Data science is a concept to bring together ideas, data examination, Machine Learning, and their related strategies to comprehend and dissect genuine phenomena with data.
It is a huge field that uses a lot of methods and concepts which belong to other fields like information science, statistics, mathematics, and computer science.
Some of the techniques utilized in Data Science encompass machine learning, visualization, pattern recognition, probability modeling data, data engineering, signal processing, etc.
- Setting the research goal:?Understanding the business or activity our data science project is part of is key to ensuring its success and the first phase of any sound data analytics project.
- Retrieving data:?Finding and getting access to the data needed in our project is the next step.
- Data preparation:?The next data science step is the dreaded data preparation process that typically takes up to 80% of the time dedicated to our data project. Checking and remediating data errors, enriching the data with data from other data sources, and transforming it into a suitable format for your models.?
- Data exploration:?Now that we have cleaned our data, it’s time to manipulate it to get the most value out of it. Diving deeper into our data using descriptive statistics and visual techniques is how we explore our data.
- Presentation and automation:?Presenting our results to the stakeholders and industrializing our analysis process for repetitive reuse and integration with other tools.
- Data modeling:?Using machine learning and statistical techniques is the step to further achieve our project goal and predict future trends. By working with clustering algorithms, we can build models to uncover trends in the data that were not distinguishable in graphs and stats.
- Scientific Computing Libraries
- Visualization Libraries
- Algorithmic Libraries
