This is the biggest segment in new job market and many people are talking about it for obvious reasons. Economy is the main reason but let us focus from the perspective of job seekers.<\/p>\n
The point is we don\u2019t want to get confused when we refer to career related to data. Remember web master, is there such a profession now? Job titles in data-driven profession may end up like web master. Somebody might be doing something important about the web, but we no longer call them web masters. The difference in required skills and responsibilities between data scientist and data analyst should be highlighted<\/p>\n
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Since there is no governing body that regulates terms used in job titles and descriptions, it must be accepted that the advertised positions that are subsequently used in organizations evolve naturally.<\/p>\n
The name of courses offered by universities presumably are important origin of data-related job titles.<\/p>\n
For example, after a multi-disciplinary course of Data Science was created, employers could conveniently use the title Data Scientist that carries distinct and broader responsibilities compared to that of existing Data Analyst.<\/p>\n
Now, data scientists are expected to know what analysts know and do, but they are also required to come up with important questions that lead to new values from seemingly disparate datasets.<\/p>\n
The logic of hierarchy, seniority and salary scale, between analysts and scientists, is apparent enough from the scope of the functions alone.[\/vc_column_text][vc_column_text]\n
Now let us go through the details with respect to daily job responsibilities. Before that, it is helpful to see how similar terms have been used in the past and how slight modifications have brought totally new meanings. The term \u2018insight\u2019 has widely been used in data-related job activities recently.<\/p>\n
How do people produce insights in their daily activities and more importantly how do organizations benefit from these? Businesses have genuine need to manage their resources, know their cost and forecast future demands. Business growth is closely linked to these concerns.<\/p>\n
Before the age of Big Data, information that leads to planning was called Business Intelligence and logically business data and statistics were fully utilized along with external sources such market trends, geopolitical situation and consumer sentiments.<\/p>\n
There were no resources to produce accurate models back then. The term insight presumably borrowed from the fields of scientific research.<\/p>\n
In Science, the term insight often means description of plausible detailed processes that take place but cannot entirely be proven by scientific methods.<\/p>\n
The recent use of the term \u2018actionable insights\u2019 in data analysis suggests investigative or research nature of this job.[\/vc_column_text][vc_column_text]\n
Data Scientist<\/strong> – Data scientists deal with something that the organizations don\u2019t know that they don\u2019t know. Data scientists need to know the big picture, understand the values proposed by the organization and help position the organization in the future \u2013 and all with data. Once the correct questions have been identified, the role of data analyst starts.<\/p>\n Data Analyst<\/strong> – Analysts can utilize identified datasets and follow specific machine learning methodology in training models and optimizing the accuracy of the models. By doing this, analysts are providing answers to the questions that the organization know they don\u2019t know. Both data scientists and analysts are needed in data-driven organizations, working together as previously described.[\/vc_column_text][vc_column_text]\n Now, after you understand why you are needed in the organization, it is much clearer on what skills are required for you to perform your work, and more importantly how you support others and contribute to the growth of your organization.<\/p>\n If you are data scientists or analysts, both obviously need to use Mathematics and Statistics, both need to be experts in Python and R programming languages, and both need to use machine learning algorithms and functions. The above section clearly shows the bigger and more strategic role of data scientists.<\/p>\n You are responsible for the entire and disparate datasets. So, you must handle raw and structured data that are often enormous in size. Structured data are stored in relational databases such as SQL database, whereas, unstructured data sources include the internet, social media and news outlets.<\/p>\n Cluster computing of big data requires additional tools such as Hadoop or the in-memory speed enhanced Spark for data scientist.<\/p>\n Furthermore, as data scientists or data analysts, you may need to come up with new machine learning or model optimization functions.<\/p>\n You need to utilize additional tools and programming languages such as MATLAB, Scala, SAS and Minitab to support research and statistical needs in algorithm development.<\/p>\n On the other hand, data analysts who assist in data discovery, visualization and summary reporting most probably will need JavaScript and HTML while working with web browser developers.<\/p>\n If the term \u2018hacking\u2019 has not been fully accepted to describe a job of a legal profession, at least the technical capability of \u2018hackers\u2019 is required for both data scientists and analysts.<\/p>\n In data profession the term \u2018hacking\u2019 means getting codes and functions that works – relatively quickly and benefiting from support communities, rather than breaking into illegally and stealing.<\/p>\n As emphasized earlier, data scientists need to communicate their insights to stakeholders and convince strategic decisions that can greatly affect the organization to made.<\/p>\n They must articulate their findings in simple laymen terms that could directly translate to decisions and actions. Communication and leadership skills are generally linked to these job functions.[\/vc_column_text][vc_column_text]Career In Data Science or Data Analytics – The Takeaways<\/strong><\/p>\n Using data to create values is not a hype. The predicted jobs or career in data science or data ss to come in 2020 have arrived. The jobs are created, advertised and filled to serve the purposes described in the above for.<\/p>\n Though not always clear where data scientists and analysts start and end, it is possible and even important to define roles, source talents and transform existing ones with required skills.[\/vc_column_text][\/vc_column][\/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” scene_position=”center” text_color=”dark” text_align=”left” overlay_strength=”0.3″ shape_divider_position=”bottom” bg_image_animation=”none”][vc_column column_padding=”no-extra-padding” column_padding_position=”all” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_link_target=”_self” column_shadow=”none” column_border_radius=”none” width=”1\/1″ tablet_width_inherit=”default” tablet_text_alignment=”default” phone_text_alignment=”default” overlay_strength=”0.3″ column_border_width=”none” column_border_style=”solid” bg_image_animation=”none”][social_buttons nectar_love=”true” facebook=”true” twitter=”true”][\/vc_column][\/vc_row]\nCareer In Data Science – The Required Skill Sets<\/h3>\n