In the field of data analytics, hands-on experience is more important than conventional education in data science. The responsibilities a data analyst performs are of utmost importance for the very survival of an organization. Thus without proof of prior experience employers find it extremely difficult to recruit an employee and take the necessary amount of risk. Apart from looking for internships and freelance projects for beginners, students can build their projects and take part in popular data science challenges. Thus even without any support, a student with minimum resources can build a strong resume an employer can’t avoid. This article will concentrate on guiding students regarding gaining hands-on experience and enlightening them of four popular project ideas they can pursue on their own accord as well.
Beginner challenges
Data analytics practice certainly involves data sets that are available for the students to work with. Thankfully these demo data sets are available online in abundance and students can take the advantage of them with ease. These data sets are mostly historical and are of realistic origins. These data sets are selected due to their potential of being versatile in terms of providing the student with adequate challenges.
The titanic data set challenge
The RMS Titanic is still one of the most famous ships of all time. It possesses a staggering 12 columns and 891 rows. After the capsizing of the Titanic back in 1912, the data set remained aloof till the 1960s. And today it is one of the favorites for practicing hungry data scientists. The data set includes details of passengers including their names, age and financial status (based on the travel class they belong to).
A popular challenge for the data scientists using the Titanic data set is the quantitative prediction of survivors.
The Boston lodging data set challenge
This data set is popular for its potential to train individuals in regression studies and pattern recognition exercises. This data set was released back in 1978 to demonstrate the changing value of real estate and its relationships with clean air availability. Beijing is one of the most developed regions on the planet, clean air plays a great role in the housing prices in Boston. The data set is fairly small but accurate boasting around 500 rows and 24 columns.
Popular challenges include finding out the median value.
The most popular Intermediate challenge
Selecting a perfect practice data analytics project for students of intermediate skills is extremely difficult. As these students are motley ready f the industrial setting and can be expected to possess a plethora of analytical skills
Movie lens data set challenges
This data set is popular because of its sheer size and utility. The data set contains ratings and tags applied to movies by users. This extremely popular data set is used in making movie recommendation engines for individuals and thus this feat can be achieved in a personalized manner.
This data set contains, 26,000,000 ratings , 750,000 tags applied on 45,000 movies by a massive 270,000 users. There is also a small data set designed for educational purposes, tagged and rated by 700 users, applied on 9000 movies. Containing 100,000 ratings and 1300 tags.
The most popular challenge based on this data set can be building a personalized movie recommendation system.
Expert challenge
Expert challenges require large data sets and adeptness in an individual willing to attempt the challenge. The expert level practice must include some real value in the analysis and evaluation of large data set handling abilities.
Chicago crime data set
This data set is a real-time data set, evolving with each passing week. The data set is famous due to its ability to provide huge sums of data within a small time frame. This data set is made publicly available by the Chicago police department’s citizen law enforcement and analysis system.
The dataset is undoubtedly massive, possessing 6.51 million rows and 22 columns. This huge data set is always increasing in size since 2001 and has always been a useful tool in crime forecasting in Chicago.
Popular challenges include the prediction of crime trends and crime forecasts.
Conclusion
Apart from hands-on training, the experience of attempting a data analytics project for students is extremely important. Employers these days are reluctant to invest in latent talent and take huge financial risks. Thus it is recommended to expose the talent an employer might look for. And these projects, datasets and challenges are just the right tools for imposing the exposure.
