Data Research and economics

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Introduction

Data is the new gold. As key to the information revolution, the importance of data and the potential it unleashes has been well acknowledged and emphasized. Companies and institutions, realizing the importance of data, have been collecting and storing data even before they knew what to do with it. However, as the volume of data has tremendously increased in recent years, organisations have sought to extract insights from these data that can help identify and explain past and present trends, and predictions future outcomes. This need has given rise to two related and complementary fields: data science and business intelligence. 

Meaning of Business Intelligence 

Business intelligence (BI) is the performing of descriptive analysis of data to inform business decisions. It uses technologies and tools to collect, organise, and transform data with the aim of driving the actions of a business. BI technologies and practices are used to support business operations such as :

• Identifying trends in market behaviour and business performance. 
• Discovering ways to improve business processes and identify revenue generation opportunities. 
• Comparing performance with competitors to identify strengths and weaknesses of the business. 

Meaning of Data Science 

Data science is the process of using advanced mathematical and statistical techniques to extract valuable information from datasets and create forecast. It employs sophisticated statistical and computer science tools such as machine learning, artificial intelligence, and descriptive analytics to collect, model, summarize and analyse data. By analysing the data, the patterns behind the raw data can be discovered and used to predict future trends. 

Data science is applied in a broad range of industries ranging from the health sector to the automobile sector where they are applied in self-driving cars. It also offers a broad range of carrer opportunities [link to Fatimo’s post] across industries. In business, data science can be used to forecast sales, predict market trends, and discover consumer spending patterns. 

Differences between data science and business intelligence 

Though both data science and business intelligence turns data into valuable information, there are some subtle difference between them. These differences can be grouped into the following categories:

Objective:

 Data science uses predictive analysis of historical data to forecast future events and conditions such as business trends and consumer behaviour. It is more concerned with getting insights on the future outcomes of specific business issues. 

Business Intelligence, on the other hand, is more concerned with what is happening or have happened in the past. It uses descriptive analysis to present historical data in a way that is easy to visualise and understand. It is focused on generating reports that clearly communicates the present and past state of the business. 

Scope 

Data science aims at predicting specific events and finding out the validity of specific hypothesis. Hence the result of a particular predictive analysis is limited to the specific hypothesis in question.

On the other hand, business intelligence is general in scope. The results of descriptive analysis must be in a form that any business unit can use it to generate whatsoever report they need. Business intelligence can provide insight on a wide variety of issues and often serve as the foundation for more in-depth analysis.

Skills requirement:

Data scientists are required to have a variety of technical knowledge such as coding, data mining, machine learning, and other advanced statistical knowledge. They are also required to have a good degree of understanding of the particular domain of the problem they are handling. 

Business analyst require mainly basic statistical skills in addition to data transformation and visualisation skills. They however require a deeper and wider knowledge of the business to effectively serve each unit.

Data Collection and Integration. 

Data science applies the process of extract/load/transform (ELT) in handling data. In this process, the data is first extracted from one or multiple sources then loaded into the data warehouse. The data is then transformed (sorted and normalized) in the warehouse in line with the needs of the specific analysis being done at a particular time. Thus offers flexibility as the query can be tailored to meet the needs of the specific analysis. 

Business intelligence, on the other hand, employs the process of extract/transform/load (ETL). In this process, the data is transformed before it is loaded into the data warehouse. Thus the warehouse schema is clearly defined making it easy for users to carry out analysis and generate reports. 

Similarities between data science and business intelligence. 

Despite the differences between data science and business intelligence, there are still major similarities between them. The most glaring similarity between them is that they both use data to provide meaningful insight for an organisation. As such the quality of output they produce is largely dependent on the quality and accuracy of data they receive. 

Another important similarity between them is that they both require collaboration with other parts of the business to fully function. They require input from the operations of other business units and their output is determined by the needs of the business. Also data scientists and business analysts often collaborate to produce a more complete and useful insight on the organisation’s past, present, and future operations.

Interrelationship between data science and business intelligence 

Though either data science and business intelligence can provide meaningful insight for an organisation, combining both of them will provide the full insight of the past, present, and future state of the organisation. With business intelligence, the organisation can discover trends in its past outputs and profitability as well as in market and consumer behaviour. While with data science the organisation can predict the impact of various decisions and market conditions on the output and profitability of the business. Thus the insight from the two fields, when combined together, can be used to accurately guide business decisions to achieve a specific goal.

Getting started with data science and business intelligence 

It is clear at this point that both data science and business intelligence, either apart or in combination, are essential in providing data-driven insights that are needed to accurately guide the decision making process. Also, with the recent advances in such technologies as cloud computing, machine learning, and artificial intelligence, the ability of data science to transform business outcomes has greatly increased. Consequently, the demand for qualified data science and business intelligence professionals is steadily rising. 

The key to an impactful career in data science goes beyond simple knowledge of statistical or data manipulation procedures. Success in data science or business intelligence requires a deep understanding of how to glean out insights from a business and how such insights can be structured in a way that accurately guides decision making.

 At Tongston’s introduction to data science course. we provide the basic knowledge in statistics, data cleaning and manipulation, and programming needed to carry out basic data science and business intelligence processes. Also, our curriculum involves practice with real-world scenarios and entrepreneurial classes that offer first-hand insight on how data science can transform business outcomes. Enroll in the introduction to data science course today.

Dear Readers,

Tongston is a multi-award-winning group focused on entrepreneurial education, enterprise, finance and media leveraging on technology to turn people, governments and institutions into valuable, influential and profitable entities.

Check out our latest entry in our Entrepreneurial Blog sharing with you amazing insights on enterprise, education, media and finance globally, premised on entrepreneurial thinking. 

Introduction

Data scientists and data analysts are two of the most in-demand roles in the field of data analytics, and yet there is often confusion about the differences between these two roles. If you’re considering a career in data analytics, it’s important to understand the key differences between data scientists and data analysts, as well as the skills and knowledge required for each role.

What is a Data Scientist?

Data scientists are professionals who use scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. They are experts in data mining, machine learning, and statistical analysis, and use these skills to solve complex problems in a variety of industries, such as finance, healthcare, and retail.

Data scientists need a strong foundation in mathematics and statistics, as well as programming skills in languages such as Python and R. They should also be familiar with machine learning algorithms and techniques, as well as database management systems and SQL. In addition, data scientists should have excellent problem-solving and communication skills, as they will often be working on complex projects and will need to present their findings to a wide range of audiences.

What is a Data Analyst?

Data analysts are professionals who use data to inform business decisions. They collect, process, and analyze large amounts of data, and then present the results in a clear and concise manner to stakeholders. Data analysts work in a variety of industries, including finance, healthcare, and retail, and are responsible for generating reports, creating dashboards, and providing insights that help organizations make better decisions.

Data analysts need strong math and statistics skills, as well as programming skills in languages such as SQL and Excel. However, their focus is more on generating reports and dashboards, rather than developing complex models and algorithms. Data analysts should also be proficient in data visualization tools such as Tableau and Power BI, as they will be responsible for presenting data in a clear and concise manner to stakeholders. Data analysts work in a variety of industries, including finance, healthcare, and retail, and are responsible for generating reports, creating dashboards, and providing insights that help organizations make better decisions.

Data analysts need strong math and statistics skills, as well as programming skills in languages such as SQL and Excel. However, their focus is more on generating reports and dashboards, rather than developing complex models and algorithms. Data analysts should also be proficient in data visualization tools such as Tableau and Power BI, as they will be responsible for presenting data in a clear and visually appealing manner. In addition, data analysts should have strong communication and presentation skills, as they will be responsible for presenting their findings to stakeholders and decision-makers.

What are the main differences between Data Scientists and Data Analysts?

The main difference between data scientists and data analysts is the scope of their work. Data scientists are responsible for solving complex problems and developing models and algorithms to extract insights from data, while data analysts are responsible for generating reports and dashboards, and providing insights to inform business decisions. Also data scientists typically have a more technical skill set, with expertise in areas such as machine learning and statistical analysis, while data analysts have a more focused skill set, with expertise in areas such as data visualization and report generation.

Another key difference is the level of education and experience required for each role. Data scientists typically have advanced degrees in fields such as computer science, statistics, or mathematics, and may have several years of industry experience. Data analysts, on the other hand, may have a bachelor’s degree in a field such as business, economics, or computer science, and may have less industry experience.

In terms of career prospects, both data scientists and data analysts are in high demand, as organizations of all sizes are increasingly looking to leverage data to inform their decision-making. However, data scientists tend to have more opportunities, as they have a more specialized and technical skill set. Data analysts may have more limited career advancement opportunities, as they are more focused on generating reports and dashboards, rather than developing complex models and algorithms.

How do I choose between a career as a Data Scientist or a Data Analyst?

If you’re considering a career in data analytics, it’s important to carefully consider your strengths, interests, and career goals. If you enjoy solving complex problems and have a strong foundation in mathematics. Ultimately, the choice between a career as a data scientist or a data analyst will depend on your interests, skills, and career goals. If you enjoy solving complex problems and have a strong foundation in math and programming, a career as a data scientist might be a good fit for you. On the other hand, if you are more interested in using data to inform business decisions and have strong communication and visualization skills, a career as a data analyst might be a better fit.

It’s worth noting that many professionals in the field of data analytics start out as data analysts and then transition to data science roles as they gain more experience and education. This can be a good way to get your foot in the door and learn about the various tools and techniques used in the field, while also determining if a career as a data scientist is the right fit for you.

No matter which role you choose, it’s important to continuously learn and improve your skills in order to stay competitive in the fast-changing field of data analytics. This might involve taking online courses or earning additional certifications, such as the Data Science Certificate from the Institute for Statistics Education or the Certified Data Analyst Certificate from the Data Science Council of America.

In conclusion, data scientists and data analysts are two important roles in the field of data analytics, and while there are some similarities between the two, there are also significant differences. Data scientists are responsible for solving complex problems and developing models and algorithms to extract insights from data, while data analysts are responsible for generating reports and dashboards, and providing insights to inform business decisions. If you’re considering a career in data analytics, it’s important to carefully consider your interests, skills, and career goals, and choose the role that best aligns with these. Regardless of which role you choose, it’s important to continuously learn and improve your skills in order to stay competitive in this fast-changing field.