Requirements

• basic knowledge of Python & data science

Description

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.

Who this course is 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
• 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