One Week of Data Science in Python – New 2025!
Udemy Coupon Code for One Week of Data Science in Python – New 2025! Course. Master Data Science Fundamentals Quickly & Efficiently in one week! Course is Designed for Busy People
Created by Dr. Ryan Ahmed, Ph.D., MBA, SuperDataScience Team, Ligency Team | 13 hours on-demand video course
Data Science Course Overview
One Week of Data Science in Python – New 2025!
Do you want to learn Data Science and build robust applications Quickly and Efficiently?
Are you an absolute beginner who wants to break into Data Science and look for a course that includes all the basics you need?
Are you a busy aspiring entrepreneur who wants to maximize business revenues and reduce costs with Data Science but lacks the time to do so quickly and efficiently?
This course is for you if the answer is yes to any of these questions!
Data Science is one of the hottest tech fields to be in now!
The field is exploding with opportunities and career prospects.
Data Science is widely adopted in many sectors, such as banking, healthcare, transportation, and technology.
In business, Data Science is applied to optimize business processes, maximize revenue, and reduce cost.
This course aims to provide you with knowledge of critical aspects of data science in one week and in a practical, easy, quick, and efficient way.
This course is unique and exceptional in many ways. It includes several practice opportunities, quizzes, and final capstone projects.
Every day, we will spend 1-2 hours together and master a data science topic.
First, we will start with the Data Science essential starter pack and master key Data Science Concepts, including the Data Science project lifecycle, what recruiters look for, and what jobs are available.
Next, we will understand exploratory data analysis and visualization techniques using Pandas, matplotlib, and Seaborn libraries.
In the following section, we will learn about regression fundamentals. We will learn how to build, train, test, and deploy regression models using the Scikit Learn library.
In the following section, we will learn about hyperparameter optimization strategies such as grid search, randomized search, and Bayesian optimization.
Next, we will learn how to train several classification algorithms such as Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Random Forest Classifier, and Naïve Bayes in SageMaker and SK-Learn libraries.
Next, we will cover Data Science on Autopilot! We will learn how to use the AutoGluon library for prototyping multiple AI/ML models and deploying the best one.
What you’ll learn
- Perform statistical analysis on real world datasets
- Understand feature engineering strategies and tools
- Perform one hot encoding and normalization
- Understand the difference between normalization and standardization
- Deal with missing data using pandas
- Change pandas DataFrame datatypes
- Define a function and apply it to a Pandas DataFrame column
- Perform Pandas operations and filtering
- Calculate and display correlation matrix heatmap
- Perform data visualization using Seaborn and Matplotlib libraries
- Plot single line plot, pie charts and multiple subplots using matplotlib
- Plot pairplot, countplot, and correlation heatmaps using Seaborn
- Plot distribution plot (distplot), Histograms and scatterplots
- Understand machine learning regression fundamentals
- Learn how to optimize model parameters using least sum of squares
- Split the data into training and testing using SK Learn Library
- Perform data visualization and basic exploratory data analysis
- Build, train and test our first regression model in Scikit-Learn
- Assess trained machine learning regression model performance
- Understand the theory and intuition behind boosting
- Train an XG-boost algorithm in Scikit-Learn to solve regression type problems
- Train several machine learning models classifier models such as Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Random Forest Classifier
- Assess trained model performance using various KPIs such as accuracy, precision, recall, F1-score, AUC and ROC.
- Compare the performance of the classification model using various KPIs.
- Apply autogluon to solve regression and classification type problems
- Use AutoGluon library to perform prototyping of AI/ML models using few lines of code
- Plot various models’ performance on model leaderboard
- Optimize regression and classification models hyperparameters using SK-Learn
- Learn the difference between various hyperparameters optimization strategies such as grid search, randomized search, and Bayesian optimization.
- Perform hyperparameters optimization using Scikit-Learn library.
- Understand bias variance trade-off and L1 and L2 regularization
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Data Science in Python Course Reviews
Everything You Need to Know About One Week of Data Science in Python – New 2025!
This course offers a comprehensive and well-structured introduction to Data Science. Dr. Ryan Ahmed, the instructor, brings a wealth of expertise in Development, making this course both informative and engaging.
The course structure is easy to follow. Each section, for example, covers a different aspect of Data Science Course, ensuring a logical progression through the material. It includes video lectures, readings, and hands-on exercises, which make complex concepts accessible and practical.
Moreover, The Instructors explains each topic clearly and concisely. He supports the lessons with plenty of examples and exercises, which help students grasp the material effectively.
What I appreciated most about this course is its practical focus. For instance, the instructor emphasizes teaching skills and knowledge that are directly applicable to real-world scenarios. Additionally, students gain access to helpful resources such as templates, checklists, and cheat sheets.
Another standout feature is the platform itself. Udemy offers flexibility, allowing students to learn at their own pace and access course materials from anywhere with an internet connection. Furthermore, the multiple payment options make it easy for students to choose a plan that suits their budget.
In addition, the course community is highly active, with forums where students can ask questions and engage with peers. The instructor, consequently, is very responsive and addresses student inquiries and feedback promptly.
Overall, I highly recommend the One Week of Data Science in Python – New 2025! to anyone looking to learn Data Science This well-organized and practical course equips you with the skills and knowledge you need to succeed in this field.
