treat Missing Values3:22
- Treating missing values with dropna() etc.
[Bonus Lecture] Import datasets with different seperator2:43
Sometimes your dataset has a seperator which is not comma (,) in this case your read_csv() will have problems to read it. You need to specify it
Concatenate 2 datasets2:18
We are unifying Employees lists together
Reset original indexing1:20
Concatenate two tables into a single dataframe, then reset the index with drop equals true to drop the old index and create a new continuous index that continues.
Detect duplicates in dataset2:16
Drop Duplicates1:10
Rename Column0:45
Rename a column in a pandas data frame to label pay as gross salary, using df3.rename with in place, and verify the updated columns.
Calculate New Column1:08
Create a new column to compute net salary by deducting 23% tax from gross salary, and validate the results.
Export Final Fine to CSV1:00

Categorical variables and how to include them into model6:27
Learn how to encode categorical data with one-hot encoding in scikit-learn, turning origin into binary columns (Americas, Europe, Asia) for regression modeling.
Which one is not a categorical variable?
Missing values and how to handle them?11:08
Learn to detect missing values, visualize a correlation matrix with Seaborn, and apply linear interpolation via SciPy interp1d to fill gaps in data.
Checking missing values in "auto" dataset
What is the python command to drop the missing values?
What is a Correlation Matrix's role?5:12
Learn how to run correlation matrix analysis on data such as auto data, build the correlation matrix from selected columns, and visualize it with a seaborn heatmap using annotations.
Correlation
How to check relationship between variables?2:07
If A has a correlation of -0.4 with B. How would the relationship be directed?
Bonus: Data preprocessing with Python

Supervised vs Unsupervised Learning & Parametric vs non-parametric Models8:42
Explore fundamentals of supervised versus unsupervised learning and parametric versus non-parametric models, then train and evaluate regression with a train and test split, using r squared and mean squared error.
How to interpret the regression analysis?9:35
split data into train and test sets, add an intercept, fit a regression model to predict mpg, and evaluate with root mean square error on the test set.
How to improve the regression model results with logarithmic transformation?8:40
Transform select variables with numpy log, create a new dataset, and assess with scatter plots and ordinary least squares regression to improve R-squared and reduce root mean square error.
How to use polynomial model in Python6:09
Explore polynomial regression in Python with polynomial features to compare linear and higher-degree fits on auto and horsepower data, using train-test splits and adjusted R-squared to avoid overfitting.
Evaluation Metrics2:32
In this section you will see how to assess the goodness of your prediction by calculating the error between actual Y values and predicted Y. The smaller it is the better results you get.
Root Mean Square Error to compare test results
Data Scientist at Automative Industry

Requirements

Basic knowledge of Python is required Particularly Pandas library

Description

In this course, we aim to provide you with a focused and efficient approach to preparing for data science tasks through practical questions. I understand that your time is valuable, so I have carefully curated the content to cut out any unnecessary noise and provide you with the most relevant materials.

First section of the course will prepare you for fundamentals of Python.

Moving beyond theory, the course will dive into a wide range of practical data science questions. These questions have been carefully selected to represent the types of problems frequently encountered in real-world data science roles. By practicing these questions, you will develop the skills and intuition necessary to tackle similar problems during interviews.

Throughout the course, we have filtered out any extraneous materials and focused solely on the core topics and questions that are most likely to come up in data science interviews. This approach will save you time and allow you to focus your efforts on what truly matters.

Index:

Missing values and how to handle them? (Python)
What are categorical variables and how to include them into model (Python)
What is a Correlation Matrix's role? (Python)
How to check relationship between variables? (Python)
How to interpret the regression analysis? (Python)
How to improve the regression model results with logarithmic transformation? (Python)
How to use polynomial model? (Python)
What is an overfitting? How to prevent it? (Theory)
Supervised vs Unsupervised Learning (Theory)
Parametric and Non-parametric model (Theory)

Who this course is for:

Beginners in Machine Learning and Python
Students who are searching to land their first job as a data scientist

Python for Data Analytics & Data Science [2026]

What you'll learn

Explore related topics

Coding Exercises

Course content

Setting up an environment1 lecture • 3min

Introduction to Python13 lectures • 1hr 7min

Pandas for Data Manipulation and Analysis14 lectures • 33min

Environment Setup for Data Analysis and Data Science2 lectures • 7min

Data Analyst Questions4 lectures • 25min

Data Scientist Questions6 lectures • 36min

Requirements

Description

Who this course is for: