Data Science Process
- 1 Data Science Process
- 1.1 Key Takeaways:
- 1.2 Introduction
- 1.3 Steps in Data Science process
- Data Science is an ideal way to achieve business goals by extracting knowledge.
- Seven Steps discussed of Data Science process.
- Each Data Science process is interlinked with each other.
- Role of Data Structured discussed in Data Science Process
- Communication Skills is essential to deploy Data Science into business successfully.
Data science and analysis has grown to be one of the hottest topics among businesses and IT leaders. Well, the buzz and excitement around data science are all thanks to the potential benefits that are associated with the trend. After all, it’s all thanks to development in data science that businesses can now make informed decisions, use data better and improve the statistics of a business. Therefore, everyone seems to think that data science is all about applying well-crafted formulas to huge data sets in a bid to get useful insights. If you thought so then, you have an idea but you are not there yet!
Well, this is just the tip of the iceberg! In fact, the goal is not just to analyze data but also to deliver bespoke solutions to businesses through data. Companies tend to opt to create data products that suit their goals and requirements but there are steps that are uniform across the block. If you are one of those with little or no knowledge on the trend then takes some notes, as this is the article takes you through some of those steps that are critical to any data science project.
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Steps in Data Science process
1. Decide and set up objectives
How much data does my business generate? Do I need a professional to get involved and help in adding better value? Before any data decisions are made in the company, questions such as these have to be asked. It is a good idea to hire a data scientist to bring the professional touch of handling data to the company. The main purpose of collecting data is to know what value will be added to the business and how it will affect the business. Objectives will help in projecting not only monetary value that will be added but also it will also aid in beating off competition from similar businesses in the industry.
2. Data collection and preparation
With a creative and effective team ready, the vital data can now be collected. A data time line has to be set to guide the scientists on how much data they should collect and how long ago the data set should date back. Companies with a detailed data system like well-organized records will provide more value to the analyst and ease their work. Once the data has been collected, it is moved to data warehouses for cleaning and analysis.
3. Data cleaning
Junk data, spelling mistakes, missing data and much more are some of the things that are looked out for during the cleaning process. Overlooking the cleaning stage might compromise data or mislead the business in getting numbers that might be skewed when compared to the actual numbers. Due to the big amount of data collected, it is advisable for the process to automated using statistical and computing algorithms, to save costs and clean as much data as possible over a short time.
4. Data visualization
Once data is cleaned and approved it is then ready for modeling. First, the data is prepared for modeling depending on the data structure. This includes data sampling for big data, which is done to reduce data set sizes before they can be fit into to a single modeling machine. As expected, data scientists must have top-notch skill sets in the realm of statistics, computing and machine learning in order to build very accurate models that will give the best results as well as be able to determine the subsequent analytic steps to be taken.
5. Predictive Analytics
Just as the word suggests, this step is all about implementing probability and statistics on the collected data to predict any unknown data. The process is usually subdivided into two: classification, when the output is a category and regression, when the desired output is a number. The predictive model is usually trained using a known data set or in other words, using a data set where input data and output data are known. The training is iterative in the sense that the model is experimented with and the performance measured after every testing step. The model iteratively fine-tuned until the performance levels saturates and after that, the best model and parameters are selected for use in predictive analysis.
6. Optimization and repetition
Optimization is evaluating certain options in relation to the business objectives with an aim of making decisions. The target here is to optimize business objective functions using optimization techniques. To do this, the business objectives are mathematically modeled as an objective function while the set of variables in the formula optimized to find the best fit.
Repetition, on the other hand, is an important aspect in data science, keeping in mind that data keeps changing every now and then. Repeated data collection and analytic will help the business in getting the best results in a dynamic business environment. This will in turn help data scientists improve data collection, cleaning and modeling. In addition, the challenges in the project will be reduced while the predictions will be more accurate and the business will improve towards its set goals.
7. Growing a data science team and giving them access data
Qualified and competent data scientists are hard to find and those around are expensive to hire. However, as the business grows, it is important for the company to grow the pool of data science talent. It is also good to let them stick to what they know i. e to analyze data while other experts like programmers can help in modeling or designing data structures.
Data science is all reliant on data thus the vital of steps of data science should never be skipped or compromised. All these processes are interlinked with each other and it is necessary to have each core step for the process to be successful. Keep in mind that communication skills are also a fundamental to the success of any business deploying data science. Data scientists should be able to portray effectively their findings and suggestions to those with little or no scientific knowledge.
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