IT Courses Online > Development > Data Science > One Week of Data Science in Python – New 2024!
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4.63
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One Week of Data Science in Python – New 2024!

Master Data Science Fundamentals Quickly & Efficiently in one week! Course is Designed for Busy People
Instructor:
Dr. Ryan Ahmed, Ph.D., MBA
3,399 students enrolled
English [Auto]
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

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 don’t have the time to get there 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.

So who is this course for?

The course targets anyone wanting to gain a fundamental understanding of Data Science and solve practical, real-world business problems.

In this course:

  1. You will have an actual practical project-based learning experience. We will build over ten projects together

  2. You will have access to all the codes and slides

  3. You will get a certificate of completion that you can post on your LinkedIn profile to showcase your skills in Data Science to employers.

  4. All this comes with a 30-day money-back guarantee, so you can give a course a try risk-free!

Check out the preview videos and the outline to get an idea of the projects we will cover.

Enroll today, and let’s harness the power of Data Science together!

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Includes

13 hours on-demand video
1 article
Certificate of Completion

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