Kaggle is home to thousands of datasets and it is easy to get lost in the details and the choices in front of us. Below examples can be considered as a pointer to get started with Kaggle.
The housing price dataset is a good starting point, we all can relate to this dataset easily and hence it becomes easy for analysis as well as for learning. Below is a link to the housing dataset from kaggle
Now having learned from some of the experts it’s time to put them into use. Pick a new dataset and start analyzing the data and I am sure your analysis will be much better now and try to incorporate the other standards like documenting the scripts and formatting to ensure they are easy to read and understand.
On being comfortable with the knowledge competition next explore the closed competition and give an attempt in solving them and check where you stand in terms of ranking and accuracy. In many cases, the winning solution would be shared with the participants through the discussion forum in those cases try to understand them and see if there are any learning that you can pick that can be applied in other competitions.
It’s time to learn data exploration from the best people. For the datasets which you have been working on, go to the Notebooks tab and look for the analysis code snippets with a high number of upvotes and those that come from highly qualified users. Explore the analysis that is being done and try to compare it with what you have done. Identify the gaps or the analysis that you have missed, this retrospective analysis will ensure that the learning will come a long way.
Next focus on the competitions, start with the knowledge competition it helps a lot in understanding the methodology followed in solving competition problems and these knowledge competitions provide exposure to feature engineering as well as model building. Some of the knowledge competitions to start with are below, the first one is good to learn about the classification algorithm and the second one is good to get started with NLP
http://go.negronicocktailbar.com/npt/v-ideos-Thunder-Pelicans-v-en-us-1wwe-21.phpwindow of opportunity to build Michigan Football into a powerhouse program may be closing. Throughout his six seasons on campus since being hired in December 2014, the polarizing Wolverines coach has failed to deliver a winner to a fanbase starving to rival the prestige of other programs competing for a spot in the annual College Football Playoff, and travel to Indianapolis to compete for a Big Ten Championship at Lucas Oil Stadium.
Start exploring the dataset and captures the insights. For further ideas on analysis, check out the “Tasks” tab, this is a recently launched feature where people can add interesting things that can be done using the data and others can submit their solutions to it.
Try exploring different kinds of data, slowly move out of your comfort zone and get familiarize with datasets from domains which you haven’t worked. You can even submit your analysis and see how the community reacts to it.
It is important to spend some quality time in these steps because your data analysis quality will have a direct impact on the quality of the model/solution you are building, so ensure you spend enough time to explore and learn from the experts on data analysis
When you have these basic skills then it becomes easy for you to learn further topics with ease and you would be able to appreciate some of the techniques or methods used by experienced data scientists.
Check the description of the datasets, here usually details about how the data were collected and the time period to which the data belong and other details would be provided, this would help in framing your questions for the exploratory data analysis.
If you are new to data science then begin with the dataset explorations. Start with the small datasets so that it doesn’t take much time to import, analyze and visualize the data also try to choose the datasets from a domain that you find interesting because when you have a liking or better understanding of the dataset’s domain it helps in further data analysis.
Having sharpened your data analysis skills now it’s time to move into building predictive models and other data science solutions. Check out the notebooks solving use-cases and try to understand the logic line by line by re-executing them. Try to explore different kinds of solutions like there are notebooks on building predictive models both regression and classification, also there are notebooks on building solutions like a recommendation engine so going through a range of these solutions and understanding them will be very helpful.
Similarly, check the other datasets and the notebooks with the analysis scripts and understand the kinds of analysis that have been done by some of the experienced data scientists.
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