Data science is a very vast field that aims at extracting value from data. Data science includes mathematics, analytics, programming, and statistics etc. Being a data scientist is one of the trending career options nowadays. Data scientists perform data science by working properly on the data and organizing it. Data science is a multipurpose field in which the complex data is converted into simpler forms by studying it.
Nowadays each and every bigger organization is hiring data scientists to deal with their important data and this is increasing the value of data science day by day. In this field of data science, one should have accuracy and speed in handling the data. In this article I am going to discuss five data science projects that will help you out in getting a good job:
• Data cleaning: After getting the data collecting, the next step is data cleaning. Databases could be corrupted with different errors like missing or inconsistent values. That’s why there is a need for data cleaning.
One who is expert in cleaning the data and have experience can immediately get the job as it is an important part of data science. To pick a tool is necessary if you have your data. For example, while using pythons, the panda library must be checked. Cleaning of data is also important to discard the unnecessary information. Also, don’t forget to examine these skills-
• import the data,
• join the multiple datasets,
• detect missing values,
• detect anomalies,
• Assurance of data quality,
• Imputing for missing values.
• Exploratory data analysis: One of the important aspects of data science is EDA which is exploratory data analysis. It is a process of generating questions, investing them and then visualization. The approach of exploratory data analysis was promoted by John turkey to encourage statisticians so as to explore the data. EDA is the best way of starting a data science project. IBM analytic community is a great source EDA datasets.
Following topics should be kept in mind while building an EDA project:
• Formulate relevant questions and hypothesis and test these questions with visualizations.
• Identify trends in data and look for a relationship between variables.
• Communicate results with visualizations.
• Data visualizations: Interactive data visualization includes tools such as dashboards. Dashboards are made to meet the need of a company and department. These tools are important for data science team and more business-oriented end users.
The dashboard project should keep a check on following:
• Including relevant metrics and creating useful features
• A logical layout, create an optimum refresh rate, generating reports.
• Communication: The data science type should focus on communication also. For good buy-in, you should know how to communicate with the teammates. You should know your audience, don’t overdo the slides, the presentation should flow well, present relevant visualizations.
• Machine learning: Machine learning project is also an important aspect of data science portfolio. Machine learning focuses on the development of computers programs and uses it for them. One should stick to the basics rather than building a complex machine learning models. Machine learning includes – classification, regression, clustering, model selection, preprocessing.
