HOT
blank

100 Days of Code: Data Scientist Challenge

Development

Rating4.4
Enroll Now

100 Days of Code: Data Scientist Challenge, available at $64.99, has an average rating of 4.4, with 333 lectures, 319 quizzes, based on 17 reviews, and has 15676 subscribers.

You will learn about solve over 300 exercises in Python deal with real programming problems work with documentation guaranteed instructor support This course is ideal for individuals who are aspiring data scientists or individuals interested in the field of data science who want to develop their skills and knowledge in data science using Python or students or individuals studying data science, statistics, or related fields who want to gain hands-on experience in applying data science concepts and techniques using Python or professionals working in data-related roles who want to enhance their data science skills and stay up-to-date with best practices and modern tools in the field or Python developers who want to transition into the field of data science and learn how to use Python for data manipulation, analysis, and machine learning or self-learners or enthusiasts who are motivated to commit to a coding challenge and want to develop a consistent coding habit while focusing on data science concepts or data analysts or researchers who want to expand their knowledge and skill set to include data science techniques and tools using Python It is particularly useful for aspiring data scientists or individuals interested in the field of data science who want to develop their skills and knowledge in data science using Python or students or individuals studying data science, statistics, or related fields who want to gain hands-on experience in applying data science concepts and techniques using Python or professionals working in data-related roles who want to enhance their data science skills and stay up-to-date with best practices and modern tools in the field or Python developers who want to transition into the field of data science and learn how to use Python for data manipulation, analysis, and machine learning or self-learners or enthusiasts who are motivated to commit to a coding challenge and want to develop a consistent coding habit while focusing on data science concepts or data analysts or researchers who want to expand their knowledge and skill set to include data science techniques and tools using Python.

Enroll now: 100 Days of Code: Data Scientist Challenge

Summary

Title: 100 Days of Code: Data Scientist Challenge

Price: $64.99

Average Rating: 4.4

Number of Lectures: 333

Number of Quizzes: 319

Number of Published Lectures: 333

Number of Published Quizzes: 319

Number of Curriculum Items: 652

Number of Published Curriculum Objects: 652

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

  • solve over 300 exercises in Python
  • deal with real programming problems
  • work with documentation
  • guaranteed instructor support

Who Should Attend

  • aspiring data scientists or individuals interested in the field of data science who want to develop their skills and knowledge in data science using Python
  • students or individuals studying data science, statistics, or related fields who want to gain hands-on experience in applying data science concepts and techniques using Python
  • professionals working in data-related roles who want to enhance their data science skills and stay up-to-date with best practices and modern tools in the field
  • Python developers who want to transition into the field of data science and learn how to use Python for data manipulation, analysis, and machine learning
  • self-learners or enthusiasts who are motivated to commit to a coding challenge and want to develop a consistent coding habit while focusing on data science concepts
  • data analysts or researchers who want to expand their knowledge and skill set to include data science techniques and tools using Python

Target Audiences

  • aspiring data scientists or individuals interested in the field of data science who want to develop their skills and knowledge in data science using Python
  • students or individuals studying data science, statistics, or related fields who want to gain hands-on experience in applying data science concepts and techniques using Python
  • professionals working in data-related roles who want to enhance their data science skills and stay up-to-date with best practices and modern tools in the field
  • Python developers who want to transition into the field of data science and learn how to use Python for data manipulation, analysis, and machine learning
  • self-learners or enthusiasts who are motivated to commit to a coding challenge and want to develop a consistent coding habit while focusing on data science concepts
  • data analysts or researchers who want to expand their knowledge and skill set to include data science techniques and tools using Python

The “100 Days of Code: Data Scientist Challenge” course is an intensive, practical-oriented program that aims to transform learners into proficient data scientists within 100 days. This course follows the recognized #100DaysOfCode challenge, inviting participants to engage in data science coding tasks for a minimum of an hour daily for 100 consecutive days. This course allows students to take a hands-on approach in learning data science, featuring a multitude of practical exercises spanning 100 days.

Each day of the challenge presents a fresh set of tasks, each tailored to explore various facets of data science including data extraction, preprocessing, modeling, analysis, and visualization. These exercises are set within the context of real-world scenarios, and range from simple tasks to more complex problems, covering topics such as data cleaning, exploratory data analysis, machine learning, deep learning, and more.

This course covers a wide range of Python libraries like Pandas, NumPy, Matplotlib, Seaborn, and Scikit-Learn, and it does not shy away from introducing the students to more advanced concepts such as Natural Language Processing (NLP), Time-Series Analysis, and Neural Networks.

With over 100 hands-on exercises, the students will be able to solidify their understanding of data science theory, develop practical coding skills and problem-solving abilities that will be crucial in a real job setting.

This course encourages a “learn by doing” approach, where students will be coding and solving problems each day, thus reinforcing the concepts learned. By the end of the 100 days, students will have built a robust portfolio showcasing their ability to tackle a variety of data science problems, proving to potential employers their readiness for the data science industry.

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.

Course Curriculum

Chapter 1: Tips

Lecture 1: A few words from the author

Lecture 2: Configuration

Lecture 3: Requirements

Chapter 2: Data Scientist

Lecture 1: Key responsibilities

Lecture 2: Skill set

Lecture 3: Educational background & Career path

Chapter 3: Starter

Lecture 1: Solution 0

Chapter 4: Day 1 – np.all() & np.any()

Lecture 1: Solution 1

Lecture 2: Solution 2

Lecture 3: Solution 3

Lecture 4: Solution 4

Chapter 5: Day 2 – np.isnan(), np.allclose() & np.equal()

Lecture 1: Solution 1

Lecture 2: Solution 2

Lecture 3: Solution 3

Chapter 6: Day 3 – np.greater(), np.zeros(), np.ones() & np.full()

Lecture 1: Solution 1

Lecture 2: Solution 2

Lecture 3: Solution 3

Chapter 7: Day 4 – np.arange() & np.eye()

Lecture 1: Solution 1

Lecture 2: Solution 2

Lecture 3: Solution 3

Chapter 8: Day 5 – np.random.rand(), np.random.randn() & np.sqrt()

Lecture 1: Solution 1

Lecture 2: Solution 2

Lecture 3: Solution 3

Chapter 9: Day 6 – np.nditer(), np.linspace() & np.random.choice()

Lecture 1: Solution 1

Lecture 2: Solution 2

Lecture 3: Solution 3

Chapter 10: Day 7 – np.diag(), np.save(), np.load(), np.savetxt() & np.loadtxt()

Lecture 1: Solution 1

Lecture 2: Solution 2

Lecture 3: Solution 3

Chapter 11: Day 8 – np.reshape(), np.tolist() & np.pad()

Lecture 1: Solution 1

Lecture 2: Solution 2

Lecture 3: Solution 3

Chapter 12: Day 9 – np.zeros(), np.append() & np.intersect1d()

Lecture 1: Solution 1

Lecture 2: Solution 2

Lecture 3: Solution 3

Chapter 13: Day 10 – np.unique(), np.argmax() & np.sort()

Lecture 1: Solution 1

Lecture 2: Solution 2

Lecture 3: Solution 3

Lecture 4: Solution 4

Chapter 14: Day 11 – np.where(), np.ravel() & np.zeros_like()

Lecture 1: Solution 1

Lecture 2: Solution 2

Lecture 3: Solution 3

Chapter 15: Day 12 – np.full_like(), np.tri() & np.random.randint()

Lecture 1: Solution 1

Lecture 2: Solution 2

Lecture 3: Solution 3

Instructors

Rating Distribution

  • 1 stars: 0 votes
  • 2 stars: 0 votes
  • 3 stars: 2 votes
  • 4 stars: 5 votes
  • 5 stars: 10 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!

You may also like

Top 10 Ethical AI Courses to Learn in November 2024

Top 10 Game Development Courses to Learn in November 2024

Top 10 Software Testing Courses to Learn in November 2024