The Evolution of Data Science
Data science has become a powerful and effective tool for businesses to gain important insights and improve their performance. Data science has taken over almost every industry in the world. There is no field in the world today that does not use data.
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| How to Use Python to Analyze Data with Data Science Harvard course |
It is difficult to understand all the data science concepts and tools you learn without implementing them in several data science projects. Working on data science projects will help you improve your data science skills.
If you are seriously thinking about coming up with data science project ideas and how to start and finish a data science project, this course (Introduction to Data Science with Python) is for you.
Data science has recently gained significant interest from companies on the one hand, and individuals wishing to learn this field on the other hand, as it focuses mainly on knowing and understanding the data owned by a particular company or institution and extracting knowledge from it. This course aims to introduce the basic concepts of data science by using the programming language “Python“, which is one of the most important and popular programming languages in the field of data science.
This course will be full of practical examples and interactive exercises that will consolidate and clarify theoretical concepts.
About the Data Science with Python Course
Data science is a rapidly advancing field that uses computation and logical strategies to analyze complex data sets. Data researchers use a range of programming languages, such as Python, to manipulate and analyze data.
This free online course from edx focuses on the uses of Python in data science. By the end of the course, you will have gained a basic understanding of machine learning models and understand the core concepts of both machine learning and artificial insights using Python. You will also consider regression models (direct, multilinear, and polynomial) and classification models (such as kNN and imputed arithmetic), and the use of popular libraries such as sklearn, Pandas, matplotlib, and numPy.
The course will cover fundamental concepts of machine learning such as: choosing the right complexity, anticipating overfitting, regularization, assessing weakness, balancing trade-offs, and evaluating models.
Taking this course will give you the opportunity to build your confidence in using Python, and set you up for advanced assistance in the field of machine learning and artificial insights, thus advancing your career.
Who is the Data Science with Python course for?
- A minimum of programming (Python preferred) and statistics knowledge is required to succeed in this course.
- The Python core requirement can be met through the introductory Python course offered through CS50 Introduction to Programming with Python.
- The statistics core requirement can be met through Fat Chance or Stat110 offered through HarvardX.
In Data Science with Python Course, you will learn to:
- Gain hands-on experience and practice using Python to solve real-world data science challenges.
- Practice programming and coding using Python for modeling, statistics, and storytelling.
- Use popular libraries such as Pandas, numPy, matplotlib, and SKLearn.
- Run basic machine learning models using Python, evaluate the performance of these models, and apply these models to real-world problems.
- Build a foundation for using Python in machine learning and artificial intelligence, preparing you for future study of Python.
Data Science with Python Course Topics:
- Linear Regression.
- Multiple Linear Regression and Polynomial Regression.
- Model Selection and Cross-Validation.
- Bias, Variance and Hyperparameters.
- Classification and Logistic Regression.
- Multiple Logistic Regression and Missing.
- Bootstrapping, Confidence Intervals and Hypothesis Testing.
- Final Project.
Course link:
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