Start Your Journey with Data Science Today
The era of industrial revolution 4.0 revolves around the growth trend of automation, Internet of Things (IoT), Big Data, and Cloud Computing technology that need each other and have amount of data. This has resulted a field of expertise called Data Science which believed can meet the growing need to organize and process information from the large amount of data generated by human and devices.
The Roadmap on How to Become Data Scientist
Data science is increasingly being used in various industries to transform data into important information; thus, making data scientist profession as one of the most interesting jobs to be involved in digital era. To become a successful data scientist, one should understand its roadmap which can be broken down into: Programming Python, Database, Math & Statistic, Data Preparation, Machine Learning, Deep Learning, Data Visualization, and Pilot Project.
For starters, Python is known as the most common used programming language for data analytics. If you are aspiring to become a data scientist, then you must learn the basic hings of python programming include data structure, variable, function, looping, if condition, as well as its library such as pandas, numpy, scikt-learn, matplotlib, seaborn, and plotly. Next, a good data scientist shall understand that studying databases such as MySQL, Postgres, Oracle, MongoDB, etc are crucial because most organizations are using a relational database to store their data. To be underlined, learn the fundamental of Math such as linear algebra, calculus, optimization, and function, as well as Statistic such as descriptive, inferential, and probability are also crucial in order to become a data scientist.
Furthermore, the next step of the roadmap is data preparation in which you should learn how to make data ready to be modelled. You also have to understand about filling missing value, redundant data treatment, remove outliers, and many others. Next, to become a successful data scientist, you must learn about machine learning and understand the type of machine learning itself, which divided into supervised machine learning, unsupervised machine learning, and reinforcement machine learning.
Moreover, learn a more advance machine learning such as deep learning is also important to be a data scientist. You will learn more about neural network algorithm to help you cluster and classify data. After that, the next step is to learn data visualization which required for everyone who wants to be a data scientist. It is very crucial for organizations because it provides insights that could improve enhanced business analytics. Finally, learning by project will make you understand more about data science. You can start by having a data science pilot project such as create classification modelling, clustering modelling, time series analysis, sentiment analysis, image recognition, and many more.
Learn Data Science Using CRISP-DM Framework
In the exciting world of data science, CRISP-DM (CRoss-Industry Standard Process for Data Mining) framework can help you to have better understanding of data science as well as help a data scientist in handling projects. There are six implementation stages of CRISP-DM divided into Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation, and Deployment which are important to know.
First, business understanding is the stage where a data scientist determines business objectives and set the background of the problem. Next, in data understanding stage, a data scientist will define the data and collect initial data as well as produce data description & quality verification. Continue to the next stage, data preparation aims to clean data such as removing outliers, redundant data, and filling missing value. This stage is also used to reduce and compress data, transform and discretize data, and also integrate multiple databases or filesand. Next, the modelling stage uses machine learning for its modelling technique where the algorithms used can be those that are related to classification, clustering, time series, and others.
Furthermore, the last two stages are evaluation and deployment. Evaluation is the stage when the assessment is conducted to check whether the best tool for data mining is already used and also to verify whether the data is in good quality. In this stage, the accuracy assessment of the machine learning model should be conducted as well. Lastly, deployment stage aims to conduct implementation when a design is ready. However, as the transaction logs being produced continuously, a project of data mining does not last long and should always be updated.
Conclusion
Data science has provided many benefits to help businesses drive efficiencies and emerged a new role that is fast gaining prominence in organizations called data scientist. If you are aspiring to be a successful data scientist in the future, you have to understand the roadmap on how to become a data scientist and start learning data science by using CRISP-DM Framework; thus, you will have a better understanding of data science.
Reference:
da Rocha, B. C., & de Sousa Junior, R. T. (2010). Identifying bank frauds using CRISP-DM and decision trees. International Journal of Computer Science and Information Technology, 2(5), 162-169.
Van Der Aalst, W. (2016). Data science in action. In Process mining (pp. 3-23). Springer, Berlin, Heidelberg.
Walker, M. A. (2015). The professionalisation of data science. International Journal of Data Science, 1(1), 7-16.
