Python for Data Science Pro: The Complete Mastery Course

Name: Python for Data Science Pro: The Complete Mastery Course
Rating: 4.3 (433 reviews)

Become a Data Science Pro: Master Data Analysis, Visualization, and Machine Learning with Python

Created bySara Academy

Last updated 5/2025

English

English [Auto],

What you'll learn

What is Python Data Science and Workflow?
Control Flow: Conditionals and Loops
Understanding Arrays and Matrices
Data Cleaning and Preparation
Merging and Joining Data
Subplots and Figures
Measures of Central Tendency
Measures of Variability
Normal, Binomial, and Other Distributions
Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning
Handling Imbalanced Data
Linear and Logistic Regression
Sentiment Analysis

Course content

6 sections • 28 lectures • 4h 7m total length

Variables, data types, and operators9:20
Explore variables, data types, and operators in Python for data science, including integers, floats, strings, and booleans, with arithmetic and comparison examples and printouts.
Control Flow: Conditionals and Loops6:14
Master conditionals and loops in Python with if, else, and else if, using a temperature example and for and while loops to demonstrate control flow and output messages.
Functions and Modules7:08
Define and use Python functions with parameters and return values, and import modules like math to reuse code across data science projects and compute circle areas.
Functions with Inputs8:10
Explore how functions with inputs use parameters and arguments to create dynamic, reusable code, with examples calculating the area of a rectangle, adding numbers, and personalized greetings.

Understanding Arrays and Matrices7:40
Learn how numpy enables efficient arrays and two-dimensional matrices in Python for data science, with practical steps to create arrays, build a 2x3 matrix, and perform addition and other operations.
Array Operations8:06
Explore array operations in Python with NumPy, performing element-wise addition, subtraction, multiplication, division, and broadcasting to handle large data sets in data science.
DataFrames and Series6:53
Learn to work with pandas series and data frames in Python for data science, including creating, indexing, and manipulating one-dimensional and two-dimensional data structures.
Data Cleaning and Preparation8:23
Learn to perform data cleaning with the pandas library by identifying and filling missing values, filtering, and removing duplicates to prepare data for analysis.
Handling Missing Data6:47
Identify and handle missing data in pandas, locate gaps with isnull, and drop rows with missing values to prepare clean data for analysis.
Merging and Joining Data9:09
Learn to merge and join data frames in Python using Pandas, combining on a common column and joining on different column names to align related datasets.
Sorting and Filtering Data10:41
Learn to sort a pandas data frame by single and multiple columns, in descending and ascending orders, and filter rows based on conditions like score greater than 85.
Grouping and Aggregation9:36
Explore grouping and aggregation in Python data science with pandas. Group data by category and compute total sales, then customize aggregations to measure range (max minus min).

Basic Plots: Line, Bar, Scatter11:52
Learn to create line, bar, and scatter plots in Python with matplotlib, visualizing trends, category comparisons, and variable relationships.
Customizing Plots10:59
Explore customizing plots in Python data science, styling line plots with color, dash patterns, markers, and widths, and enhancing bar plots with colors, edge colors, and grid lines for clarity.
Subplots and Figures6:18
Create and customize subplots in python using the matplot library, building a two-plot figure to compare datasets with color, titles, and clear visualization.
Creating Interactive Charts9:26
Create interactive charts in Python with Plotly, including line charts and scatter plots, enabling zooming, hovering, and dynamic titles and axis labeling.

Measures of Central Tendency8:25
Explore measures of central tendency in Python with pandas, computing mean, median, and mode from a data frame to summarize a score column.
Measures of Variability7:29
Explore measures of variability in Python using pandas, including range, variance, and standard deviation, demonstrated on a sample data frame of scores.
Normal, Binomial, and Other Distributions12:29
Explore normal, binomial, and Poisson distributions in Python with SciPy and NumPy, learning to generate data, compute probabilities, and visualize results.
Null and Alternative Hypotheses9:06
Explore hypothesis testing in python by comparing means of independent samples with a two-sample t test and applying a chi-square test for independence on a contingency table of categorical variables.

Feature Scaling and Normalization8:03
Master feature scaling and normalization in Python data science with min-max scaling, standardization, and robust scaling to boost performance for algorithms like k-nearest neighbors and logistic regression.
Encoding Categorical Variables11:59
Encode categorical variables in python with label encoding, ordinal encoding, and one-hot encoding; label encoding maps categories to integers, ordinal encoding preserves order, and one-hot creates binary columns.
Handling Imbalanced Data9:25
Balance imbalanced data in python by applying random oversampling and semantic minority oversampling (smote) using imblearn, to improve model learning and prediction accuracy.
Linear and Logistic Regression11:23
Learn how to build linear and logistic regression models in Python for data science, using numpy and scikit-learn to predict continuous outcomes and perform binary classification with a classification report.

Introduction to Time Series Data7:02
Explore time series data in Python with pandas to create a data frame indexed by date ranges and visualize trends using matplotlib for forecasting and pattern analysis.
Decomposition, ARIMA Models10:57
Decompose time series into trend, seasonality, and residuals with the seasonal decomposition function, then fit an ARIMA model in Python to forecast future values.
Text Preprocessing8:09
Learn text preprocessing in Python for data science by cleaning text, removing noise, and normalizing data. Practice punctuation removal, lowercasing, and tokenization with NLTK to prepare text for analysis.
Sentiment Analysis5:53
Explore sentiment analysis in Python, using TextBlob and Vader to determine positive, negative, or neutral tones in text with polarity scores, including social media examples.

Requirements

No prior knowledge is required!

Description

Elevate your data science skills to a professional level with "Python for Data Science Pro: The Complete Mastery Course." This comprehensive course is designed for individuals who want to master Python for data analysis, machine learning, and data visualization, ensuring you are fully equipped to tackle complex data challenges in any industry.

Starting with the fundamentals of Python, you’ll quickly progress to advanced topics, including data manipulation with Pandas, statistical analysis, and machine learning with scikit-learn. You’ll also explore powerful data visualization tools like Matplotlib and Seaborn, enabling you to present data insights clearly and effectively. The course is packed with hands-on projects and real-world datasets, providing you with practical experience that mirrors the demands of the data science field.

By the end of this course, you’ll have the expertise to analyze, visualize, and model data using Python, making you a highly sought-after data science professional.

What You'll Learn:

Python Basics for Data Science: Get up to speed with Python programming, including syntax, data structures, and essential libraries.
Data Manipulation with Pandas: Learn to clean, manipulate, and analyze large datasets efficiently.
Statistical Analysis: Master statistical methods and techniques to uncover insights from data.
Machine Learning with scikit-learn: Build and evaluate machine learning models to predict outcomes and uncover patterns.
Data Visualization: Create impactful visualizations using Matplotlib and Seaborn to communicate data insights effectively.
Best Practices: Learn industry-standard practices for writing clean, efficient, and reproducible Python code.

Who This Course is For:

Aspiring data scientists who want to master Python for data science.
Python developers looking to specialize in data analysis and machine learning.
Data analysts eager to upgrade their skills with advanced data science techniques.
Professionals in any industry who want to leverage data science for decision-making and problem-solving.

By enrolling in this course, you’ll gain a complete mastery of Python for data science, from data manipulation to machine learning. This course is your pathway to becoming a proficient data scientist, capable of extracting valuable insights from data and driving impactful decisions in any organization. Start your journey to data science excellence today!

Who this course is for:

Anyone who want to learn master Python for data science.
Python developers looking to specialize in data analysis and machine learning.

Python for Data Science Pro: The Complete Mastery Course

What you'll learn

Explore related topics

Course content

Module 1: Introduction to Python and Data Science4 lectures • 31min

Module 2: Data Manipulation with Python8 lectures • 1hr 7min

Module 3: Data Visualization4 lectures • 39min

Module 4: Statistical Analysis4 lectures • 37min

Module 5: Introduction to Machine Learning4 lectures • 41min

Module 6: Advanced Topics in Data Science4 lectures • 32min

Requirements

Description

Who this course is for: