• Introduction • Introduce Data Science as a field • Identify the disciplines under Data Science • Explain Data Science and Data Analysis • Identify their differences and similarities • Helpful resources for both disciplines. Introduction. Humans have always shared data from the very beginning of time but in recent times, we’ve suddenly come to an awakening of the power of data and how we can use them to make better decisions. This interest has birthed a lot of roles and as usual, there seems to be an overlap in function and application. This article is specifically written for anyone, irrespective of their previous experience who wants to venture into the field of Data and has a hard time deciding on which to choose. No previous knowledge is required. What is Data Science? Data science (DS) is an interdisciplinary field that combines skills from the fields of statistics, mathematics, visualization and other methods in order to obtain and analyze data in order to make good business decisions. In simpler terms, DS is a field that extracts valuable business information from obtained data. This field has gotten a lot of recognition lately as a result of the vast amount of data available and the need for businesses to properly: • identify customers, • make good business decisions, • compete in the market place, • target their customers, etc. DS disciplines There are a lot of disciplines under DS which can suit anyone interested, based on personal interests and strengths. Some of those fields include: • Data Engineering, • Data Analysis • Data Preparation, • Data Mining, • Data Visualization, • Machine Learning, • Business Intelligence etc. For the purpose of this article, I’ll try to shed light on what it means to be and the difference between being a Data Analyst and a Data Scientist.

Who’s a Data Analyst? A Data Analyst is someone who extracts insights, identifies trends, develop charts and dashboards. The job of a Data analyst is to scrutinize data in order to understand it. Take for example, a company announces a discount, the job of the data analyst will be to compare and understand the impact of the discount on company sales. A Data Analyst is expected to have technical expertise, be able to properly communicate their findings, preferably with dashboards and visualizations. The following skills are needed for a Data Analyst role: • Data mining/warehousing/modeling, • R/SAS, • SQL, • Statistics, • Database management/reporting, • Data Visualization (Power BI/Tableau) • Python. Who’s a Data Scientist? A Data Scientist is someone who looks at data, interprets it and uses it to make predictions. As with the afore mentioned example, if a company announces a discount, the job of the data scientist will be to predict the impact of the discount and who to target. A data scientist is expected to have a lot of expertise hence, a lot of data scientists have advanced degrees. The following skills are needed for a Data Scientist role: • Hadoop, • Java, • Python, • Object-oriented programming, • Machine learning, • Mathematics, • Everything a Data Analyst does.

Difference between Data Analysis and Data Science. The major difference between a Data Analyst role and a Data Science role is that Data Scientists do a lot of heavy coding than Data analysts. A more subtle difference is what they do with the data they obtain; a Data Analyst looks at data to understand it and gain insight while a Data Scientist looks at data in order to make predictions. Furthermore, as their roles suggest, a Data Analyst and Data Scientist will typically operate in different sectors. Data Analysts will mostly work in industries like health, retail, marketing while Data Scientists will be more suited to Artificial Intelligence (AI), blockchain industries because of the skillset involved.

Helpful links for Data Science youtube.com/watch?v=pzo13OPXZS4 youtube.com/watch?v=N6BghzuFLIg simplilearn.com/tutorials/data-science-tuto.. coursera.org/specializations/introduction-d..

Helpful links for Data Analysis edureka.co/blog/what-is-data-analytics/amp youtube.com/watch?v=fWE93St-RaQ coursera.org/learn/introduction-to-data-ana...