FREE COURSE: https://quantra.quantinsti.com/course/introduction-to-data-science Timestamp: 00:22 - 00:35 - Data collection definition 00:36 - 01:09 - Primary data collection method 01:09 - 01:30 - Secondary data collection method 01:30 - 02:20 - Sample data Welcome to this video lesson on data collection. After completing this video, you will be able to explain what data collection is data collection methods and types of data You have defined the problem statement in the previous section. The next step is to collect the data which can help to solve the defined problem. Data collection is a systematic approach to gather relevant information from a variety of sources. Depending on the problem statement, the data collection method is broadly classified into two categories. When you have a unique problem and no related research is done on the subject. Then, you need to collect new data. This method is called as primary data collection. For example, you want information on the average time that employees spend in a cafeteria across companies. There is no public data available for these. But you can collect the data through various methods such as surveys, interviews of employees and by monitoring the time spent by employees in cafeteria. This method is time consuming. Another method is to use the data which is readily available or collected by someone else. This data can be found on open-source websites such as Kaggle, Gapminder, news articles, government census, magazines and so on. This method is called as secondary data collection. It is less time-consuming than the primary method. For our problem statement on EPL, we have collected and aggregated the data from various open-source websites such as Github, Kaggle, and datahub. A snapshot of the data collected is shown on the screen. As you can see, some attributes such as Age, Weights, and Heights have numeric values. These values can be analysed statistically. This type of data is known as quantitative data. Other attributes such as Name of the player and the Club provide more information about the player. Measurements and analysis for these are done differently, from those done with quantitative data like height and weight. This type of data is known as qualitative data. In any dataset, you can encounter both quantitative and qualitative data. You need to clean the dataset to make it suitable for further analysis. In the next video, you will learn to clean the datasets. Quantra is an online education portal that specializes in Algorithmic and Quantitative trading. Quantra offers various bite-sized, self-paced and interactive courses that are perfect for busy professionals, seeking implementable knowledge in this domain. Find more info on - https://quantra.quantinsti.com/ Like us on Facebook: https://www.facebook.com/goquantra/ Follow us on Twitter: https://twitter.com/GoQuantra