Python Toolbox by DataCamp

Are you fed up with dealing with messy data? Did you know that data scientists spend the majority of their time organizing, cleaning and finding data? You can clean up your data intelligently, it turns out! You can do just that with this course, "Dealing With Missing Data in Python". Learn how to correct missing values in both numerical and categorical data as well as time-series data. You'll learn how to spot patterns in missing data. While working with data related to diabetes and air quality, you will learn how to analyse and impute data and then evaluate its results.

While you can't have a time machine, it would be great to have one that analyzes time. When time is added to any analysis, things can get quite crazy. It's easy to be confused by time zones and day-month boundaries, daylight saving time, and other factors. Python is the best tool to do any kind of time-related analysis. We will use data sets that include information about hurricanes and bicycle rides. We will be discussing how to count events and calculate the time between them. Also, how to plot the data over time. Both standard Python and Pandas will be used. We will also touch on dateutil, which is the only official Python documentation-endorsed timezone library. After this course, you will be able handle date/or time data in any format confidently.

Data scientists are often faced with situations where they have to extract key information, clear up text that contains strings or match patterns in order to find meaningful words. These are all part and parcel of text mining. They are necessary before you can use machine learning algorithms. This course will teach you the basics of string manipulation and regular expressions. This course will show you how to create regular expressions to allow you to find, extract, replace, and match strings. These skills can be used for streaming tweets or movie reviews.

Data scientists should spend their time extracting actionable insights from data and not waiting for the code to finish running. This will allow you to reduce the time it takes to run your Python code, and also save computational resources. This will enable you to pursue your passions in Data Scientist. This course will show you how to make your code faster, cleaner, more efficient, and take advantage of Python's built-in data structures, functions, and modules. How to time and profile code to find bottlenecks. Then, you'll practice eliminating bottlenecks using Python's Standard Library or NumPy. After completing this course, you will be able write Python code efficiently.

Interviews for programming positions are often difficult. You might be asked questions to test your knowledge of programming languages. To test your thinking skills, you might be asked questions. You will be asked questions about the tools available when you apply for a job as a data scientist. To achieve the best results, you will need to work hard. To demonstrate your competence, it is important to practice. This course is designed to help data scientists just beginning their career. This course is also a refresher for people looking for new opportunities. To help you prepare for the Python coding interview, you will be taught both basic and advanced topics. Because it's not a step-by–step course, some exercises might be more difficult than others. Interviews can be difficult, but who is to say?