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One Week of Data Science in Python

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4.2

One Week of Data Science in Python

Master Data Science Fundamentals Quickly & Efficiently in one week! Course is Designed for Busy People

updated on icon Updated on Apr, 2024

language icon Language - English

person icon Ligency

category icon Python,Development,Data Science

Lectures -125

Resources -12

Duration -13 hours

4.2

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Course Description

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 Data Science project lifecycle, what recruiters look for, and what kind of 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.

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!

WHO SHOULD TAKE THIS COURSE

  • The course targets anyone wanting to gain a fundamental understanding of Data Science and solve practical, real-world business problems.
  • Data Scientists who want to advance their careers and build their portfolios.
  • Seasoned consultants wanting to transform businesses by leveraging Data Science.
  • Tech enthusiasts who are passionate and new to Data Science & AI and want to gain practical experience.

Goals

What will you learn in this course:

  • 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.
  • Perform data visualization using Seaborn and Matplotlib libraries.
  • Understand machine learning regression fundamentals.
  • Learn how to optimize model parameters.
  • Perform data visualization and fundamental exploratory data analysis.
  • Understand the theory and intuition behind boosting.
  • Train several machine learning 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.
  • Plot various models’ performance on the model leaderboard.
  • Understand bias-variance trade-off and L1 and L2 regularization.
One Week of Data Science in Python

Curriculum

Check out the detailed breakdown of what’s inside the course

Course Introduction, Welcome Message & Data Science Starter Pack!
9 Lectures
  • play icon Course-promo 03:24 03:24
  • play icon Course Welcome Message 01:06 01:06
  • play icon Course Outline 02:36 02:36
  • play icon What is Data Science? 10:24 10:24
  • play icon What is a Typical Data Scientist Profile, Education, Experience & Salary? 14:36 14:36
  • play icon What do Data Scientists REALLY do? 03:21 03:21
  • play icon What do Recruiters Look for in Data Science Applicants? 05:01 05:01
  • play icon What Data Science Jobs are Available Out there? 11:00 11:00
  • play icon Resources
Day 1: Data Wrangling, Exploratory Data Analysis (EDA) & Feature Engineering
22 Lectures
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Day 2: Effective Data Visualization in Data Science
18 Lectures
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Day 3: Regression Analysis in Data Science
15 Lectures
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Day 4: Classification Analysis in Data Science
23 Lectures
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Day 5: Data Science on Autopilot
15 Lectures
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Day 6: Models Optimization
19 Lectures
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Day 7: Deep Learning
5 Lectures
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Appendix & Optional Content for "Regression in DS" Section
7 Lectures
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Appendix & Optional Content for Models Optimization
3 Lectures
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Bonus
4 Lectures
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Instructor Details

Ligency

Ligency

Hi there,

We are the Ligency team. You will be hearing from us when new Ligency courses are released, when we publish new podcasts, blogs, share cheatsheets and more!

We are here to help you stay on the cutting edge of Data Science and Technology. 

See you in class,

Sincerely,

Ligency Team!

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