Data Science Life cycle revolves around using machine learning and other analytical methods to produce insights and predictions from data to achieve a business objective. The entire process involves several steps like data cleaning, preparation, modelling, model evaluation and many more steps.
All data science project has 'Data' as a core element like gemstone , without data no science can be applied and hence nothing can be achieved.
Now have a look on Data science life cycle flow chart
So let's walk through the structure step by step
1. Business Understanding
Business Understanding plays a key role in success of any project. We have all the technology to make our lives easy but still with this tremendous change a success of any project depends on the quality of questions asked for the dataset. If you understand the business problem then definitely you will reach your goal without any failure.
Every business domain has its own set of rules we have to understand it and also the data and if any query is there we should ask so we will not have lack of information.
2. Data Mining
The process of digging through data to discover hidden connections and predict future trends has a long history. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use.
Data mining is used to increase revenues in the business , cut costs, improve customer relationships, reduce risks and many more.
3. Data Cleaning
This step is also known as Data Wrangling. Data cleansing is also important because it improves your data quality and in doing so, increases overall productivity. Data cleaning means to remove incorrectly formatted, duplicate, or incomplete data within a dataset and also how to handle the missing values within the dataset.
4. Data Exploration
Data exploration is the most human-centric step of the Data Science process: as such, it is the simplest to understand, but also the simplest to misunderstand. Data exploration is the first step of data analysis used to explore and visualize data to uncover insights from the start or identify areas or patterns to dig into more. Using interactive dashboards and point-and-click data exploration, users can better understand the bigger picture and get to insights faster.
5. Feature Engineering
Feature engineering is useful to improve the performance of machine learning algorithms and Selecting the important features and reducing the size of the feature set makes computation in machine learning and data analytic algorithms more feasible. Feature engineering refers to a process of selecting and transforming variables when creating a predictive model using machine learning or statistical modeling (such as deep learning, decision trees, or regression). The process involves a combination of data analysis, applying rules of thumb, and judgement.
6. Predictive Modeling
Data modeling is considered as the heart of data analysis. A model takes the prepared data from the previous step (Data Preparation) as input and provides the desired output. This step includes choosing the appropriate type of model, whether the problem is a classification problem, or a regression problem or a clustering problem. After choosing the model , amongst the various algorithms present. We need to tune the hyper parameters of each model to achieve the desired performance.
In the end we need to evaluate the model by measuring the accuracy (How well the model performs i.e. does it describe the data accurately) and relevance (Does it answer the original question that is set out to answer). We also need to make sure there is a correct balance between performance and generalizability, which means the model created should not be biased and should be a generalized model. In short picking the best model is predictive modeling.
7. Data visualization
Last but not the least, visualization of findings should be done. It should be in line with business questions. It should be meaningful to the organisation and the stakeholders. Presentation through visualization should be such that it should trigger action in the audience.
All the above steps make a complete Data Science project but it is an iterative process and various steps are repeated until we are able to fine tune the methodology for a specific business case. Python and R are the most widely used languages for Data Science.
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