Description
What you’ll learn
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Learn to use Pandas for Data Analysis
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Learn to work with numerical data in Python
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Learn statistics and math with Python
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Learn how to code in Jupyter Notebook
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Learn how to install packages in Python
When it comes to being attractive, data scientists are already there. In a highly competitive job market, it is tough to keep them after they have been hired. People with a unique mix of scientific training, computer expertise, and analytical abilities are hard to find.
Like the Wall Street “quants” of the 1980s and 1990s, modern-day data scientists are expected to have a similar skill set. People with a background in physics and mathematics flocked to investment banks and hedge funds in those days because they could come up with novel algorithms and data methods.
That being said, data science is becoming one of the most well-suited occupations for success in the twenty-first century. It is computerized, programming-driven, and analytical in nature. Consequently, it comes as no surprise that the need for data scientists has been increasing in the employment market over the last several years.
The supply, on the other hand, has been quite restricted. It is challenging to get the knowledge and abilities required to be recruited as a data scientist.
Lots of resources for learning Python are available online. Because of this, students frequently get overwhelmed by Python’s high learning curve.
It’s a whole new ball game in here! Step-by-step instruction is the hallmark of this course. Throughout each subsequent lesson, we continue to build on what we’ve previously learned. Our goal is to equip you with all the tools and skills you need to master Python, Numpy & Pandas.
You’ll walk away from each video with a fresh idea that you can put to use right away!
All skill levels are welcome in this course, and even if you have no prior programming or statistical experience, you will be able to succeed!
Who this course is for:
- No previous skills or expertise required. Only a drive to succeed!
Course content
- Python Quick Refresher (Optional)21 lectures • 41min
- Python Quick Refresher (Optional)
- Essential Python Libraries for Data Science7 lectures • 6min
- Essential Python Libraries for Data Science
- Fundamental NumPy Properties5 lectures • 18min
- Fundamental NumPy Properties
- Mathematics for Data Science15 lectures • 20min
- Mathematics for Data Science
- Python Pandas DataFrames & Series22 lectures • 34min
- Python Pandas DataFrames & Series
- Data Cleaning10 lectures • 32min
- Data Cleaning
- Data Visualization using Python12 lectures • 28min
- Data Visualization using Python
- Exploratory Data Analysis10 lectures • 30min
- Exploratory Data Analysis
- Time Series in Python5 lectures • 17min
- Time Series in Python
- BONUS Section – Don’t Miss Out1 lecture • 1min
- BONUS Section – Don’t Miss Out
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