Python for Beginners: Learn Data Science in Google Colab

Learn the fundamentals of Python by understanding variables and different data types. This lecture explains how data is stored and used in Python with simple, real-world examples.

Practice working with common Python data types including integers, floats, strings, and booleans. This session focuses on practical examples to build a strong foundation.

Understand how operators work in Python. This lecture introduces arithmetic, relational, and logical operators with clear explanations for beginners.

Learn how to perform mathematical operations in Python using arithmetic operators. Includes hands-on examples for addition, subtraction, multiplication, and division.

Explore how to compare values in Python using relational operators. Understand conditions like greater than, less than, and equality with real examples.

Master logical operators such as AND, OR, and NOT in Python. This lecture uses simple examples to explain how decisions and conditions work in real data analysis.

In this lecture, you will get an overview of how data is imported and explored in Python using Google Colab. We will introduce the workflow of loading datasets, understanding their structure, and preparing for analysis.

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Learn how to import CSV files into Python using the pandas library in Google Colab. This lecture covers file upload methods and how to load data into a DataFrame for analysis.

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This lecture explains how to import Excel files into Python. You will learn how to read Excel sheets using pandas and prepare your data for further analysis.

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Understand the structure of your dataset by checking its dimensions, column names, and data types. This lecture introduces key pandas functions to explore and understand your data.

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Generate summary statistics such as mean, standard deviation, and distribution of variables using pandas. This helps you quickly understand the characteristics of your dataset.

Learn how to identify missing values and duplicate records in your dataset using pandas. This is an essential step to ensure data quality before analysis.

In this video, you will learn how to identify missing values in your dataset using Python's Pandas library. We will show you simple commands to check for null or missing data, count them, and understand how they may affect your data analysis process.

In this video, you will learn how to rename variables (columns) in your dataset using Python’s Pandas library in Google Colab. Renaming variables helps make your data more understandable and easier to work with during analysis. We will cover different methods to rename single or multiple columns efficiently.

You've learned the basics of Python and pandas—now you're ready for the real-world challenge of data science. The most common roadblock? Categorical data.

Machine learning models cannot understand text like "Male" or "New York." You need to transform this data into a numerical format, and this process is called data encoding. This course is your complete, hands-on guide to mastering the essential data encoding techniques that every data scientist and machine learning engineer must know.

We will go beyond the basics, exploring various encoding methods and learning when to use each one to get the best performance from your models. By the end of this course, you will be able to confidently handle any type of categorical data, preparing your datasets for powerful machine learning algorithms.

Raw data is rarely ready for analysis or machine learning. In the real world, you'll need to clean, format, and transform your data into a usable format. This process, known as variable transformation, is a critical skill that separates a beginner from a professional data scientist.

This course is your complete guide to mastering variable transformation in Python. We will move beyond simple data loading and dive into the practical techniques used by data professionals every day. From creating new features like BMI to handling messy dates and categorizing data, you'll learn to prepare any dataset for a powerful machine learning model.

We'll use industry-standard libraries like pandas and NumPy to efficiently manipulate your data. By the end of this course, you'll have the confidence to tackle real-world datasets and make them "machine-learning ready."

In this lecture, you will learn how to upload a dataset from your computer folder to Google Drive. This is the first step before importing the data into Google Colab for analysis.

For the lecture title, your current title is good:

Uploading Data from a Computer Folder to Google Drive

For resources, you can upload the sample dataset if you want students to practice with the same file. If the dataset is already shown in the video, adding it as a resource is helpful.

In this lecture, students will learn how to import data from Google Drive into Google Colab using Python. This step helps them access their dataset directly inside Colab for data analysis.

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import pandas as pd

df=pd.read_csv('/content/MyData.csv')

#----

import matplotlib.pyplot as plt

plt.hist(df['momheight'])

plt.show()

#-----

import matplotlib.pyplot as plt

plt.figure(figsize=(16,9))

sns.histplot(df['momheight'], bins=10, kde=True, color='blue')

plt.xlabel('Maternal body height, cm')

plt.ylabel('Frequency')

plt.show()

#-----

Histograms are an essential tool for any data analyst or scientist. They provide a powerful way to understand the distribution of your data, helping you uncover patterns, identify outliers, and make informed decisions.

In this video, you will learn to master the art of creating effective histograms using Python's matplotlib library within the collaborative environment of Google Colab. We'll cover everything from the basics of what a histogram is and why it's so useful, to the practical steps of writing the code.

By the end of this lesson, you will be able to:

• Understand what a histogram is and when to use one.

• Write Python code to create basic histograms from scratch.

• Customize your histograms by adjusting the number of bins, colors, and labels for maximum clarity.

• Interpret the results of your histograms to gain insights into your data's frequency and distribution.

This hands-on guide will equip you with a fundamental data visualization skill, empowering you to better explore and present your datasets. No complex setup is required—just a browser and your Google account.

#----

plt.figure(figsize=(16,9))

sns.boxplot(df['income'],  color='red')

plt.title('Distribution of Maternal Height')

plt.xlabel('Maternal Height (cm)')

plt.show()

#---

Box plots, also known as box-and-whisker plots, are a powerful data visualization tool that provides a concise summary of a dataset's distribution. They are indispensable for quickly identifying key statistical measures and spotting potential outliers.

In this video, you will learn to create and interpret box plots using Python's matplotlib and seaborn libraries within the accessible Google Colab environment. We'll start with the fundamentals of what a box plot represents, including the median, quartiles, and whiskers. You will then move on to writing the code to generate your own plots.

By the end of this lesson, you will be able to:

• Understand the key components of a box plot: the median, interquartile range (IQR), and outliers.

• Use Python to generate box plots from your datasets.

• Customize the appearance of your box plots for better clarity and presentation.

• Interpret box plots to understand the spread and skewness of your data.

This hands-on guide will give you a core data visualization skill, enabling you to perform exploratory data analysis more effectively.

Learn how to explore and summarize data in Python using boxplots. Understand distributions, detect outliers, and visualize patterns with clear labeling and hands-on examples.

#---

plt.figure(figsize=(16,9))

sns.violinplot(df['momheight'],  color='green')

plt.title('Distribution of Maternal Height')

plt.xlabel('Maternal Height (cm)')

plt.show()

#--

Violin plots are a powerful data visualization tool that combines the benefits of a box plot with a kernel density estimate. They are excellent for visualizing the distribution of numerical data and are particularly useful for comparing distributions across different groups.

In this video, you will learn to create and interpret violin plots using Python's seaborn and matplotlib libraries within the convenient Google Colab environment. We'll start by understanding what a violin plot reveals, including the median, quartiles, and the full shape of the data distribution.

Learn how to create simple and clear pie charts to visualize your data quickly and effectively

#----

import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

#

df=pd.read_csv('/content/MyData.csv')

df.columns

#

wealth_counts=df['wealth'].value_counts()

plt.pie(wealth_counts, labels=wealth_counts.index, autopct='%1.1f%%', startangle=90)

plt.title('Wealth Distribution')

plt.show()

Master creating bar graphs to show numbers, counts, and estimates effectively

#---

import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

#

df=pd.read_csv('/content/MyData.csv')

df.columns

#

plt.figure(figsize=(8, 6))

sns.countplot(data=df, x='wealth', order=df['wealth'].value_counts().index, palette='viridis')

plt.title('Wealth Category Distribution')

plt.xlabel('Wealth Category')

plt.ylabel('Count')

plt.show()

Discover how to turn raw data into meaningful visuals. Create correlation heatmaps, customize labels, and understand patterns in your datasets.

In this lecture, students will first see a sample boxplot that will be created during the course. This will help them understand the final output before learning the step-by-step process of creating a publication-ready boxplot in Python.

In this lecture, students will learn how to import a new dataset in Python for creating publication-ready boxplots. They will understand how to load the data, check the dataset structure, and prepare the variables needed for boxplot visualization.

In this lecture, students will learn how to write Python code to create a boxplot. They will use the imported dataset, select the required variables, and generate a clear boxplot for data visualization. This lecture will help students understand the basic coding steps before improving the plot for publication-ready presentation.

In this lecture, students will learn how to format important boxplot properties in Python. They will customize elements such as figure size, box width, colors, line style, marker style, and overall appearance to make the boxplot more professional and publication-ready.

In this lecture, students will learn how to customize axis labels and legend labels in Python boxplots. They will understand how clear labels make the boxplot easier to read, interpret, and present in a publication-ready format.

Learn how to calculate and interpret mean, standard deviation, and median for numerical variables, as well as frequency and proportion for categorical variables using Python.

Learn how to explore the relationship between two variables in Python using cross-tabulation, frequency, and proportion for categorical data, as well as comparison of means and medians across groups for numerical data.

Learn how to compare two variables in Python using bivariate analysis. This includes cross-tabulation with frequency and proportion for categorical data, group comparisons of mean, standard deviation, and median for numerical data, and significance testing with p-values.

In this video, we will introduce you to Simple Logistic Regression, one of the most commonly used techniques in machine learning for binary classification. We will walk you through the theory behind logistic regression, followed by a hands-on example using Python and Google Colab. By the end of this video, you will understand how to implement logistic regression in real-world data analysis projects.

In this video, we'll walk you through the process of implementing Simple and Multiple Logistic Regression models in Python using Google Colab. We’ll start by preparing a table with the necessary data, followed by training the models for binary classification. You'll learn how to clean and prepare data for logistic regression, how to build both simple and multiple models, and how to evaluate their performance. We'll also cover the interpretation of coefficients, odds ratios, and key metrics such as accuracy, confusion matrix, and ROC curve. This hands-on guide will help you develop the skills to apply logistic regression to your own datasets and research projects.

Master data science and machine learning using Python in Google Colab. Learn data analysis, visualization, statistical modeling, regression, and modern machine learning techniques. Hands-on projects, practical coding, and real-world examples make this course ideal for beginners, students, and researchers.

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Master tabular data analysis and machine learning using Python in Google Colab. Learn how to clean, explore, visualize, and model real-world datasets (CSV/Excel). Build predictive models with regression, classification, and evaluation techniques. Designed for beginners, students, and researchers who want hands-on experience in applied data science.

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Learn data management and machine learning step by step using Python in Google Colab. This course teaches how to import, clean, transform, and manage tabular data (CSV/Excel), followed by visualization, statistical analysis, regression, and predictive modeling. Ideal for beginners, students, and researchers working with real-world datasets.

In this lecture, you will learn how to import datasets into Python using Google Colab. We will cover reading  Excel files with Pandas, exploring the data structure, and preparing it for machine learning models like Random Forest.

In this lecture, you will learn how to transform categorical variables into numerical values using one-hot encoding. We will use Pandas and Scikit-Learn in Google Colab to create dummy variables and prepare datasets for Random Forest and other machine learning models.

In this lecture, you will learn how to define the class variable (target) and the features (independent variables) in a dataset. Using Python and Google Colab, we will separate input variables from the output variable to prepare data for Random Forest and other machine learning models.

In this lecture, you will learn how to split your dataset into training and testing sets using Python and Google Colab. We will use Scikit-Learn’s train_test_split function to ensure your Random Forest and other machine learning models are trained and evaluated properly.

n this lecture, you will discover how to use the Random Forest library in Python inside Google Colab. You will practice importing the library, building a Random Forest model, and understanding how this machine learning technique improves accuracy with ensemble learning.

Learn how to create and train a Random Forest model in Python using Scikit-Learn. This lecture guides you step by step through model initialization, training with your dataset, and preparing it for evaluation in Google Colab.

Learn how to assess your machine learning model with a confusion matrix. This lecture explains how to create and interpret a confusion matrix in Python for Random Forest, helping you measure accuracy, precision, recall, and overall performance.

Learn how to analyze and rank the importance of features in your dataset using a Random Forest model. This lecture shows how to use Python and Google Colab to interpret which variables impact predictions the most.

Learn how to assess the performance of your Random Forest model with sensitivity (recall) and specificity. This lecture demonstrates how to calculate these metrics from your predictions in Python using Google Colab, helping you interpret your model’s strengths and weaknesses.

In this lecture, you will learn how to generate and interpret the ROC curve for a Random Forest model in Python using Google Colab. We will cover the steps of fitting the model, plotting the ROC curve, and calculating the Area Under the Curve (AUC) to evaluate model performance. By the end, you will understand how to use ROC curves to assess classification accuracy.

In this lecture, you will learn how to plot and interpret the Precision–Recall curve for classification models in Python using Google Colab. We will explain the meaning of precision and recall, show how to generate the curve, and calculate the Area Under the Curve (AUC-PR). By the end, you will understand how to evaluate model performance on imbalanced datasets using the Precision–Recall curve.