Data Science Bootcamp in Python: 250+ Exercises to Master
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:
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numpy
-
pandas
-
seaborn
-
plotly
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scikit-learn
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opencv
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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
-
Paweł 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
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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!
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