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Data Science Bootcamp in Python- 250+ Exercises to Master

  • Development
  • Dec 23, 2024
SynopsisData Science Bootcamp in Python: 250+ Exercises to Master, av...
Data Science Bootcamp in Python- 250+ Exercises to Master  No.1

Data Science Bootcamp in Python: 250+ Exercises to Master, available at $49.99, has an average rating of 4.3, with 65 lectures, based on 61 reviews, and has 37743 subscribers.

You will learn about solve over 250 exercises in data science in Python deal with real programming problems deal with real problems in data science work with libraries numpy, pandas, seaborn, plotly, scikit-learn, opencv, tensorflow work with documentation guaranteed instructor support This course is ideal for individuals who are aspiring data scientists who want to learn and practice data science concepts and techniques using Python or students or individuals with a background in statistics, mathematics, or related fields who want to apply their knowledge to real-world data analysis and gain practical experience in Python or programmers or software developers who want to expand their skillset to include data science and machine learning using Python or professionals working in data-related roles who want to enhance their data analysis and machine learning skills using Python for better decision-making and insights or data analysts or business analysts who want to upgrade their skills to perform more advanced data analysis, visualization, and modeling using Python or self-learners who are interested in data science and want to acquire practical experience by solving a variety of data-related exercises in Python It is particularly useful for aspiring data scientists who want to learn and practice data science concepts and techniques using Python or students or individuals with a background in statistics, mathematics, or related fields who want to apply their knowledge to real-world data analysis and gain practical experience in Python or programmers or software developers who want to expand their skillset to include data science and machine learning using Python or professionals working in data-related roles who want to enhance their data analysis and machine learning skills using Python for better decision-making and insights or data analysts or business analysts who want to upgrade their skills to perform more advanced data analysis, visualization, and modeling using Python or self-learners who are interested in data science and want to acquire practical experience by solving a variety of data-related exercises in Python.

Enroll now: Data Science Bootcamp in Python: 250+ Exercises to Master

Summary

Title: Data Science Bootcamp in Python: 250+ Exercises to Master

Price: $49.99

Average Rating: 4.3

Number of Lectures: 65

Number of Published Lectures: 65

Number of Curriculum Items: 65

Number of Published Curriculum Objects: 65

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

  • solve over 250 exercises in data science in Python
  • deal with real programming problems
  • deal with real problems in data science
  • work with libraries numpy, pandas, seaborn, plotly, scikit-learn, opencv, tensorflow
  • work with documentation
  • guaranteed instructor support
  • Who Should Attend

  • aspiring data scientists who want to learn and practice data science concepts and techniques using Python
  • students or individuals with a background in statistics, mathematics, or related fields who want to apply their knowledge to real-world data analysis and gain practical experience in Python
  • programmers or software developers who want to expand their skillset to include data science and machine learning using Python
  • professionals working in data-related roles who want to enhance their data analysis and machine learning skills using Python for better decision-making and insights
  • data analysts or business analysts who want to upgrade their skills to perform more advanced data analysis, visualization, and modeling using Python
  • self-learners who are interested in data science and want to acquire practical experience by solving a variety of data-related exercises in Python
  • Target Audiences

  • aspiring data scientists who want to learn and practice data science concepts and techniques using Python
  • students or individuals with a background in statistics, mathematics, or related fields who want to apply their knowledge to real-world data analysis and gain practical experience in Python
  • programmers or software developers who want to expand their skillset to include data science and machine learning using Python
  • professionals working in data-related roles who want to enhance their data analysis and machine learning skills using Python for better decision-making and insights
  • data analysts or business analysts who want to upgrade their skills to perform more advanced data analysis, visualization, and modeling using Python
  • self-learners who are interested in data science and want to acquire practical experience by solving a variety of data-related exercises in Python
  • The “Data Science Bootcamp in Python: 250+ Exercises to Master” is a highly comprehensive course designed to catapult learners into the exciting field of data science using Python. This bootcamp-style course allows participants to gain hands-on experience through extensive problem-solving exercises covering a wide range of data science topics.

    The course is structured into multiple sections that cover core areas of data science. These include data manipulation and analysis using Python libraries like Pandas and NumPy, data visualization with matplotlib and seaborn, and machine learning techniques using scikit-learn.

    Each exercise within the course is designed to reinforce a particular data science concept or skill, challenging participants to apply what they’ve learned in a practical context. Detailed solutions for each problem are provided, allowing learners to compare their approach and gain insights into best practices and efficient methods.

    The “Data Science Bootcamp in Python: 250+ Exercises to Master” course is ideally suited for anyone interested in data science, whether you’re a beginner aiming to break into the field, or an experienced professional looking to refresh and broaden your skillset. This course emphasizes practical skills and applications, making it a valuable resource for aspiring data scientists and professionals looking to apply Python in their data science endeavours.

    Data Scientist – Unveiling Insights from Data Universe!

    A data scientist is a skilled professional who leverages their expertise in mathematics, statistics, programming, and domain knowledge to extract meaningful insights and valuable knowledge from complex datasets. They utilize various analytical techniques, statistical models, and machine learning algorithms to discover patterns, trends, and correlations within the data.

    The role of a data scientist involves tasks such as data collection, data cleaning, exploratory data analysis, feature engineering, and building predictive or prescriptive models. They work closely with stakeholders to understand business needs, formulate data-driven strategies, and communicate findings effectively to support decision-making processes.

    Data scientists possess strong analytical and problem-solving skills, as well as a deep understanding of statistical concepts and programming languages such as Python or R. They are proficient in data manipulation, data visualization, and machine learning techniques.

    In addition to technical skills, data scientists possess strong communication and storytelling abilities. They can translate complex data findings into actionable insights and effectively communicate them to both technical and non-technical audiences.

    Data scientists play a crucial role in various industries, including finance, healthcare, marketing, technology, and more. They help organizations make informed decisions, optimize processes, identify new opportunities, and solve complex problems by harnessing the power of data.

    Packages that you will use in the exercises:

  • numpy

  • pandas

  • seaborn

  • plotly

  • scikit-learn

  • opencv

  • tensorflow

  • Course Curriculum

    Chapter 1: Tips

    Lecture 1: A few words from the author

    Lecture 2: Configuration

    Lecture 3: Tip

    Chapter 2: –NUMPY–

    Lecture 1: Intro

    Chapter 3: 001-010 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 4: 011-020 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 5: 021-030 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 6: 031-040 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 7: 041-050 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 8: 051-060 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 9: 061-070 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 10: 071-080 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 11: 081-090 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 12: 091-100 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 13: –PANDAS–

    Lecture 1: Intro

    Chapter 14: 101-110 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 15: 111-120 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 16: 121-130 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 17: 131-140 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 18: 141-150 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 19: 151-160 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 20: 161-170 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 21: 171-180 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 22: 181-190 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 23: 191-200 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 24: –SUMMARY–

    Lecture 1: Intro

    Chapter 25: 201-210 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 26: 211-220 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 27: 221-230 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 28: 231-240 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 29: 241-250 Exercises

    Lecture 1: Exercises

    Lecture 2: Exercises + Solutions

    Chapter 30: Configuration (optional)

    Lecture 1: Info

    Lecture 2: Requirements

    Lecture 3: Google Colab + Google Drive

    Lecture 4: Google Colab + GitHub

    Lecture 5: Google Colab – Intro

    Lecture 6: Anaconda installation – Windows 10

    Lecture 7: Introduction to Spyder

    Lecture 8: Anaconda installation – Linux

    Chapter 31: Bonus

    Lecture 1: Bonus

    Instructors

  • Data Science Bootcamp in Python- 250+ Exercises to Master  No.2
    Pawe? Krakowiak
    Python Developer/Data Scientist/Stockbroker
  • Rating Distribution

  • 1 stars: 3 votes
  • 2 stars: 3 votes
  • 3 stars: 7 votes
  • 4 stars: 13 votes
  • 5 stars: 35 votes
  • Frequently Asked Questions

    How long do I have access to the course materials?

    You can view and review the lecture materials indefinitely, like an on-demand channel.

    Can I take my courses with me wherever I go?

    Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!